ORCID Profile
0000-0001-9916-6382
Current Organisation
UNSW
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Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2021
Publisher: IEEE
Date: 07-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 06-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2017
Publisher: IEEE
Date: 07-2012
Publisher: Elsevier BV
Date: 15-05-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 06-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2018
Publisher: IEEE
Date: 07-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2017
Publisher: IEEE
Date: 07-2011
Publisher: IEEE
Date: 2004
Publisher: IEEE
Date: 10-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2014
Publisher: MDPI AG
Date: 30-08-2017
DOI: 10.3390/RS9090899
Publisher: IEEE
Date: 07-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2003
Publisher: IEEE
Date: 07-2010
Publisher: Elsevier BV
Date: 08-2017
Publisher: Informa UK Limited
Date: 10-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2015
Publisher: Elsevier BV
Date: 03-1998
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2018
Publisher: Informa UK Limited
Date: 22-08-2018
Publisher: Informa UK Limited
Date: 10-08-2006
Publisher: Springer International Publishing
Date: 2017
Publisher: IEEE
Date: 07-2016
Publisher: Informa UK Limited
Date: 13-11-2018
Publisher: SPIE
Date: 03-04-2008
DOI: 10.1117/12.777445
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2016
Publisher: IEEE
Date: 12-2017
Publisher: Elsevier BV
Date: 09-2011
Publisher: IEEE
Date: 09-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2017
Publisher: Informa UK Limited
Date: 12-2006
Publisher: Informa UK Limited
Date: 14-03-2014
Publisher: IEEE
Date: 07-2017
Publisher: IEEE
Date: 07-2016
Publisher: IEEE
Date: 06-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Informa UK Limited
Date: 03-11-2011
Publisher: IEEE
Date: 10-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 10-2011
Publisher: MDPI AG
Date: 22-01-2019
DOI: 10.3390/RS11030223
Abstract: Capsule networks can be considered to be the next era of deep learning and have recently shown their advantages in supervised classification. Instead of using scalar values to represent features, the capsule networks use vectors to represent features, which enriches the feature presentation capability. This paper introduces a deep capsule network for hyperspectral image (HSI) classification to improve the performance of the conventional convolutional neural networks (CNNs). Furthermore, a modification of the capsule network named Conv-Capsule is proposed. Instead of using full connections, local connections and shared transform matrices, which are the core ideas of CNNs, are used in the Conv-Capsule network architecture. In Conv-Capsule, the number of trainable parameters is reduced compared to the original capsule, which potentially mitigates the overfitting issue when the number of available training s les is limited. Specifically, we propose two schemes: (1) A 1D deep capsule network is designed for spectral classification, as a combination of principal component analysis, CNN, and the Conv-Capsule network, and (2) a 3D deep capsule network is designed for spectral-spatial classification, as a combination of extended multi-attribute profiles, CNN, and the Conv-Capsule network. The proposed classifiers are tested on three widely-used hyperspectral data sets. The obtained results reveal that the proposed models provide competitive results compared to the state-of-the-art methods, including kernel support vector machines, CNNs, and recurrent neural network.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2010
Publisher: SPIE
Date: 10-06-2007
DOI: 10.1117/12.761314
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2002
Publisher: IEEE
Date: 07-2015
Publisher: IEEE
Date: 07-2016
Publisher: IEEE
Date: 06-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: MDPI AG
Date: 12-03-2017
DOI: 10.3390/RS9030261
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 07-2016
Publisher: MDPI AG
Date: 15-06-2022
DOI: 10.3390/RS14122859
Abstract: Anomaly detection of hyperspectral imagery (HSI) identifies the very few s les that do not conform to an intricate background without priors. Despite the extensive success of hyperspectral interpretation techniques based on generative adversarial networks (GANs), applying trained GAN models to hyperspectral anomaly detection remains promising but challenging. Previous generative models can accurately learn the complex background distribution of HSI and typically convert the high-dimensional data back to the latent space to extract features to detect anomalies. However, both background modeling and feature-extraction methods can be improved to become ideal in terms of the modeling power and reconstruction consistency capability. In this work, we present a multi-prior-based network (MPN) to incorporate the well-trained GANs as effective priors to a general anomaly-detection task. In particular, we introduce multi-scale covariance maps (MCMs) of precise second-order statistics to construct multi-scale priors. The MCM strategy implicitly bridges the spectral- and spatial-specific information and fully represents multi-scale, enhanced information. Thus, we reliably and adaptively estimate the HSI label to alleviate the problem of insufficient priors. Moreover, the twin least-square loss is imposed to improve the generative ability and training stability in feature and image domains, as well as to overcome the gradient vanishing problem. Last but not least, the network, enforced with a new anomaly rejection loss, establishes a pure and discriminative background estimation.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Inderscience Publishers
Date: 2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2016
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2018
Publisher: Springer Science and Business Media LLC
Date: 13-02-2018
Publisher: IEEE
Date: 10-2010
Publisher: IEEE
Date: 12-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2019
Publisher: IEEE
Date: 2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-1994
DOI: 10.1109/36.295042
Publisher: American Society for Photogrammetry and Remote Sensing
Date: 04-2007
Publisher: Oxford University Press (OUP)
Date: 24-03-2021
DOI: 10.1093/BJS/ZNAB101
Abstract: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18–49, 50–69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351 best case 196, worst case 816) or non-cancer surgery (733 best case 407, worst case 1664). Both exceeded the NNV in the general population (1840 best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population.
