ORCID Profile
0000-0001-5334-3574
Current Organisation
Monash University
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Photogrammetry and Remote Sensing | Pattern Recognition and Data Mining | Artificial Intelligence and Image Processing
Expanding Knowledge in the Earth Sciences | Expanding Knowledge in the Information and Computing Sciences |
Publisher: ACM
Date: 13-08-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2012
Publisher: Springer Science and Business Media LLC
Date: 22-07-2016
Publisher: Springer Science and Business Media LLC
Date: 06-02-2019
Publisher: Springer Science and Business Media LLC
Date: 18-11-2020
Publisher: MDPI AG
Date: 04-03-2019
DOI: 10.3390/RS11050523
Abstract: Latest remote sensing sensors are capable of acquiring high spatial and spectral Satellite Image Time Series (SITS) of the world. These image series are a key component of classification systems that aim at obtaining up-to-date and accurate land cover maps of the Earth’s surfaces. More specifically, current SITS combine high temporal, spectral and spatial resolutions, which makes it possible to closely monitor vegetation dynamics. Although traditional classification algorithms, such as Random Forest (RF), have been successfully applied to create land cover maps from SITS, these algorithms do not make the most of the temporal domain. This paper proposes a comprehensive study of Temporal Convolutional Neural Networks (TempCNNs), a deep learning approach which applies convolutions in the temporal dimension in order to automatically learn temporal (and spectral) features. The goal of this paper is to quantitatively and qualitatively evaluate the contribution of TempCNNs for SITS classification, as compared to RF and Recurrent Neural Networks (RNNs) —a standard deep learning approach that is particularly suited to temporal data. We carry out experiments on Formosat-2 scene with 46 images and one million labelled time series. The experimental results show that TempCNNs are more accurate than the current state of the art for SITS classification. We provide some general guidelines on the network architecture, common regularization mechanisms, and hyper-parameter values such as batch size we also draw out some differences with standard results in computer vision (e.g., about pooling layers). Finally, we assess the visual quality of the land cover maps produced by TempCNNs.
Publisher: Springer International Publishing
Date: 2015
Publisher: Springer International Publishing
Date: 2016
Publisher: IEEE
Date: 07-2012
Publisher: Elsevier BV
Date: 03-2011
Publisher: IEEE
Date: 12-2014
DOI: 10.1109/ICDM.2014.23
Publisher: ACM
Date: 13-08-2016
Publisher: IEEE
Date: 12-2014
DOI: 10.1109/ICDM.2014.27
Publisher: IEEE
Date: 12-2013
DOI: 10.1109/ICDM.2013.17
Publisher: Springer International Publishing
Date: 2020
Publisher: Springer Science and Business Media LLC
Date: 22-05-2018
Publisher: Springer Science and Business Media LLC
Date: 13-07-2020
Publisher: Springer Science and Business Media LLC
Date: 15-04-2016
Publisher: Springer Science and Business Media LLC
Date: 14-06-2016
Publisher: American Geophysical Union (AGU)
Date: 29-06-2020
DOI: 10.1029/2019WR026255
Abstract: Commercial microwave links (CMLs) have proven useful for providing rainfall information close to the ground surface. However, large uncertainties are associated with these retrievals, partly due to challenges in the type of data collection and processing. In particular, the most common case is when only minimum and maximum received signal levels (RSLs) over a given time interval (hereafter 15 min) are stored by mobile network operators. The average attenuation and the corresponding rainfall rate are then calculated based on a weighted average method using the minimum and maximum attenuation. In this study, an alternative to using a constant weighted average method is explored, based on a machine learning model trained to produce actual attenuation from minimum/maximum values. A rainfall retrieval deep learning model was designed based on a long short‐term memory (LSTM) model architecture and trained with disdrometer data in a form that is comparable to the data provided by mobile network operators. A first evaluation used only disdrometer data to mimic both attenuation from a CML and corresponding rainfall rates. For the test data set, the relative bias was reduced from 5.99% to 2.84% and the coefficient of determination ( R 2 ) increased from 0.86 to 0.97. The second evaluation used this disdrometer‐trained LSTM to retrieve rainfall rates from an actual CML located nearby the disdrometer. A significant improvement in the overall rainfall estimation compared to existing microwave link attenuation models was observed. The relative bias reduced from 7.39% to −1.14% and the R 2 improved from 0.71 to 0.82.
