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浙江大学学报(工学版)
J4  2013, Vol. 47 Issue (8): 1508-1516    DOI: 10.3785/j.issn.1008-973X.2013.08.027
    
Land use classification using ZY1-“02C” remote sensing images
MA Li-gang1, ZHANG Le-ping1, DENG Jing-song1, WANG Ya-jie2, WANG Ke1
1. Institute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou 310029, China; 2. The 2nd Surveying and Mapping Institute of Zhejiang, Hangzhou 310012, China
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Abstract  

Land use of Hang Zhou city was classified from ZY-1 02C imagery in an neutral network approach using spectral, texture, and nDSM (Normalized Digital Surface Model) features. Texture features are selected from the combined use of semi-variance function and Z test. Construction and bare land were separated according to texture complexity distinction. Shadow and water were identified with the support of nDSM . Accuracy assessment indicate that addition of image textures can improve overall classification accuracy by 4% in comparison with classification using original bands solely. Furthermore, inclusion of elevation data can increase overall accuracy by 10% to 82.78%, which demonstrates the effectiveness of proposed method in the classification of 02C data. Classification result is acceptable.

Published: 01 August 2013
CLC:  TP 751.1  
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Cite this article:

MA Li-gang, ZHANG Le-ping, DENG Jing-song, WANG Ya-jie, WANG Ke. Land use classification using ZY1-“02C” remote sensing images. J4, 2013, 47(8): 1508-1516.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2013.08.027     OR     http://www.zjujournals.com/eng/Y2013/V47/I8/1508


资源一号“02C”遥感影像土地利用分类

以杭州市主城区为试验区,针对建设用地与裸地空间纹理的复杂度和水体与阴影高程差异,拟采用半方差函数与Z检验结合选出的图像纹理结合高程信息等分量实现神经网络分类.结果表明,与单纯使用光谱信息相比,图像纹理的引入使总体分类精度提高约4%,加入高程信息则可以使总体分类精度提高约10%,达到82.75%,表明该方法可以应用于新数据的分类并得到相对满意的结果.

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