Paper
30 October 2009 Land-cover classification and change analysis of Qinghai-Tibet Highway by remote sensing data
Jubai An, Ling Zhou, Zifeng Yang, Hongcai Zhang
Author Affiliations +
Proceedings Volume 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications; 749812 (2009) https://doi.org/10.1117/12.832877
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China
Abstract
Remote sensing data cover large areas and can be acquired in a regular repeatable manner. Automatic land-cover classification in satellite images is an important topic and has applied in remote sensing widely. In this paper, we consider Landsat5 Thematic Mapper (TM) data of the Qinghai-Tibet highway of 1986 and 1994 to analyze the changes of land-cover. Statistics and artificial intelligence method are combined to improve the classification precision. And the classification result can provide quantitative data for road environment issue, road location selection, and landscape design. In this paper, Principal Component Analysis (PCA) is applied to character the main information of TM land-cove image. Then two neural network models are used to classify the TM image: Back-Propagation Neural Network (BPNN) and Self-organizing feature map (SOFM) neural network. BP neural network is widely used. Contingency matrix is used to evaluate the classification precision. By comparing classification accuracy and Kappa quotient, conclusion is drawn that the classification accuracy of SOFM is higher than BP and MLC and the classification ability of BP is not as good as MLC. Overall accuracy of SOFM is 94.0%, Kappa is 0.9114, and overall accuracy is 14.9% and 9.8% higher than BP and MLC. So SOFM is used to classify image of 1986. In the end the land-cover changes of two year are analyzed.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jubai An, Ling Zhou, Zifeng Yang, and Hongcai Zhang "Land-cover classification and change analysis of Qinghai-Tibet Highway by remote sensing data", Proc. SPIE 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications, 749812 (30 October 2009); https://doi.org/10.1117/12.832877
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KEYWORDS
Neural networks

Remote sensing

Image classification

Neurons

Principal component analysis

Earth observing sensors

Roads

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