Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 11-16, 2016
https://doi.org/10.5194/isprs-archives-XLI-B7-11-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
17 Jun 2016
IMAGE QUATY ASSESSMENT FOR VHR REMOTE SENSING IMAGE CLASSIFICATION
Zhipeng Li1,2, Li Shen1,2, and Linmei Wu1,2 1State-province Joint Engineering Laboratory of Spatial Information Technology for High-speed Railway Safety, Southwest Jiaotong University, Chengdu, 611756 , P. R. China
2Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, 611756 , P. R. China
Keywords: Image quality assessment, image texture analysis, Image classification Abstract. The data from remote sensing images are widely used for characterizing land use and land cover at present. With the increasing availability of very high resolution (VHR) remote sensing images, the remote sensing image classification becomes more and more important for information extraction. The VHR remote sensing images are rich in details, but high within-class variance as well as low between-class variance make the classification of ground cover a difficult task. What’s more, some related studies show that the quality of VHR remote sensing images also has a great influence on the ability of the automatic image classification. Therefore, the research that how to select the appropriate VHR remote sensing images to meet the application of classification is of great significance. In this context, the factors of VHR remote sensing image classification ability are discussed and some indices are selected for describing the image quality and the image classification ability objectively. Then, we explore the relationship of the indices of image quality and image classification ability under a specific classification framework. The results of the experiments show that these image quality indices are not effective for indicating the image classification ability directly. However, according to the image quality metrics, we can still propose some suggestion for the application of classification.
Conference paper (PDF, 1432 KB)


Citation: Li, Z., Shen, L., and Wu, L.: IMAGE QUATY ASSESSMENT FOR VHR REMOTE SENSING IMAGE CLASSIFICATION, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 11-16, https://doi.org/10.5194/isprs-archives-XLI-B7-11-2016, 2016.

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