Share Email Print
cover

Proceedings Paper

Some new classification methods for hyperspectral remote sensing
Author(s): Pei-jun Du; Yun-hao Chen; Simon Jones; Jelle G. Ferwerda; Zhi-jun Chen; Hua-peng Zhang; Kun Tan; Zuo-xia Yin
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Hyperspectral Remote Sensing (HRS) is one of the most significant recent achievements of Earth Observation Technology. Classification is the most commonly employed processing methodology. In this paper three new hyperspectral RS image classification methods are analyzed. These methods are: Object-oriented FIRS image classification, HRS image classification based on information fusion and HSRS image classification by Back Propagation Neural Network (BPNN). OMIS FIRS image is used as the example data. Object-oriented techniques have gained popularity for RS image classification in recent years. In such method, image segmentation is used to extract the regions from the pixel information based on homogeneity criteria at first, and spectral parameters like mean vector, texture, NDVI and spatial/shape parameters like aspect ratio, convexity, solidity, roundness and orientation for each region are calculated, finally classification of the image using the region feature vectors and also using suitable classifiers such as artificial neural network (ANN). It proves that object-oriented methods can improve classification accuracy since they utilize information and features both from the point and the neighborhood, and the processing unit is a polygon (in which all pixels are homogeneous and belong to the class). HRS image classification based on information fusion, divides all bands of the image into different groups initially, and extracts features from every group according to the properties of each group. Three levels of information fusion: data level fusion, feature level fusion and decision level fusion are used to HRS image classification. Artificial Neural Network (ANN) can perform well in RS image classification. In order to promote the advances of ANN used for HIRS image classification, Back Propagation Neural Network (BPNN), the most commonly used neural network, is used to HRS image classification.

Paper Details

Date Published: 28 October 2006
PDF: 11 pages
Proc. SPIE 6419, Geoinformatics 2006: Remotely Sensed Data and Information, 641929 (28 October 2006); doi: 10.1117/12.713419
Show Author Affiliations
Pei-jun Du, China Univ. of Mining and Technology (China)
Yun-hao Chen, Beijing Normal Univ. (China)
Simon Jones, Royal Melbourne Institute of Technology Univ. (Australia)
Jelle G. Ferwerda, Royal Melbourne Institute of Technology Univ. (Australia)
Zhi-jun Chen, Beijing Normal Univ. (China)
Hua-peng Zhang, China Univ. of Mining and Technology (China)
Kun Tan, China Univ. of Mining and Technology (China)
Zuo-xia Yin, China Univ. of Mining and Technology (China)


Published in SPIE Proceedings Vol. 6419:
Geoinformatics 2006: Remotely Sensed Data and Information
Liangpei Zhang; Xiaoling Chen, Editor(s)

© SPIE. Terms of Use
Back to Top