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Cross-modal data integration and spectral optimization for enhanced individual apple tree canopy nitrogen concentration estimation using UAV remote sensing. [PDF]
Plant PhenomicsChen B, Zhang N, Li Y, Li Z, Chai X.
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A Deep Learning Approach for Pixel-Level Material Classification via Hyperspectral Imaging. [PDF]
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Hyperspectral Band Selection: A Review
IEEE Geoscience and Remote Sensing Magazine, 2019A hyperspectral imaging sensor collects detailed spectral responses from ground objects using hundreds of narrow bands; this technology is used in many real-world applications. Band selection aims to select a small subset of hyperspectral bands to remove spectral redundancy and reduce computational costs while preserving the significant spectral ...
Qian Du, Weiwei Sun
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Constrained band selection for hyperspectral imagery [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2006 Constrained energy minimization (CEM) has shown effective in hyperspectral target detection. It linearly constrains a desired target signature while minimizing interfering effects caused by other unknown signatures. This paper explores this idea for band selection and develops a new approach to band selection, referred to as constrained band selection (
Chein-I Chang, Su Wang 0002
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Gray Wolf Optimizer for hyperspectral band selection
Applied Soft Computing Journal, 2016Graphical abstractDisplay Omitted HighlightsWe propose a new approach for feature selection in hyperspectral image classification.The problem of band selection is reformulated as a combinatorial problem.We design a new objective function which takes into account two term, the classification error rate and the class separability distance.To optimize the
Abdelkader Benyettou
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Hyperspectral Band Selection by Multitask Sparsity Pursuit
IEEE Transactions on Geoscience and Remote Sensing, 2015Hyperspectral images have been proved to be effective for a wide range of applications; however, the large volume and redundant information also bring a lot of inconvenience at the same time. To cope with this problem, hyperspectral band selection is a pertinent technique, which takes advantage of removing redundant components without compromising the ...
Yuan Yuan 0001 +2 more
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Hyperspectral Band Selection via Rank Minimization
IEEE Geoscience and Remote Sensing Letters, 2017Band selection is an important preprocessing technique for hyperspectral imagery, through which a subset of critical and representative spectral bands can be selected from a raw image cube for compact yet effect representation. Among the valid selection strategies, performing band selection in an unsupervised manner is usually considered more general ...
Guokang Zhu, Ning Wei, Shuying Li
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Hyperspectral Band Selection Based on Rough Set
IEEE Transactions on Geoscience and Remote Sensing, 2015Band selection is a well-known approach to reduce the dimensionality of hyperspectral imagery. Rough set theory is a paradigm to deal with uncertainty, vagueness, and incompleteness of data. Although it has been applied successfully to feature selection in different application domains, it is seldom used for the analysis of the hyperspectral imagery ...
Lorenzo Bruzzone, Swarnajyoti Patra
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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 +2 more
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