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Deep Kernel Extreme-Learning Machine for the Spectral–Spatial Classification of Hyperspectral Imagery
Summary
This paper describes a deep neural network method combining kernel extreme-learning machines with spectral-spatial analysis for classifying hyperspectral remote sensing images. Hyperspectral imaging is also being developed as a tool for detecting and identifying microplastics in environmental samples.
Extreme-learning machines (ELM) have attracted significant attention in hyperspectral image classification due to their extremely fast and simple training structure. However, their shallow architecture may not be capable of further improving classification accuracy. Recently, deep-learning-based algorithms have focused on deep feature extraction. In this paper, a deep neural network-based kernel extreme-learning machine (KELM) is proposed. Furthermore, an excellent spatial guided filter with first-principal component (GFFPC) is also proposed for spatial feature enhancement. Consequently, a new classification framework derived from the deep KELM network and GFFPC is presented to generate deep spectral and spatial features. Experimental results demonstrate that the proposed framework outperforms some state-of-the-art algorithms with very low cost, which can be used for real-time processes.
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