Publisher: Wiley
Date: 09-08-2021
DOI: 10.1111/ANAE.15560
Abstract: We aimed to determine the impact of pre‐operative isolation on postoperative pulmonary complications after elective surgery during the global SARS‐CoV‐2 pandemic. We performed an international prospective cohort study including patients undergoing elective surgery in October 2020. Isolation was defined as the period before surgery during which patients did not leave their house or receive visitors from outside their household. The primary outcome was postoperative pulmonary complications, adjusted in multivariable models for measured confounders. Pre‐defined sub‐group analyses were performed for the primary outcome. A total of 96,454 patients from 114 countries were included and overall, 26,948 (27.9%) patients isolated before surgery. Postoperative pulmonary complications were recorded in 1947 (2.0%) patients of which 227 (11.7%) were associated with SARS‐CoV‐2 infection. Patients who isolated pre‐operatively were older, had more respiratory comorbidities and were more commonly from areas of high SARS‐CoV‐2 incidence and high‐income countries. Although the overall rates of postoperative pulmonary complications were similar in those that isolated and those that did not (2.1% vs 2.0%, respectively), isolation was associated with higher rates of postoperative pulmonary complications after adjustment (adjusted OR 1.20, 95%CI 1.05–1.36, p = 0.005). Sensitivity analyses revealed no further differences when patients were categorised by: pre‐operative testing use of COVID‐19‐free pathways or community SARS‐CoV‐2 prevalence. The rate of postoperative pulmonary complications increased with periods of isolation longer than 3 days, with an OR (95%CI) at 4–7 days or ≥ 8 days of 1.25 (1.04–1.48), p = 0.015 and 1.31 (1.11–1.55), p = 0.001, respectively. Isolation before elective surgery might be associated with a small but clinically important increased risk of postoperative pulmonary complications. Longer periods of isolation showed no reduction in the risk of postoperative pulmonary complications. These findings have significant implications for global provision of elective surgical care.
Publisher: Elsevier BV
Date: 1993
Publisher: MDPI AG
Date: 20-04-2019
DOI: 10.3390/RS11080952
Abstract: Coastal wetland mapping plays an essential role in monitoring climate change, the hydrological cycle, and water resources. In this study, a novel classification framework based on the gravitational optimized multilayer perceptron classifier and extended multi-attribute profiles (EMAPs) is presented for coastal wetland mapping using Sentinel-2 multispectral instrument (MSI) imagery. In the proposed method, the morphological attribute profiles (APs) are firstly extracted using four attribute filters based on the characteristics of wetlands in each band from Sentinel-2 imagery. These APs form a set of EMAPs which comprehensively represent the irregular wetland objects in multiscale and multilevel. The EMAPs and original spectral features are then classified with a new multilayer perceptron (MLP) classifier whose parameters are optimized by a stability-constrained adaptive alpha for a gravitational search algorithm. The performance of the proposed method was investigated using Sentinel-2 MSI images of two coastal wetlands, i.e., the Jiaozhou Bay and the Yellow River Delta in Shandong province of eastern China. Comparisons with four other classifiers through visual inspection and quantitative evaluation verified the superiority of the proposed method. Furthermore, the effectiveness of different APs in EMAPs were also validated. By combining the developed EMAPs features and novel MLP classifier, complicated wetland types with high within-class variability and low between-class disparity were effectively discriminated. The superior performance of the proposed framework makes it available and preferable for the mapping of complicated coastal wetlands using Sentinel-2 data and other similar optical imagery.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2016
Publisher: MDPI AG
Date: 06-2017
DOI: 10.3390/RS9060548
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2019
Publisher: IEEE
Date: 09-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2020
Publisher: Elsevier BV
Date: 07-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2014
Publisher: IEEE
Date: 07-2012
Publisher: MDPI AG
Date: 08-06-2018
DOI: 10.3390/RS10060905
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2019
Publisher: IEEE
Date: 08-2010
Publisher: Informa UK Limited
Date: 26-03-2010
Publisher: Wiley
Date: 09-03-2021
DOI: 10.1111/ANAE.15458
