Exploring Kernel Based Spatial Context for CNN Based Hyperspectral Image Classification

Jingyu Ji, Shaohui Mei, Xiao Liu, Xu Li, Shan Zeng, Zhiyong Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Convolutional Neural Network (CNN) has received remarkable achievements in hyperspectral image (HSI) classification. However, how to effectively implement spatial context that has been demonstrated to be crucial for classification of HSI is still an open issue. Current CNNs for hyperspectral classification are restricted into a small scale due to small-scale input and limited training samples. Therefore, in this paper, two different ways are proposed to implement both spatial context and spectral signature into CNN based classification of HSI: 1). fixed kernels in which weights are determined by prior information, i.e., mean, mean and standard deviation of pixels in a spatial neighborhood, and Gaussian kernel; 2). learnable kernels in which weights are learned from training samples, i.e., 2D learnable kernel, 3D convolutional kernel, and 2-Layer kernel. In the successive CNN for classification of HSI, dropout and batch normalization are also used to improve the classification performance of hyperspectral images under small sample conditions. Experiments on two- known HSIs demonstrating that, in the considered small-scale CNN, fixed kernels are more effective than learnable kernels to explore spatial information for classification of HSIs, especially for the case with small number of training samples.

Original languageEnglish
Title of host publicationDICTA 2017 - 2017 International Conference on Digital Image Computing
Subtitle of host publicationTechniques and Applications
EditorsYi Guo, Manzur Murshed, Zhiyong Wang, David Dagan Feng, Hongdong Li, Weidong Tom Cai, Junbin Gao
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-7
Number of pages7
ISBN (Electronic)9781538628393
DOIs
StatePublished - 19 Dec 2017
Event2017 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2017 - Sydney, Australia
Duration: 29 Nov 20171 Dec 2017

Publication series

NameDICTA 2017 - 2017 International Conference on Digital Image Computing: Techniques and Applications
Volume2017-December

Conference

Conference2017 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2017
Country/TerritoryAustralia
CitySydney
Period29/11/171/12/17

Fingerprint

Dive into the research topics of 'Exploring Kernel Based Spatial Context for CNN Based Hyperspectral Image Classification'. Together they form a unique fingerprint.

Cite this