ORCID Profile
0000-0002-4429-6590
Current Organisations
Hajee Mohammad Danesh Science and Technology University
,
Deakin University
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Publisher: IEEE
Date: 11-2018
Publisher: IEEE
Date: 10-2014
Publisher: Elsevier BV
Date: 02-2019
Publisher: IEEE
Date: 10-2014
Publisher: IEEE
Date: 07-2019
Publisher: IEEE
Date: 2017
Publisher: IEEE
Date: 12-2017
Publisher: Springer Nature Singapore
Date: 2023
Publisher: Springer Science and Business Media LLC
Date: 31-10-2012
DOI: 10.1038/NATURE11632
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 05-2014
Publisher: Informa UK Limited
Date: 16-07-2020
Publisher: MDPI AG
Date: 06-01-2023
DOI: 10.3390/S23020657
Abstract: A hyperspectral image (HSI), which contains a number of contiguous and narrow spectral wavelength bands, is a valuable source of data for ground cover examinations. Classification using the entire original HSI suffers from the “curse of dimensionality” problem because (i) the image bands are highly correlated both spectrally and spatially, (ii) not every band can carry equal information, (iii) there is a lack of enough training s les for some classes, and (iv) the overall computational cost is high. Therefore, effective feature (band) reduction is necessary through feature extraction (FE) and/or feature selection (FS) for improving the classification in a cost-effective manner. Principal component analysis (PCA) is a frequently adopted unsupervised FE method in HSI classification. Nevertheless, its performance worsens when the dataset is noisy, and the computational cost becomes high. Consequently, this study first proposed an efficient FE approach using a normalized mutual information (NMI)-based band grouping strategy, where the classical PCA was applied to each band subgroup for intrinsic FE. Finally, the subspace of the most effective features was generated by the NMI-based minimum redundancy and maximum relevance (mRMR) FS criteria. The subspace of features was then classified using the kernel support vector machine. Two real HSIs collected by the AVIRIS and HYDICE sensors were used in an experiment. The experimental results demonstrated that the proposed feature reduction approach significantly improved the classification performance. It achieved the highest overall classification accuracy of 94.93% for the AVIRIS dataset and 99.026% for the HYDICE dataset. Moreover, the proposed approach reduced the computational cost compared with the studied methods.
Publisher: IEEE
Date: 12-2017
Publisher: IEEE
Date: 02-2018
Publisher: IEEE
Date: 05-2014
Publisher: IEEE
Date: 02-2018
Publisher: Informa UK Limited
Date: 03-01-2022
Publisher: IEEE
Date: 05-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 12-2019
Publisher: Informa UK Limited
Date: 16-04-2019
Publisher: Informa UK Limited
Date: 10-11-2020
Publisher: IEEE
Date: 02-2018
Publisher: IEEE
Date: 11-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 11-2018
Publisher: IEEE
Date: 02-2018
Publisher: IEEE
Date: 12-2017
Publisher: IEEE
Date: 02-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Informa UK Limited
Date: 19-11-2020
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: Universitas Ahmad Dahlan
Date: 10-2021
Publisher: MDPI AG
Date: 13-07-2023
DOI: 10.3390/RS15143532
Abstract: Object classification in hyperspectral images involves accurately categorizing objects based on their spectral characteristics. However, the high dimensionality of hyperspectral data and class imbalance pose significant challenges to object classification performance. To address these challenges, we propose a framework that incorporates dimensionality reduction and re-s ling as preprocessing steps for a deep learning model. Our framework employs a novel subgroup-based dimensionality reduction technique to extract and select the most informative features with minimal redundancy. Additionally, the data are res led to achieve class balance across all categories. The reduced and balanced data are then processed through a hybrid CNN model, which combines a 3D learning block and a 2D learning block to extract spectral–spatial features and achieve satisfactory classification accuracy. By adopting this hybrid approach, we simplify the model while improving performance in the presence of noise and limited s le size. We evaluated our proposed model on the Salinas scene, Pavia University, and Kennedy Space Center benchmark hyperspectral datasets, comparing it to state-of-the-art methods. Our object classification technique achieves highly promising results, with overall accuracies of 99.98%, 99.94%, and 99.46% on the three datasets, respectively. This proposed approach offers a compelling solution to overcome the challenges of high dimensionality and class imbalance in hyperspectral object classification.
Publisher: MDPI AG
Date: 20-02-2023
DOI: 10.3390/RS15041147
Abstract: Hyperspectral images (HSIs) are one of the most successfully used tools for precisely and potentially detecting key ground surfaces, vegetation, and minerals. HSIs contain a large amount of information about the ground scene therefore, object classification becomes the most difficult task for such a high-dimensional HSI data cube. Additionally, the HSI’s spectral bands exhibit a high correlation, and a large amount of spectral data creates high dimensionality issues as well. Dimensionality reduction is, therefore, a crucial step in the HSI classification pipeline. In order to identify a pertinent subset of features for effective HSI classification, this study proposes a dimension reduction method that combines feature extraction and feature selection. In particular, we exploited the widely used denoising method minimum noise fraction (MNF) for feature extraction and an information theoretic-based strategy, cross-cumulative residual entropy (CCRE), for feature selection. Using the normalized CCRE, minimum redundancy maximum relevance (mRMR)-driven feature selection criteria were used to enhance the quality of the selected feature. To assess the effectiveness of the extracted features’ subsets, the kernel support vector machine (KSVM) classifier was applied to three publicly available HSIs. The experimental findings manifest a discernible improvement in classification accuracy and the qualities of the selected features. Specifically, the proposed method outperforms the traditional methods investigated, with overall classification accuracies on Indian Pines, Washington DC Mall, and Pavia University HSIs of 97.44%, 99.71%, and 98.35%, respectively.
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: Informa UK Limited
Date: 19-03-2020
Location: Bangladesh
Location: Bangladesh
No related grants have been discovered for Md Palash Uddin.