||Because of the development of remote sensing technology, space-borne images have improved their resolution in the past decades. Remote sensing has many applications, such as land management, change detection, water resource and so on. Moreover, Taiwan is in the divergent boundaries. There are many earthquakes every year and some cause serious landslide. Change detection by remote sensing is an efficient approach to detect terrain features’ change and for environmental protection. We compare to images of the same region but collected at different times to detect the changes. However, images are diverse not only on ground condition, but also atmospheric conditions. How to increase the recognition rate between images is an important issue.|
In this thesis, we reduce the atmospheric conditions based on color transform algorithm without using complex atmospheric parameters. Because of its simplicity, relative correction is commonly used recently. We first apply image fusion to enhance the resolution and match our images. Then, we adopt color transform and Whitening/Dewhitening method for correction based on the statistic of images. For applying the result in change detection, we use univariate image differencing to detect the difference between the images. Finally, quantitative analysis is conducted for performance comparison.
The image scenes used for experiments are in Central Cross-Island Highway of Taiwan during 921 chi-chi earthquake and Cao-ling area in Yunlin county. From the result, our method can reduce the atmospheric disturbances on the satellite images. It provides a procedural for correction algorithms and yields good quality and performance.
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