Foreword to the Special Issue on Hyperspectral Image and Signal Processing

This paper proposes a framework for hyperspectral images (HSIs) classification with composite kernels discriminant analysis (CKDA). The CKDA uses the spectral and spatial information extracted by Gaussian weighted local mean operator (GWLM) and is suitable to solve few labeled samples classification problem of HSI, which has very important practical significance for the case that training samples are insufficient due to high cost. Experimental results show that the spatial information extracted by GWLM can greatly improve the performance, and demonstrate the superiority of CKDA for HSI classification in the case of few labeled samples. Compared with other state-of-the-art spectralspatial kernel methods, the proposed methods also show very good advantages, especially the parallel kernel method.Index Terms-Composite kernels discriminant analysis (CKDA), Gaussian weighted local mean operator (GWLM), spatial information, spectral information.

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Peculiarities of Hyperspectral Image Lossy Compression for Sub-band Groups

Zemliachenko¹,

Єремеєв²,

Vozel³

2019

2019 IEEE 2nd Ukraine Conference on Electrical and Computer Engineering (UKRCON)

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Foreword to the Special Issue on Hyperspectral Image and Signal Processing

Abstract: Foreword to the special issue on hyperspectral image and signal processing.

Cited by 3 publications

References 60 publications

Wavelet Transformation of Functional Data for Hyperspectral Image Classification

Wavelet Transformation of Functional Data for Hyperspectral Image Classification

Hyperspectral Image Classification Using Spectral–Spatial Composite Kernels Discriminant Analysis

Peculiarities of Hyperspectral Image Lossy Compression for Sub-band Groups

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