Hyperspectral Band Selection with Convolutional Neural Network

Cai, Rui; Yuan, Yuan; Lu, Xiaoqiang

doi:10.1007/978-3-030-03341-5_33

Cited by 7 publications

(4 citation statements)

References 20 publications

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“…From the classification loss in the pipeline, with the help of back-propagation, we jointly learn the binary mask that satisfies the constraint for a given number of bands and the parameters of the classification network, implementing the final task of classification from the selected bands. The proposed architecture is tested over publicly available datasets and compared with the state-of-the-art approaches in HBS [11,16,[23][24][25][26][27]. Our experimental results show higher classification accuracy than the compared HBS techniques for the same number of selected bands.…”

Section: Introductionmentioning

confidence: 95%

“…HBS based on attention mappings is used in [42], where DL models produce more sophisticated feature maps for classification with the most informative sets of bands by optimizing the deep CNNs. In [26], contribution maps of each class were produced to record the discriminative band locations, which are progressively added to CNNs to select more distinguished bands. Recently, the work in [27] introduced the concept of band-independent convolution and hard thresholding (BHCNN) to select the bands for the classification task.…”

Section: Supervised Hyperspectral Band Selectionmentioning

confidence: 99%

“…The MLBS is a supervised technique, where we train the proposed joint band selection and classification neural network utilizing the training dataset described earlier. For a fair comparison, the proposed approach is compared with state-of-the-art supervised techniques such as MEAC [28], BHCNN [27], and CMCNN [26]. While MEAC is a signal processing-based technique, BHCNN and CMCNN are deep neural network-based approaches like MLBS.…”

Section: Quantitative Analysis and Comparisonsmentioning

confidence: 99%

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Learning-Based Optimization of Hyperspectral Band Selection for Classification

Ayna,

Mdrafi,

et al. 2023

Remote Sensing

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Hyperspectral sensors acquire spectral responses from objects with a large number of narrow spectral bands. The large volume of data may be costly in terms of storage and computational requirements. In addition, hyperspectral data are often information-wise redundant. Band selection intends to overcome these limitations by selecting a small subset of spectral bands that provide more information or better performance for particular tasks. However, existing band selection techniques do not directly maximize the task-specific performance, but rather utilize hand-crafted metrics as a proxy to the final goal of performance improvement. In this paper, we propose a deep learning (DL) architecture composed of a constrained measurement learning network for band selection, followed by a classification network. The proposed joint DL architecture is trained in a data-driven manner to optimize the classification loss along band selection. In this way, the proposed network directly learns to select bands that enhance the classification performance. Our evaluation results with Indian Pines (IP) and the University of Pavia (UP) datasets show that the proposed constrained measurement learning-based band selection approach provides higher classification accuracy compared to the state-of-the-art supervised band selection methods for the same number of bands selected. The proposed method shows 89.08% and 97.78% overall accuracy scores for IP and UP respectively, being 1.34% and 2.19% higher than the second-best method.

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Section: Introductionmentioning

confidence: 95%

Section: Supervised Hyperspectral Band Selectionmentioning

confidence: 99%

Section: Quantitative Analysis and Comparisonsmentioning

confidence: 99%

See 1 more Smart Citation

Learning-Based Optimization of Hyperspectral Band Selection for Classification

Ayna,

Mdrafi,

et al. 2023

Remote Sensing

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“…There are many ways to achieve BS. According to [1], they can roughly be classified as six divisions: ranking-based [3][4][5][6], searchingbased [7][8][9][10][11][12][13][14][15][16][17][18], clustering-based [19][20][21][22][23], sparsity-based [24][25][26][27][28][29], embedding-based [30][31][32][33][34], and hybrid scheme based [35][36][37].…”

Section: Introductionmentioning

confidence: 99%

A Band Subset Selection Approach Based on Sparse Self-Representation and Band Grouping for Hyperspectral Image Classification

Liu

Chen

2022

Remote Sensing

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Band subset selection (BSS) is one of the ways to implement band selection (BS) for a hyperspectral image (HSI). Different from conventional BS methods, which select bands one by one, BSS selects a band subset each time and preserves the best one from the collection of the band subsets. This paper proposes a BSS method, called band grouping-based sparse self-representation BSS (BG-SSRBSS), for hyperspectral image classification. It formulates BS as a sparse self-representation (SSR) problem in which the entire bands can be represented by a set of informatively complementary bands. The BG-SSRBSS consists of two steps. To tackle the issue of selecting redundant bands, it first applies band grouping (BG) techniques to pre-group the entire bands to form multiple band groups, and then performs band group subset selection (BGSS) to find the optimal band group subset. The corresponding representative bands are taken as the BS result. To efficiently find the nearly global optimal subset among all possible band group subsets, sequential and successive iterative search algorithms are adopted. Land cover classification experiments conducted on three real HSI datasets show that BG-SSRBSS can improve classification accuracy by 4–20% compared to the existing BSS methods and requires less computation time.

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