Publisher: Informa UK Limited
Date: 27-03-2014
Publisher: Elsevier BV
Date: 2012
Publisher: IEEE
Date: 07-2013
Publisher: IEEE
Date: 07-2012
Publisher: World Scientific Pub Co Pte Lt
Date: 12-2011
DOI: 10.1142/S0129065711003024
Abstract: Satellite Image Time Series (SITS) provide us with precious information on land cover evolution. By studying these series of images we can both understand the changes of specific areas and discover global phenomena that spread over larger areas. Changes that can occur throughout the sensing time can spread over very long periods and may have different start time and end time depending on the location, which complicates the mining and the analysis of series of images. This work focuses on frequent sequential pattern mining (FSPM) methods, since this family of methods fits the above-mentioned issues. This family of methods consists of finding the most frequent evolution behaviors, and is actually able to extract long-term changes as well as short term ones, whenever the change may start and end. However, applying FSPM methods to SITS implies confronting two main challenges, related to the characteristics of SITS and the domain's constraints. First, satellite images associate multiple measures with a single pixel (the radiometric levels of different wavelengths corresponding to infra-red, red, etc.), which makes the search space multi-dimensional and thus requires specific mining algorithms. Furthermore, the non evolving regions, which are the vast majority and overwhelm the evolving ones, challenge the discovery of these patterns. We propose a SITS mining framework that enables discovery of these patterns despite these constraints and characteristics. Our proposal is inspired from FSPM and provides a relevant visualization principle. Experiments carried out on 35 images sensed over 20 years show the proposed approach makes it possible to extract relevant evolution behaviors.
Publisher: Elsevier BV
Date: 10-2012
Publisher: Springer Science and Business Media LLC
Date: 05-03-2020
Publisher: IEEE
Date: 07-2011
Publisher: IEEE
Date: 07-2011
Publisher: IEEE
Date: 07-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2014
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Elsevier BV
Date: 11-2014
DOI: 10.1016/J.ARTMED.2014.10.007
Abstract: Surgery is one of the riskiest and most important medical acts that is performed today. Understanding the ways in which surgeries are similar or different from each other is of major interest. Desires to improve patient outcomes and surgeon training, and to reduce the costs of surgery, all motivate a better understanding of surgical practices. To facilitate this, surgeons have started recording the activities that are performed during surgery. New methods have to be developed to be able to make the most of this extremely rich and complex data. The objective of this work is to enable the simultaneous comparison of a set of surgeries, in order to be able to extract high-level information about surgical practices. We introduce non-linear temporal scaling (NLTS): a method that finds a multiple alignment of a set of surgeries. Experiments are carried out on a set of lumbar disc neurosurgeries. We assess our method both on a highly standardised phase of the surgery (closure) and on the whole surgery. Experiments show that NLTS makes it possible to consistently derive standards of surgical practice and to understand differences between groups of surgeries. We take the training of surgeons as the common theme for the evaluation of the results and highlight, for ex le, the main differences between the practices of junior and senior surgeons in the removal of a lumbar disc herniation. NLTS is an effective and efficient method to find a multiple alignment of a set of surgeries. NLTS realigns a set of sequences along their intrinsic timeline, which makes it possible to extract standards of surgical practices.
Publisher: Society for Industrial and Applied Mathematics
Date: 30-06-2015
Publisher: Springer Science and Business Media LLC
Date: 22-09-2016
Publisher: IEEE
Date: 07-2011
Start Date: 2015
End Date: 2016
Funder: Air Force Research Laboratory
View Funded ActivityStart Date: 06-2017
End Date: 06-2020
Amount: $329,287.00
Funder: Australian Research Council
View Funded Activity