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Multi-Objective Unsupervised Band Selection Method for Hyperspectral Images Classification
IEEE Transactions on Image Processing, 2023With the increasing spectral dimension of hyperspectral images (HSI), how correctly choose bands based on band correlation and information has become more significant, but also complicated. Band selection is a combinatorial optimization problem, and intelligent optimization algorithms have been shown to be crucial in solving combinatorial optimization ...
Xianfeng Ou, Meng Wu, Bing Tu
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A New Unsupervised Hyperspectral Band Selection Method Based on Multiobjective Optimization
IEEE Geoscience and Remote Sensing Letters, 2017Unsupervised band selection methods usually assume specific optimization objectives, which may include band or spatial relationship. However, since one objective could only represent parts of hyperspectral characteristics, it is difficult to determine which objective is the most appropriate.
Xia Xu, Zhenwei Shi, Bin Pan
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Unsupervised Hyperspectral Band Selection by Fuzzy Clustering With Particle Swarm Optimization
IEEE Geoscience and Remote Sensing Letters, 2017Due to the lack of label information and the intrinsic complexity of hyperspectral images (HSIs), unsupervised band selection is always one of the most challenging tasks in HSI processing. Fuzzy clustering is a promising technique for unsupervised band selection, which can partition unlabeled data into groups effectively.
Mingyang Zhang, Maoguo Gong
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IEEE Transactions on Geoscience and Remote Sensing, 2016
Band selection is an important preprocessing step for hyperspectral image processing. Many valid criteria have been proposed for band selection, and these criteria model band selection as a single-objective optimization problem. In this paper, a novel multiobjective model is first built for band selection.
Maoguo Gong +2 more
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Band selection is an important preprocessing step for hyperspectral image processing. Many valid criteria have been proposed for band selection, and these criteria model band selection as a single-objective optimization problem. In this paper, a novel multiobjective model is first built for band selection.
Maoguo Gong +2 more
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Stochastic gate-based autoencoder for unsupervised hyperspectral band selection
Pattern Recognition, 2022Lizhi Wang, Hua Huang
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Unsupervised Hyperspectral Band Selection Based on Hypergraph Spectral Clustering
IEEE Geoscience and Remote Sensing Letters, 2022Hyperspectral images can provide spectral characteristics related to the physical properties of different materials, which arouses great interest in many fields. Band selection (BS) could effectively solve the problem of high dimensions and redundant information of HSI data.
Jingyu Wang 0002 +5 more
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Unsupervised band selection for hyperspectral image analysis
2007 IEEE International Geoscience and Remote Sensing Symposium, 2007Band selection is a common approach to reduce the data dimensionality of hyperspectral imagery. It extracts several bands of importance in some sense by taking advantage of high spectral correlation. Driven by detection or classification accuracy, one would expect that using a subset of original bands the accuracy is unchanged or tolerably degraded ...
Qian Du 0001, He Yang
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Marginalized Graph Self-Representation for Unsupervised Hyperspectral Band Selection
IEEE Transactions on Geoscience and Remote Sensing, 2022Unsupervised band selection is an essential step in preprocessing hyperspectral images (HSIs) to select informative bands. Most existing methods exploit the spatial information from the entire HSI while ignoring the difference between diverse homogeneous regions. Moreover, traditional methods utilize the limited size of data for model training that may
Yongshan Zhang +3 more
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Discovering Diverse Subset for Unsupervised Hyperspectral Band Selection
IEEE Transactions on Image Processing, 2017Band selection, as a special case of the feature selection problem, tries to remove redundant bands and select a few important bands to represent the whole image cube. This has attracted much attention, since the selected bands provide discriminative information for further applications and reduce the computational burden.
Yuan, Yuan +3 more
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Efficient Unsupervised Band Selection Through Spectral Rhythms
IEEE Journal of Selected Topics in Signal Processing, 2015The main goal of remote sensing image classification is to associate land cover classes to each pixel in the monitored area. In this sense, hyperspectral images play a key role by providing detailed spectral information per pixel. On the other hand, although the huge amount of spectral bands enables the creation of more accurate thematic maps, they can
Lilian Chaves Brandao dos Santos +2 more
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