Abstract: Peri‐operative SARS‐CoV‐2 infection increases postoperative mortality. The aim of this study was to determine the optimal duration of planned delay before surgery in patients who have had SARS‐CoV‐2 infection. This international, multicentre, prospective cohort study included patients undergoing elective or emergency surgery during October 2020. Surgical patients with pre‐operative SARS‐CoV‐2 infection were compared with those without previous SARS‐CoV‐2 infection. The primary outcome measure was 30‐day postoperative mortality. Logistic regression models were used to calculate adjusted 30‐day mortality rates stratified by time from diagnosis of SARS‐CoV‐2 infection to surgery. Among 140,231 patients (116 countries), 3127 patients (2.2%) had a pre‐operative SARS‐CoV‐2 diagnosis. Adjusted 30‐day mortality in patients without SARS‐CoV‐2 infection was 1.5% (95%CI 1.4–1.5). In patients with a pre‐operative SARS‐CoV‐2 diagnosis, mortality was increased in patients having surgery within 0–2 weeks, 3–4 weeks and 5–6 weeks of the diagnosis (odds ratio (95%CI) 4.1 (3.3–4.8), 3.9 (2.6–5.1) and 3.6 (2.0–5.2), respectively). Surgery performed ≥ 7 weeks after SARS‐CoV‐2 diagnosis was associated with a similar mortality risk to baseline (odds ratio (95%CI) 1.5 (0.9–2.1)). After a ≥ 7 week delay in undertaking surgery following SARS‐CoV‐2 infection, patients with ongoing symptoms had a higher mortality than patients whose symptoms had resolved or who had been asymptomatic (6.0% (95%CI 3.2–8.7) vs. 2.4% (95%CI 1.4–3.4) vs. 1.3% (95%CI 0.6–2.0), respectively). Where possible, surgery should be delayed for at least 7 weeks following SARS‐CoV‐2 infection. Patients with ongoing symptoms ≥ 7 weeks from diagnosis may benefit from further delay.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2021
Publisher: IEEE
Date: 07-2012
Publisher: IEEE
Date: 06-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: MDPI AG
Date: 17-07-2019
DOI: 10.3390/RS11141692
Abstract: Automatic weed detection and classification faces the challenges of large intraclass variation and high spectral similarity to other vegetation. With the availability of new high-resolution remote sensing data from various platforms and sensors, it is possible to capture both spectral and spatial characteristics of weed species at multiple scales. Effective multi-resolution feature learning is then desirable to extract distinctive intensity, texture and shape features of each category of weed to enhance the weed separability. We propose a feature extraction method using a Convolutional Neural Network (CNN) and superpixel based Local Binary Pattern (LBP). Both middle and high level spatial features are learned using the CNN. Local texture features from superpixel-based LBP are extracted, and are also used as input to Support Vector Machines (SVM) for weed classification. Experimental results on the hyperspectral and remote sensing datasets verify the effectiveness of the proposed method, and show that it outperforms several feature extraction approaches.
Publisher: IEEE
Date: 10-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2012
Publisher: MDPI AG
Date: 12-12-2021
DOI: 10.3390/RS13245043
Abstract: Current mainstream networks for hyperspectral image (HSI) classification employ image patches as inputs for feature extraction. Spatial information extraction is limited by the size of inputs, which makes networks unable to perform effective learning and reasoning from the global perspective. As a common component for capturing long-range dependencies, non-local networks with pixel-by-pixel information interaction bring unaffordable computational costs and information redundancy. To address the above issues, we propose a class feature fused fully convolutional network (CFF-FCN) with a local feature extraction block (LFEB) and a class feature fusion block (CFFB) to jointly utilize local and global information. LFEB based on dilated convolutions and reverse loop mechanism can acquire the local spectral–spatial features at multiple levels and deliver shallower layer features for coarse classification. CFFB calculates global class representation to enhance pixel features. Robust global information is propagated to every pixel with low computational cost. CFF-FCN considers a fully global class context and obtains more discriminative representation by concatenating high-level local features and re-integrated global features. Experimental results conducted on three real HSI data sets demonstrate that the proposed fully convolutional network is superior to multiple state-of-the-art deep learning-based approaches, especially in the case of a small number of training s les.
