Collaborative-guided spectral abundance learning with bilinear mixing model for hyperspectral subpixel target detection

Zhu, Dehui; Du, Bo; Hu, Meiqi; Dong, Yanni; Zhang, Liangpei

doi:10.1016/j.neunet.2023.02.002

Cited by 11 publications

(2 citation statements)

References 52 publications

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“…These include kernel methods [13], hierarchical structures [14][15][16], and fractional Fourier transforms [17]. To utilize in-scene spectra better, spectral unmixing techniques [18,19] and sparsity assumptions [20][21][22][23][24] are introduced to construct hyperspectral target detectors, which require certain assumptions.…”

Section: Introductionmentioning

confidence: 99%

Target Detection Adapting to Spectral Variability in Multi-Temporal Hyperspectral Images Using Implicit Contrastive Learning

Zhang,

Gao,

Wang

et al. 2024

Remote Sensing

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Hyperspectral target detection (HTD) is a crucial aspect of remote sensing applications, aiming to identify targets in hyperspectral images (HSIs) based on their known prior spectral signatures. However, the spectral variability resulting from various imaging conditions in multi-temporal hyperspectral images poses a challenge to both classical and deep learning (DL) methods. To overcome the limitations imposed by spectral variability, an implicit contrastive learning-based target detector (ICLTD) is proposed to exploit in-scene spectra in an unsupervised way. First, only prior spectra are utilized for explicit supervision, while an implicit contrastive learning module (ICLM) is designed to normalize the feature distributions of prior and in-scene spectra. This paper theoretically demonstrates that the ICLM can transfer the gradients from prior spectral features to those of in-scene spectra based on their feature similarities and differences. Because of transferred gradient signals, the ICLTD is regularized to extract similar representations for the prior and in-scene target spectra, while augmenting feature differences between the target and background spectra. Additionally, a local spectral similarity constraint (LSSC) is proposed to enhance the capability of scene adaptation by leveraging the spectral similarities among in-scene targets. To validate the performance of the ICLTD under spectral variability, multi-temporal HSIs captured under various imaging conditions are collected to generate prior spectra and in-scene spectra. Comparative evaluations against several DL detectors and classical methods reveal the superior performance of the ICLTD in achieving a balance between target detectability and background suppressibility under spectral variability.

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Section: Introductionmentioning

confidence: 99%

Target Detection Adapting to Spectral Variability in Multi-Temporal Hyperspectral Images Using Implicit Contrastive Learning

Zhang,

Gao,

Wang

et al. 2024

Remote Sensing

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“…Hyperspectral image (HSI) consists of hundreds of spectral bands with high spectral resolution, which turns to be one of the key trends of future satellite imaging. HSI produces remarkably detailed information of the Earth's surface, beneficial for fine-grained classification [20], [21], subpixel unmixing [22], [23], anomaly and target detection [24], [25], and change detection [26], [27]. By leveraging the capability of HSI, hyperspectral change detection (HCD) provides unprecedent potential to discriminate the subtle and detailed changes.…”

Section: Introductionmentioning

confidence: 99%

Multi-Temporal Spatial-Spectral Comparison Network For Hyperspectral Anomalous Change Detection

2022

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

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High spectral resolution imagery of the Earth's surface enables users to monitor changes over time in fine-grained scale, playing an increasingly important role in agriculture, defense, and emergency response. However, most current algorithms are still confined to describing local features and fail to incorporate a global perspective, which limits their ability to capture interactions between global features, thus usually resulting in incomplete change regions. In this paper, we proposed a Global Multi-head INteractive self-attention change Detection network (GlobalMind) to explore the implicit correlation between different surface objects and variant land cover transformations, acquiring a comprehensive understanding of the data and accurate change detection result. Firstly, a simple but effective Global Axial Segmentation (GAS) strategy is designed to expand the self-attention computation along the row space or column space of hyperspectral images, allowing the global connection with high efficiency. Secondly, with GAS, the global spatial multi-head interactive self-attention (Global-M) module is crafted to mine the abundant spatial-spectral feature involving potential correlations between the ground objects from the entire rich and complex hyperspectral space. Moreover, to acquire the accurate and complete cross-temporal changes, we devise a global temporal interactive multi-head self-attention (GlobalD) module which incorporates the relevance and variation of bi-temporal spatialspectral features, deriving the integrate potential same kind of changes in the local and global range with the combination of GAS. We perform extensive experiments on five mostly used hyperspectral datasets, and our method outperforms the state-ofthe-art algorithms with high accuracy and efficiency.

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Active learning based on similarity level histogram and adaptive-scale sampling for very high resolution image classification

Li,

Gao,

Yang

et al. 2023

Neural Networks

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Collaborative-guided spectral abundance learning with bilinear mixing model for hyperspectral subpixel target detection

Cited by 11 publications

References 52 publications

Target Detection Adapting to Spectral Variability in Multi-Temporal Hyperspectral Images Using Implicit Contrastive Learning

Target Detection Adapting to Spectral Variability in Multi-Temporal Hyperspectral Images Using Implicit Contrastive Learning

Multi-Temporal Spatial-Spectral Comparison Network For Hyperspectral Anomalous Change Detection

Active learning based on similarity level histogram and adaptive-scale sampling for very high resolution image classification

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