ABLAL: Adaptive Background Latent Space Adversarial Learning Algorithm for Hyperspectral Target Detection

Sun, Long; Ma, Zongfang; Zhang, Yi

doi:10.1109/jstars.2023.3329771

Cited by 8 publications

(1 citation statement)

References 33 publications

(46 reference statements)

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“…The rich spectral information helps to accurately identify these observed targets, which is beneficial to fine classification, and the image information retains the spatial distribution of the scene, providing context support for the subsequent interpretation. Therefore, hyperspectral images are increasingly and successfully applied in the fields of agriculture [1][2][3][4], ecological science [5,6], military [7][8][9][10], and atmospheric detection [11][12][13]. However, constrained by the law of conservation of energy and imaging capability of the sensors, hyperspectral data have the problems of lower spatial resolution and smaller imaging widths, universally.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Hyperspectral Image Collaborative Mapping Model Based on Adaptive Learning for Fine Classification

Zhang,

Liu,

Zhang

et al. 2024

Remote Sensing

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Hyperspectral (HS) data, encompassing hundreds of spectral channels for the same area, offer a wealth of spectral information and are increasingly utilized across various fields. However, their limitations in spatial resolution and imaging width pose challenges for precise recognition and fine classification in large scenes. Conversely, multispectral (MS) data excel in providing spatial details for vast landscapes but lack spectral precision. In this article, we proposed an adaptive learning-based mapping model, including an image fusion module, spectral super-resolution network, and adaptive learning network. Spectral super-resolution networks learn the mapping between multispectral and hyperspectral images based on the attention mechanism. The image fusion module leverages spatial and spectral consistency in training data, providing pseudo labels for spectral super-resolution training. And the adaptive learning network incorporates spectral response priors via unsupervised learning, adjusting the output of the super-resolution network to preserve spectral information in reconstructed data. Through the experiment, the model eliminates the need for the manual setting of image prior information and complex parameter selection, and can adjust the network structure and parameters dynamically, eventually enhancing the reconstructed image quality, and enabling the fine classification of large-scale scenes with high spatial resolution. Compared with the recent dictionary learning and deep learning spectral super-resolution methods, our approach exhibits superior performance in terms of both image similarity and classification accuracy.

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Section: Introductionmentioning

confidence: 99%

A Multi-Hyperspectral Image Collaborative Mapping Model Based on Adaptive Learning for Fine Classification

Zhang,

Liu,

Zhang

et al. 2024

Remote Sensing

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Multi-view Cross-Attention Network for Hyperspectral Object Tracking

Zhu,

Wang,

Wang

et al. 2024

Lecture Notes in Computer Science

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Tucker Decomposition-Based Network Compression for Anomaly Detection With Large-Scale Hyperspectral Images

Wang,

Zhao

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Deep learning methodologies have demonstrated considerable effectiveness in hyperspectral anomaly detection (HAD). However, the practicality of deep learning-based HAD in real-world applications is impeded by challenges arising from limited labeled data, large-scale hyperspectral images and constrained computational resources. In light of these challenges, this paper introduces a convolutional neural network-based HAD model through the incorporation of Tucker decomposition, named as TD-CNND. Drawing inspiration from transfer learning, the proposed model initially constructs pixel sample pairs from known labeled hyperspectral images in the source domain, feeding them into the designed CNN to train the network learning spectral feature differences to obtain a convolutional neural network containing knowledge from the source domain. Subsequently, to prevent the need for network retraining caused by structural changes and to reduce model parameters for improving detecting timeliness, a general network compression scheme based on Tucker decomposition is applied to the convolutional neural network, where the convolutional layers of the above CNN undergo Tucker tensor decomposition to compress the network and alleviate parameter redundancy. Finally, spectral features realignment is used to recover the detection accuracy loss caused by Tucker tensor decomposition. In addition, a dual-windows structure is implemented during the detection phase, incorporating spatial information to the aforementioned spectrallevel learning model, facilitating spectral-spatial collaborative hyperspectral anomaly detection. Experimental evaluations using three real hyperspectral datasets and artificially expanded datasets demonstrate that, in comparison with state-of-the-art methods, the proposed TD-CNND method exhibits effectiveness and superiority in terms of both time cost and detection accuracy, where the notable advantages in terms of time cost become more pronounced with an increasing number of pixels.

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ABLAL: Adaptive Background Latent Space Adversarial Learning Algorithm for Hyperspectral Target Detection

Cited by 8 publications

References 33 publications

A Multi-Hyperspectral Image Collaborative Mapping Model Based on Adaptive Learning for Fine Classification

A Multi-Hyperspectral Image Collaborative Mapping Model Based on Adaptive Learning for Fine Classification

Multi-view Cross-Attention Network for Hyperspectral Object Tracking

Tucker Decomposition-Based Network Compression for Anomaly Detection With Large-Scale Hyperspectral Images

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