Blind hyperspectral sparse unmixing based on online dictionary learning

Xiaorui, Song; Wu, Lei; Hao, Hongxing

doi:10.1117/12.2325087

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…Grounded in the conceptual framework presented above, an innovative hyperspectral image unmixing technique rooted in online dictionary learning strategies was introduced in [32]. This technique adheres to the traditional dictionary learning paradigm, wherein the background endmember dictionary is systematically derived by optimizing an objective function configured in the least absolute shrinkage and selection operator (LASSO) framework as follows: min…”

Section: Adaptive Dictionary Learning-based Background Endmember Extr...mentioning

confidence: 99%

Adaptive Background Endmember Extraction for Hyperspectral Subpixel Object Detection

Yang,

Song,

Bai

et al. 2024

Remote Sensing

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Subpixel object detection presents a significant challenge within the domain of hyperspectral image (HSI) processing, primarily due to the inherently limited spatial resolution of imaging spectrometers. For subpixel object detection, the dimensional extent of the object of interest is smaller than an individual pixel, which significantly diminishes the utility of spatial information pertaining to the object. Therefore, the efficacy of detection algorithms depends heavily on the spectral data inherent in the image. The detection of subpixel objects in hyperspectral imagery primarily relies on the suppression of the background and the enhancement of the object of interest. Hence, acquiring accurate background information from HSI images is a crucial step. In this study, an adaptive background endmember extraction for hyperspectral subpixel object detection is proposed. An adaptive scale constraint is incorporated into the background spectral endmember learning process to improve the adaptability of background endmember extraction, thus further enhancing the algorithm’s generalizability and applicability in diverse analytical scenarios. Experimental results demonstrate that the adaptive endmember extraction-based subpixel object detection algorithm consistently outperforms existing state-of-the-art algorithms in terms of detection efficacy on both simulated and real-world datasets.

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Section: Adaptive Dictionary Learning-based Background Endmember Extr...mentioning

confidence: 99%

Adaptive Background Endmember Extraction for Hyperspectral Subpixel Object Detection

Yang,

Song,

Bai

et al. 2024

Remote Sensing

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show abstract

“…However, this approach requires quite a high signal to noise ratio (SNR). With regard to dictionary learning theory, an endmember extraction algorithm based on online dictionary learning (EEODL) has been proposed [20]. This method reduces the computational complexity by using the online scheme and it performs well with highly mixed images, although it is still somewhat sensitive to noise.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hyperspectral Endmember Extraction Algorithm Based on Online Robust Dictionary Learning

Xiaorui

2019

Remote Sensing

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Due to the sparsity of hyperspectral images, the dictionary learning framework has been applied in hyperspectral endmember extraction. However, current endmember extraction methods based on dictionary learning are not robust enough in noisy environments. To solve this problem, this paper proposes a novel endmember extraction approach based on online robust dictionary learning, termed EEORDL. Because of the large scale of the hyperspectral image (HSI) data, an online scheme is introduced to reduce the computational time of dictionary learning. In the proposed algorithm, a new form of the objective function is introduced into the dictionary learning process to improve the robustness for noisy HSI data. The experimental results, conducted with both synthetic and real-world hyperspectral datasets, illustrate that the proposed EEORDL outperforms the state-of-the-art approaches under different signal-to-noise ratio (SNR) conditions, especially for high-level noise.

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Detection of Subpixel Targets on Hyperspectral Remote Sensing Imagery Based on Background Endmember Extraction

Xiaorui

Zou

2021

IEEE Trans. Geosci. Remote Sensing

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Blind hyperspectral sparse unmixing based on online dictionary learning

Cited by 4 publications

References 5 publications

Adaptive Background Endmember Extraction for Hyperspectral Subpixel Object Detection

Adaptive Background Endmember Extraction for Hyperspectral Subpixel Object Detection

A Novel Hyperspectral Endmember Extraction Algorithm Based on Online Robust Dictionary Learning

Detection of Subpixel Targets on Hyperspectral Remote Sensing Imagery Based on Background Endmember Extraction

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