Dr. Xiwang Zhang Profile

Dr. Xiwang Zhang

at Institute of Remote Sensing and Digital Earth

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Proceedings Article | 15 November 2007 Paper

Research on classification of high-resolution remote sensing image

Xiwang Zhang, Bingfang Wu, Qiangzi Li

Proceedings Volume 6786, 67862Z (2007) https://doi.org/10.1117/12.750961

KEYWORDS: Image segmentation, Image classification, Image fusion, Remote sensing, Spatial resolution, Image resolution, Feature extraction, Classification systems, Reflectivity, Image processing algorithms and systems

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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.

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