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- Title
A NOVEL DEEP CONVOLUTIONAL NEURAL NETWORK FOR SPECTRAL-SPATIAL CLASSIFICATION OF HYPERSPECTRAL DATA.
- Authors
Na Li; Chengguo Wang; Huijie Zhao; Xuemei Gong; Daming Wang
- Abstract
Spatial and spectral information are obtained simultaneously by hyperspectral remote sensing. Joint extraction of these information of hyperspectral image is one of most import methods for hyperspectral image classification. In this paper, a novel deep convolutional neural network (CNN) is proposed, which extracts spectral-spatial information of hyperspectral images correctly. The proposed model not only learns sufficient knowledge from the limited number of samples, but also has powerful generalization ability. The proposed framework based on three-dimensional convolution can extract spectral-spatial features of labeled samples effectively. Though CNN has shown its robustness to distortion, it cannot extract features of different scales through the traditional pooling layer that only have one size of pooling window. Hence, spatial pyramid pooling (SPP) is introduced into three-dimensional local convolutional filters for hyperspectral classification. Experimental results with a widely used hyperspectral remote sensing dataset show that the proposed model provides competitive performance.
- Subjects
SIGNAL convolution; ARTIFICIAL neural networks; HYPERSPECTRAL imaging systems
- Publication
International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 2018, Vol 42, Issue 3, p897
- ISSN
1682-1750
- Publication type
Article
- DOI
10.5194/isprs-archives-XLII-3-897-2018