Publisher: IEEE
Date: 07-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: MDPI AG
Date: 12-10-2019
DOI: 10.3390/RS11202372
Abstract: Current new developments in remote sensing imagery enable satellites to capture videos from space. These satellite videos record the motion of vehicles over a vast territory, offering significant advantages in traffic monitoring systems over ground-based systems. However, detecting vehicles in satellite videos are challenged by the low spatial resolution and the low contrast in each video frame. The vehicles in these videos are small, and most of them are blurred into their background regions. While region proposals are often generated for efficient target detection, they have limited performance on satellite videos. To meet this challenge, we propose a Local Region Proposing approach (LRP) with three steps in this study. A video frame is segmented into semantic regions first and possible targets are then detected in these coarse scale regions. A discrete Histogram Mixture Model (HistMM) is proposed in the third step to narrow down the region proposals by quantifying their likelihoods towards the target category, where the training is conducted on positive s les only. Experiment results demonstrate that LRP generates region proposals with improved target recall rates. When a slim Fast-RCNN detector is applied, LRP achieves better detection performance over the state-of-the-art approaches tested.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2019
Publisher: MDPI AG
Date: 07-01-2020
DOI: 10.3390/RS12020205
Abstract: Information extraction from multi-sensor remote sensing images has increasingly attracted attention with the development of remote sensing sensors. In this study, a supervised change detection method, based on the deep Siamese convolutional network with hybrid convolutional feature extraction module (OB-DSCNH), has been proposed using multi-sensor images. The proposed architecture, which is based on dilated convolution, can extract the deep change features effectively, and the character of “network in network” increases the depth and width of the network while keeping the computational budget constant. The change decision model is utilized to detect changes through the difference of extracted features. Finally, a change detection map is obtained via an uncertainty analysis, which combines the multi-resolution segmentation, with the output from the Siamese network. To validate the effectiveness of the proposed approach, we conducted experiments on multispectral images collected by the ZY-3 and GF-2 satellites. Experimental results demonstrate that our proposed method achieves comparable and better performance than mainstream methods in multi-sensor images change detection.
Publisher: IEEE
Date: 12-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2019
Publisher: IEEE
Date: 12-2007
Publisher: IEEE
Date: 07-2015
Publisher: IEEE
Date: 2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2020
Publisher: Elsevier BV
Date: 08-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: MDPI AG
Date: 28-04-2022
DOI: 10.3390/RS14092125
Abstract: Joint sparse representation classification (JSRC) is a representative spectral–spatial classifier for hyperspectral images (HSIs). However, the JSRC is inappropriate for highly heterogeneous areas due to the spatial information being extracted from a fixed-sized neighborhood block, which is often unable to conform to the naturally irregular structure of land cover. To address this problem, a superpixel-based JSRC with nonlocal weighting, i.e., superpixel-based nonlocal weighted JSRC (SNLW-JSRC), is proposed in this paper. In SNLW-JSRC, the superpixel representation of an HSI is first constructed based on an entropy rate segmentation method. This strategy forms homogeneous neighborhoods with naturally irregular structures and alleviates the inclusion of pixels from different classes in the process of spatial information extraction. Afterwards, the superpixel-based nonlocal weighting (SNLW) scheme is built to weigh the superpixel based on its structural and spectral information. In this way, the weight of one specific neighboring pixel is determined by the local structural similarity between the neighboring pixel and the central test pixel. Then, the obtained local weights are used to generate the weighted mean data for each superpixel. Finally, JSRC is used to produce the superpixel-level classification. This speeds up the sparse representation and makes the spatial content more centralized and compact. To verify the proposed SNLW-JSRC method, we conducted experiments on four benchmark hyperspectral datasets, namely Indian Pines, Pavia University, Salinas, and DFC2013. The experimental results suggest that the SNLW-JSRC can achieve better classification results than the other four SRC-based algorithms and the classical support vector machine algorithm. Moreover, the SNLW-JSRC can also outperform the other SRC-based algorithms, even with a small number of training s les.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: SPIE
Date: 02-10-2008
DOI: 10.1117/12.800160
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Springer Netherlands
Date: 2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2005
Publisher: IEEE
Date: 2009
Publisher: IEEE
Date: 2009
Publisher: Springer Science and Business Media LLC
