Abstract
Joint sparse representation (JSR) is a commonly used classifier that recognizes different objects with core features extracted from images. However, the generalization ability is weak for the traditional linear kernel, and the objects with similar feature values associated with different categories are not sufficiently distinguished especially for a hyperspectral image (HSI). In this article, an HSI classification technique based on the weight wavelet kernel JSR ensemble model and the β-whale optimization algorithm is proposed to conduct pixel-level classification, where the wavelet function is acted as the kernel of JSR. Moreover, ensemble learning is used to determine the category label of each sample by comprehensive decision of some subclassifiers, and the β function is utilized to enhance the exploration phase of the whale optimization algorithm and obtain the optimal weight of subclassifiers. Experimental results indicate that the performance of the proposed HSI classification method is better than that of other newly proposed and corresponding approaches, the misclassification and classified noise are eliminated to some extent, and the overall classification accuracy reaches 95% for all HSIs.
Highlights
I N RECENT years, hyperspectral remote sensing sensors have been applied to collect images with enough spectralManuscript received November 6, 2020; revised December 16, 2020, January 10, 2021, and January 21, 2021; accepted January 28, 2021
To evaluate the performance of the proposed hyperspectral image (HSI) classification technique based on the wavelet kernel JSR ensemble (W2 JSRE) model and the β-whale optimization algorithm (WOA), three public collected HSIs and two measured airborne HSIs are used in the experiments
An HSI classification technique based on the W2 JSRE model and the β-WOA is proposed
Summary
I N RECENT years, hyperspectral remote sensing sensors have been applied to collect images with enough spectralManuscript received November 6, 2020; revised December 16, 2020, January 10, 2021, and January 21, 2021; accepted January 28, 2021. A hyperspectral image (HSI) has been considered as an applicable tool for Earth observation because of its ability to obtain independent and continuous bands, analyze information from visible to nearinfrared wavelength ranges, and supply multiple features from the fixed wavelength. It provides abundant spectral information and has a huge potential for the interpretation of different ground objects [2], [3]. The analysis of HSI has become a subject of research interest in remote sensing, which has been applied in a series of fields such as quantitative analysis [4], environmental monitoring [5], and land-cover mapping [6]. Image classification is a significant step in identifying object types on the Earth’s surface, and HSI classification aims to distinguish each sample into a discrete group of specific category labels [7], [8]
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