TY - JOUR
AU - 
AB - remote sensing Article Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network 1 , 1 2 Ying Li *, Haokui Zhang and Qiang Shen School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, Shaanxi, China; hkzhang1991@mail.nwpu.edu.cn Department of Computer Science, Institute of Mathematics, Physics and Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK; qqs@aber.ac.uk * Correspondence: lybyp@nwpu.edu.cn; Tel.: +86-138-9143-3893 Academic Editors: Gonzalo Pajares Martinsanz and Prasad S. Thenkabail Received: 17 September 2016; Accepted: 9 January 2017; Published: 13 January 2017 Abstract: Recent research has shown that using spectral–spatial information can considerably improve the performance of hyperspectral image (HSI) classification. HSI data is typically presented in the format of 3D cubes. Thus, 3D spatial filtering naturally offers a simple and effective method for simultaneously extracting the spectral–spatial features within such images. In this paper, a 3D convolutional neural network (3D-CNN) framework is proposed for accurate HSI classification. The proposed method views the HSI cube data altogether without relying on any preprocessing or post-processing, extracting the deep spectral–spatial-combined features effectively. In addition, it requires fewer parameters than other deep learning-based methods. Thus, the model is lighter, less likely to over-fit, and easier to train. For comparison and validation, we test 
TI - Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network
JF - Remote Sensing
DO - 10.3390/rs9010067
DA - 2017-01-13
UR - https://www.deepdyve.com/lp/unpaywall/spectral-spatial-classification-of-hyperspectral-imagery-with-3d-SSdSP9NNsR
DP - DeepDyve
ER -