Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 897-900, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-897-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 897-900, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-897-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

A NOVEL DEEP CONVOLUTIONAL NEURAL NETWORK FOR SPECTRAL–SPATIAL CLASSIFICATION OF HYPERSPECTRAL DATA

N. Li1, C. Wang1, H. Zhao1, X. Gong1, and D. Wang2 N. Li et al.
  • 1School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing, China
  • 2China Geological Survey, Beijing, China

Keywords: Hyperspectral Data, Classification, Three-dimensional Convolution, Deep CNN, Feature Extraction

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.