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Proceedings Paper

Research on classification of high-resolution remote sensing image
Author(s): Xiwang Zhang; Bingfang Wu; Qiangzi Li
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Paper Abstract

High-resolution remote sensing image is usually considered that its pixel size is less than 10 meters. Traditional classification methods based on pixels are not fit for the classification of this kind of image because this kind of image has higher spatial resolution and more local heterogeneity compared to the low-resolution remote sensing image data. Object-oriented image classification method provides a good technique to solve this problem. This method segments image to create homogeneous regions or image objects through region merging or boundary detection algorithms. Objects possess more features such as geometric and structure characteristics besides spectral characteristics than pixels. So it is important to select appropriate characterstics in classification. Class-Related features, landscape pattern metrics, geometric attributes of objects, spatial information are very useful characteristics. The paper will pay more attention to the selection and integrative utilization of these features and spectral characteristics, and give several examples to show their performance. (1) If We want to extract a kind of feature which has similar spectral characteristic as the other feature but has a certain positional relationship with a specific feature, at this time, Class-Related features will be very efficacious. (2) Both river and pounds have also similar spectral characteristic, but has different geometric characteristic in the landscape pattern metrics. Synthetically use of the landscape pattern metrics and spectral characteristic will wok well. (3) In the same segmentation scale, the objects from the region with more homogeneity will be bigger than other objects from the region with more heterogeneity. So, the area and spectral characteristic can be used in classification. The results show a better accuracy. The selection and integrative utilization of features of objects were very important in achieving these high accuracies.

Paper Details

Date Published: 15 November 2007
PDF: 7 pages
Proc. SPIE 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, 67862Z (15 November 2007); doi: 10.1117/12.750961
Show Author Affiliations
Xiwang Zhang, Institute of Remote Sensing Applications (China)
Bingfang Wu, Institute of Remote Sensing Applications (China)
Qiangzi Li, Institute of Remote Sensing Applications (China)


Published in SPIE Proceedings Vol. 6786:
MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition

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