Sparse Hyperspectral Unmixing Using Spectral Library Adaptive Adjustment

Zhang, Zuoyu; Liao, Shouyi; Hao, Fang; Wang, Shicheng

doi:10.1109/jstars.2019.2939829

Cited by 4 publications

(3 citation statements)

References 54 publications

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“…And at the end of MLP head, softmax layer is used to meet the physical constraints of the generated abundance, i.e., the ANC and ASC, respectively. For the input HSI patches with natural random noise, the noise increases the sample diversity of HSI patches, and the pattern collapse problem of GAN [39] can be mitigated when it is used as conditional input, which allows HyperGAN to generate high-quality abundance. Due to the manipulation on HSI patches rather than individual pixel, our method is able to exploit the spatial information in HSI.…”

Section: B Hyperspectral Unmixing Structure Based On Conditional Gene...mentioning

confidence: 99%

“…Most current methods for generating synthetic data typically initiate the process by creating individual abundance vectors that are either entirely random or adhere to specific distributions [39], [44]. Subsequently, these vectors are multiplied with the endmembers matrix to obtain the resulting mixed hyperspectral pixels.…”

Section: The Synthesis Of Hyperspectral Data With Spatial Structurementioning

confidence: 99%

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Pixel-to-Abundance Translation: Conditional Generative Adversarial Networks Based on Patch Transformer for Hyperspectral Unmixing

Wang,

Zhang,

Zhang

et al. 2024

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

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Spectral unmixing is a significant challenge in hyperspectral image processing. Existing unmixing methods utilize prior knowledge about the abundance distribution to solve the regularization optimization problem, where the difficulty lies in choosing appropriate prior knowledge and solving the complex regularization optimization problem. To solve these problems, we propose a hyperspectral conditional generative adversarial network (HyperGAN) method as a generic unmixing framework based on the following assumption: the unmixing process from pixel to abundance can be regarded as a transformation of two modalities with an internal specific relationship. The proposed HyperGAN is composed of a generator and a discriminator, the former completes the modal conversion from mixed hyperspectral pixel patch to the abundance of corresponding endmember of the central pixel and the latter is used to distinguish whether the distribution and structure of generated abundance are the same as the true ones. Additionally, we propose hyperspectral image (HSI) Patch Transformer as the main component of the generator, which utilizes adaptive attention score to capture the internal pixels correlation of the HSI patch and leverages the spatialspectral information in a fine-grained way to achieve optimization of the unmixing process. Furthermore, we propose a data synthesis method based on superpixel segmentation and random split to get synthetic data with a spatial structure to further evaluate the effectiveness of our model for unmixing. Experiments on synthetic data and real hyperspectral data achieve impressive results compared to state-of-the-art competitors.

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Section: B Hyperspectral Unmixing Structure Based On Conditional Gene...mentioning

confidence: 99%

Section: The Synthesis Of Hyperspectral Data With Spatial Structurementioning

confidence: 99%

Pixel-to-Abundance Translation: Conditional Generative Adversarial Networks Based on Patch Transformer for Hyperspectral Unmixing

Wang,

Zhang,

Zhang

et al. 2024

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

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“…Over the last decades, hyperspectral imaging could concurrently image an object or a scene via hundreds or thousands of narrow bands with a spectral range that covers the various visible and infrared bands, has faced a growing interest in multifarious fields such as mineral exploration, military reconnaissance, agricultural analysis, etc. (Ravel, et al, 2018, Zhang et al, 2019. Hyperspectral imaging, also known as imaging spectroscopy.…”

Section: Introductionmentioning

confidence: 99%

Unmixing of Hyperspectral Data Using Spectral Libraries

Küçük

Yüksel

2020

International Journal of Environment and Geoinformatics

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Dear colleagues and friends, X. TUFUAB Technical Symposium was held in Aksaray on 25-27, April 2019. The Symposium was carried out by the organizations of Aksaray University and General Directorate of Mapping. As a international symposium in the field of photogrammetry and remote sensing, X.TUFUAB Technical Symposium 2019 is devoted to promote the advancement of knowledge, research, development, education and training in Geographical Information Sciences, Information Technology, Environmental Management and Resources, Sustainable Agriculture, Surveying, Photogrammetry and Remote Sensing, their integration and applications, as to contribute to the well-being of humanity and the sustainability of the environment. 425 participants and scientists from 7 countries were attended to this symposium. 125 oral presentations and 10 poster presentations were presented during the symposium. 135 presentations take place in 25 sessions in two days.

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