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Divide-and-Conquer Dual-Architecture Convolutional Neural Network for Classification of Hyperspectral Images

Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an 710071, China
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Remote Sens. 2019, 11(5), 484; https://doi.org/10.3390/rs11050484
Received: 25 January 2019 / Revised: 19 February 2019 / Accepted: 22 February 2019 / Published: 27 February 2019
(This article belongs to the Special Issue Robust Multispectral/Hyperspectral Image Analysis and Classification)
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Abstract

Convolutional neural network (CNN) is well-known for its powerful capability on image classification. In hyperspectral images (HSIs), fixed-size spatial window is generally used as the input of CNN for pixel-wise classification. However, single fixed-size spatial architecture hinders the excellent performance of CNN due to the neglect of various land-cover distributions in HSIs. Moreover, insufficient samples in HSIs may cause the overfitting problem. To address these problems, a novel divide-and-conquer dual-architecture CNN (DDCNN) method is proposed for HSI classification. In DDCNN, a novel regional division strategy based on local and non-local decisions is devised to distinguish homogeneous and heterogeneous regions. Then, for homogeneous regions, a multi-scale CNN architecture with larger spatial window inputs is constructed to learn joint spectral-spatial features. For heterogeneous regions, a fine-grained CNN architecture with smaller spatial window inputs is constructed to learn hierarchical spectral features. Moreover, to alleviate the problem of insufficient training samples, unlabeled samples with high confidences are pre-labeled under adaptively spatial constraint. Experimental results on HSIs demonstrate that the proposed method provides encouraging classification performance, especially region uniformity and edge preservation with limited training samples. View Full-Text
Keywords: Hyperspectral image classification; divide-and-conquer; dual-architecture convolutional neural network; homogeneous and heterogeneous regions; superpixel segmentation Hyperspectral image classification; divide-and-conquer; dual-architecture convolutional neural network; homogeneous and heterogeneous regions; superpixel segmentation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Feng, J.; Wang, L.; Yu, H.; Jiao, L.; Zhang, X. Divide-and-Conquer Dual-Architecture Convolutional Neural Network for Classification of Hyperspectral Images. Remote Sens. 2019, 11, 484.

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