Underwater Hyperspectral Target Detection with Band Selection [PDF]
Remote Sensing, 2020 Compared to multi-spectral imagery, hyperspectral imagery has very high spectral resolution with abundant spectral information. In underwater target detection, hyperspectral technology can be advantageous in the sense of a poor underwater imaging ...
Xianping Fu +5 more
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Representative Band Selection for Hyperspectral Image Classification [PDF]
ISPRS International Journal of Geo-Information, 2018 The high dimensionality of hyperspectral images (HSIs) brings great difficulty for their later data processing. Band selection, as a commonly used dimension reduction technique, is the selection of optimal band combinations from the original bands, while
Fuding Xie +3 more
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Unsupervised Hyperspectral Band Selection via Multimodal Evolutionary Algorithm and Subspace Decomposition [PDF]
Sensors, 2023 Unsupervised band selection is an essential task to search for representative bands in hyperspectral dimension reduction. Most of existing studies utilize the inherent attribute of hyperspectral image (HSI) and acquire single optimal band subset while ...
Yunpeng Wei +3 more
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Hyperspectral Band Selection via Optimal Combination Strategy
Remote Sensing, 2022 Band selection is one of the main methods of reducing the number of dimensions in a hyperspectral image. Recently, various methods have been proposed to address this issue.
Shuying Li +3 more
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Joint Learning of Correlation-Constrained Fuzzy Clustering and Discriminative Non-Negative Representation for Hyperspectral Band Selection [PDF]
Sensors, 2023 Hyperspectral band selection plays an important role in overcoming the curse of dimensionality. Recently, clustering-based band selection methods have shown promise in the selection of informative and representative bands from hyperspectral images (HSIs).
Zelin Li, Wenhong Wang
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Two-Stage Unsupervised Hyperspectral Band Selection Based on Deep Reinforcement Learning
Remote SensingHyperspectral images are high-dimensional data that capture detailed spectral information across a wide range of wavelengths, enabling the precise identification and analysis of different materials or objects. However, the high dimensionality of the data
Yi Guo +4 more
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Mixed-Noise Band Selection for Hyperspectral Images [PDF]
IEEE Access, 2020 Hyperspectral images (HSIs) with abundant spectral information are generally susceptible to various types of noise, such as Gaussian noise and stripe noise.
Zhen Li, Chenwei Deng, Yun Huang
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BSDR: A Data-Efficient Deep Learning-Based Hyperspectral Band Selection Algorithm Using Discrete Relaxation. [PDF]
Sensors (Basel)Hyperspectral band selection algorithms are crucial for processing high-dimensional data, which enables dimensionality reduction, improves data analysis, and enhances computational efficiency.
Rahman M +4 more
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Hyperspectral Band Selection Method Based on Global Partition Clustering
Remote SensingBand selection is an important step in the dimensionality reduction processing of hyperspectral images and is highly important for eliminating redundant spectral information and reducing computational costs.
Tingrui Hu, Xian Guo, Peichao Gao
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Multiple Band Prioritization Criteria-Based Band Selection for Hyperspectral Imagery
Remote Sensing, 2022 Band selection (BS) is an effective pre-processing way to reduce the redundancy of hyperspectral data. Specifically, the band prioritization (BP) criterion plays an essential role since it can judge the importance of bands from a particular perspective ...
Xudong Sun +3 more
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