Hyperspectral Intrinsic Image Decomposition Based on Physical Prior-Driven Unsupervised Learning

Hyperspectral image (HSI) feature extraction is an important means to improve the classification of different ground features. According to the structural characteristics of hyperspectral data, the general feature extraction scheme can extract features from the point of view of spectral dimension, spatial and spatial spectrum. And the feature extraction time is also an index to measure the feature extraction method. Therefore, from the perspective of spatial dimension, this paper explores the relationship between HSI feature extraction time and training sample ratio. Three groups of HSIs sets were used for correlation test and analysis in the experiment. According to the characteristics of different data sets, the best selection scheme between spatial domain feature extraction method and training samples is given.

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Hyperspectral Intrinsic Image Decomposition Based on Physical Prior-Driven Unsupervised Learning

Cited by 3 publications

References 41 publications

HyperDID: Hyperspectral Intrinsic Image Decomposition With Deep Feature Embedding

HyperDID: Hyperspectral Intrinsic Image Decomposition With Deep Feature Embedding

Deep Intrinsic Decomposition With Adversarial Learning for Hyperspectral Image Classification

Research on the relationship between feature extraction time and training samples of hyperspectral image based on spatial domain

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