Iterative constrained energy minimization convolutional neural network for hyperspectral image classification
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Type of Work9 pages
conference papers and proceedings
Citation of Original PublicationBai Xue, Xiaodi Shang, Shengwei Zhong, Peter F. Hu, and Chein-I Chang "Iterative constrained energy minimization convolutional neural network for hyperspectral image classification", Proc. SPIE 10986, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV, 109861L (14 May 2019); https://doi.org/10.1117/12.2519046
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SubjectsHyperspectral image classification
iterative constrained energy minimization (ICEM)
convolutional neural network (CNN)
band selection and nonlinear expansion (BSNE)
In hyperspectral image classification, how to jointly take care of spectral and spatial information received considerable interest lately, and many spectral-spatial classification approaches have been proposed. Unlike spectral-spatial classifications which are developed from traditional aspect, iterative constrained energy minimization (ICEM) and iterative target-constrained interference-minimized classifier (ITCIMC) approaches are developed from subpixel detection and mixed pixel classification point of view, and generally performs better than existing spectral-spatial approaches in terms of several measurements, such as accuracy rate and precision rate. Recently, convolutional neural networks (CNNs) have been successfully applied to visual imagery classification and have received great attention in hyperspectral image classification, due to the outstanding ability of CNN to capture spatial information. This paper extends ICEM to iterative constrained energy minimization convolution neural network approach for hyperspectral image classification. In order to capture spatial information, instead of Gaussian filter, CNN is utilized to generate binary pixelwise classification map for constrained energy minimization (CEM) detection results, and CNN classification map is feedbacked into hyperspectral bands, and then CEM detection is reprocessed in an iteration manner. Since CNN can reduce the performance of precision rate, a background recovery procedure is designed, to recover background detection map from CEM detection map and add it into CEM result as a new detection map.