Hyperspectral Remote Sensing Images Feature Extraction Based on Spectral Fractional Differentiation
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Published:2023-06-01
Issue:11
Volume:15
Page:2879
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ISSN:2072-4292
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Container-title:Remote Sensing
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language:en
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Short-container-title:Remote Sensing
Author:
Liu Jing1ORCID, Li Yang1ORCID, Zhao Feng2ORCID, Liu Yi3ORCID
Affiliation:
1. School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China 2. School of Computer Science and Technology, Shandong Technology and Business University, Yantai 264005, China 3. School of Electronic Engineering, Xidian University, Xi’an 710071, China
Abstract
To extract effective features for the terrain classification of hyperspectral remote-sensing images (HRSIs), a spectral fractional-differentiation (SFD) feature of HRSIs is presented, and a criterion for selecting the fractional-differentiation order is also proposed based on maximizing data separability. The minimum distance (MD) classifier, support vector machine (SVM) classifier, K-nearest neighbor (K-NN) classifier, and logistic regression (LR) classifier are used to verify the effectiveness of the proposed SFD feature, respectively. The obtained SFD feature is sent to the full connected network (FCN) and 1-dimensionality convolutional neural network (1DCNN) for deep-feature extraction and classification, and the SFD-Spa feature cube containing spatial information is sent to the 3-dimensionality convolutional neural network (3DCNN) for deep-feature extraction and classification. The SFD-Spa feature after performing the principal component analysis (PCA) on spectral pixels is directly connected with the first principal component of the original data and sent to 3DCNNPCA and hybrid spectral net (HybridSN) models to extract deep features. Experiments on four real HRSIs using four traditional classifiers and five network models have shown that the extracted SFD feature can effectively improve the accuracy of terrain classification, and sending SFD feature to deep-learning environments can further improve the accuracy of terrain classification for HRSIs, especially in the case of small-size training samples.
Funder
National Natural Science Foundation of China Natural Science Foundation of Shaanxi Province of China
Subject
General Earth and Planetary Sciences
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