Date: 05-2016
Publisher: Informa UK Limited
Date: 21-09-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2016
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2012
Publisher: Elsevier BV
Date: 11-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 2018
Publisher: IEEE
Date: 07-2017
Publisher: IEEE
Date: 2008
Publisher: MDPI AG
Date: 07-02-2019
DOI: 10.3390/S19030684
Abstract: While ship detection using high-resolution optical satellite images plays an important role in various civilian fields—including maritime traffic survey and maritime rescue—it is a difficult task due to influences of the complex background, especially when ships are near to land. In current literatures, land masking is generally required before ship detection to avoid many false alarms on land. However, sea–land segmentation not only has the risk of segmentation errors, but also requires expertise to adjust parameters. In this study, Faster Region-based Convolutional Neural Network (Faster R-CNN) is applied to detect ships without the need for land masking. We propose an effective training strategy for the Faster R-CNN by incorporating a large number of images containing only terrestrial regions as negative s les without any manual marking, which is different from the selection of negative s les by targeted way in other detection methods. The experiments using Gaofen-1 satellite (GF-1), Gaofen-2 satellite (GF-2), and Jilin-1 satellite (JL-1) images as testing datasets under different ship detection conditions were carried out to evaluate the effectiveness of the proposed strategy in the avoidance of false alarms on land. The results show that the method incorporating negative s le training can largely reduce false alarms in terrestrial areas, and is superior in detection performance, algorithm complexity, and time consumption. Compared with the method based on sea–land segmentation, the proposed method achieves the absolute increment of 70% of the F1-measure, when the image contains large land area such as the GF-1 image, and achieves the absolute increment of 42.5% for images with complex harbors and many coastal ships, such as the JL-1 images.
Publisher: IEEE
Date: 07-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2021
Publisher: IEEE
Date: 06-2010
Publisher: IEEE
Date: 07-2014
Publisher: SPIE
Date: 28-08-2008
DOI: 10.1117/12.794064
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2009
Publisher: Copernicus GmbH
Date: 20-10-2017
DOI: 10.5194/ISPRS-ANNALS-IV-4-W2-161-2017
Abstract: Abstract. Most traditional methods for rice mapping with remote sensing data are effective when they are applied to the initial growing stage of rice, as the practice of flooding during this period makes the spectral characteristics of rice fields more distinguishable. In this study, we propose a sequential classifier training approach for rice mapping that can be used over the whole growing period of rice for monitoring various growth stages. Rice fields are firstly identified during the initial flooding period. The identified rice fields are used as training data to train a classifier that separates rice and non-rice pixels. The classifier is then used as a priori knowledge to assist the training of classifiers for later rice growing stages. This approach can be applied progressively to sequential image data, with only a small amount of training s les being required from each image. In order to demonstrate the effectiveness of the proposed approach, experiments were conducted at one of the major rice-growing areas in Australia. The proposed approach was applied to a set of multitemporal remote sensing images acquired by the Sentinel-2A satellite. Experimental results show that, compared with traditional spectral-indexbased algorithms, the proposed method is able to achieve more stable and consistent rice mapping accuracies and it reaches higher than 80% during the whole rice growing period.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2016
Publisher: Elsevier BV
Date: 04-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2017
Publisher: IEEE
Date: 08-2016
Publisher: Informa UK Limited
Date: 21-03-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2011
Publisher: Springer Berlin Heidelberg
Date: 2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2018
Publisher: IEEE
Date: 2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2013
Publisher: IEEE
Date: 10-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2020
Publisher: American Society for Pharmacology & Experimental Therapeutics (ASPET)
Date: 14-09-2016
Abstract: Current antipsychotics are effective in treating the positive symptoms associated with schizophrenia, but they remain suboptimal in targeting cognitive dysfunction. Recent studies have suggested that positive allosteric modulation of the M
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 07-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: MDPI AG
Date: 15-02-2018
DOI: 10.3390/RS10020299
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2016
Publisher: IEEE
Date: 07-2013
Publisher: MDPI AG
Date: 28-06-2022
DOI: 10.3390/RS14133100
