The protection of agricultural land was needed to make sure the food production stability. In Taiwan, the total area of agricultural land was gradually lost due to the construction project of impervious area inside the agricultural land. In this study, an object-based approach for impervious area extraction was developed to protect the agricultural land. The image is first segmented using algorithms available in eCognition Developer software. The vegetation and water are then eliminated from the segmented regions to generate impervious area candidates. Finally, the rules integrate spectral properties and contextual relationship with its neighbor are employed to extract the impervious area. A total of seven high-resolution Pleiades images covering Kaohsiung city, Taiwan was used. The proposed method was then applied to the seven satellite images. The image-tiling procedure was used to process the seven satellite images systematically. The satellite image often contains an area covered by the cloud and leads to losing the ground target information. The seven images used were overlapped to fill in that missing object caused by the cloud. The post-processing was then employed to remove the misclassified caused by cloud. The accuracy assessment was conducted on the overlapping area of the seven satellite images. The F1-score value shows not significantly different between two overlap subsets images. The similarity test was then conducted to evaluate between two overlap objects. A high number of samples depicts the extraction result of the two overlap objects is similar.

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