Abstract: Very-high-resolution (VHR) bi-temporal images change detection (CD) is a basic remote sensing images (RSIs) processing task. Recently, deep convolutional neural networks (DCNNs) have shown great feature representation abilities in computer vision tasks and have achieved remarkable breakthroughs in automatic CD. However, a great majority of the existing fusion-based CD methods pay no attention to the definition of CD, so they can only detect one-way changes. Therefore, we propose a new temporal reliable change detection (TRCD) algorithm to solve this drawback of fusion-based methods. Specifically, a potential and effective algorithm is proposed for learning temporal-reliable features for CD, which is achieved by designing a novel objective function. Unlike the traditional CD objective function, we impose a regular term in the objective function, which aims to enforce the extracted features before and after exchanging sequences of bi-temporal images that are similar to each other. In addition, our backbone architecture is designed based on a high-resolution network. The captured features are semantically richer and more spatially precise, which can improve the performance for small region changes. Comprehensive experimental results on two public datasets demonstrate that the proposed method is more advanced than other state-of-the-art (SOTA) methods, and our proposed objective function shows great potential.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2016
Publisher: Elsevier BV
Date: 10-2018
Publisher: MDPI AG
Date: 18-11-2019
DOI: 10.3390/RS11222690
Abstract: Hyperspectral remote sensing obtains abundant spectral and spatial information of the observed object simultaneously. It is an opportunity to classify hyperspectral imagery (HSI) with a fine-grained manner. In this study, the fine-grained classification of HSI, which contains a large number of classes, is investigated. On one hand, traditional classification methods cannot handle fine-grained classification of HSI well on the other hand, deep learning methods have shown their powerfulness in fine-grained classification. So, in this paper, deep learning is explored for HSI supervised and semi-supervised fine-grained classification. For supervised HSI fine-grained classification, densely connected convolutional neural network (DenseNet) is explored for accurate classification. Moreover, DenseNet is combined with pre-processing technique (i.e., principal component analysis or auto-encoder) or post-processing technique (i.e., conditional random field) to further improve classification performance. For semi-supervised HSI fine-grained classification, a generative adversarial network (GAN), which includes a discriminative CNN and a generative CNN, is carefully designed. The GAN fully uses the labeled and unlabeled s les to improve classification accuracy. The proposed methods were tested on the Indian Pines data set, which contains 33,3951 s les with 52 classes. The experimental results show that the deep learning-based methods provide great improvements compared with other traditional methods, which demonstrate that deep models have huge potential for HSI fine-grained classification.
Publisher: IEEE
Date: 06-2012
Publisher: Wiley
Date: 19-09-2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Wiley
Date: 08-05-2018
DOI: 10.1111/COIN.12177
Publisher: IEEE
Date: 2009
Publisher: IEEE
Date: 07-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2013
Publisher: MDPI AG
Date: 11-06-2017
DOI: 10.3390/RS9060592
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Wiley
Date: 10-10-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 10-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2015
Publisher: AIP
Date: 2010
DOI: 10.1063/1.3314268
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2014
Publisher: SPIE
Date: 17-09-2009
DOI: 10.1117/12.830046
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2016
Publisher: MDPI AG
Date: 28-02-2019
DOI: 10.3390/RS11050492
Abstract: In order to reconstruct a high spatial and high spectral resolution image (H2SI), one of the most common methods is to fuse a hyperspectral image (HSI) with a corresponding multispectral image (MSI). To effectively obtain both the spectral correlation of bands in HSI and the spatial correlation of pixels in MSI, this paper proposes an adversarial selection fusion (ASF) method for the HSI–MSI fusion problem. Firstly, the unmixing based fusion (UF) method is adopted to dig out the spatial correlation in MSI. Then, to acquire the spectral correlation in HSI, a band reconstruction-based fusion (BRF) method is proposed, regarding H2SI as the product of the optimized band image dictionary and reconstruction coefficients. Finally, spectral spatial quality (SSQ) index is designed to guide the adversarial selection process of UF and BRF. Experimental results on four real-world images demonstrate that the proposed strategy achieves smaller errors and better reconstruction results than other comparison methods.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2009
Publisher: SPIE
Date: 03-04-2008
DOI: 10.1117/12.777016
Publisher: Elsevier BV
Date: 10-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 2009
Publisher: IEEE
Date: 11-2017
Publisher: IEEE
Date: 07-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2016
Publisher: SPIE
Date: 10-06-2007
DOI: 10.1117/12.761233
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1999
DOI: 10.1109/36.739109
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
No related grants have been discovered for Xiuping Jia.