Detecting changes in high-resolution satellite coastal imagery using an image object detection approach

This article presents a spatial contrast-enhanced image object-based change detection approach (SICA) to identify changed areas using shape differences between bi-temporal high-resolution satellite images. Each image was segmented and intrinsic image objects were extracted from their hierarchic candidates by the proposed image object detection approach (IODA). Then, the dominant image object (DIO) presentation was labelled from the results of optimal segmentation. Comparing the form and the distribution of bi-temporal DIOs by using the raster overlay function, ground objects were recognized as being spatially changed where the corresponding image objects were detected as merged or split into geometric shapes. The result of typical spectrum-based change detection between two images was enhanced by using changed spatial information of image objects. The result showed that the change detection accuracies of the pixels with both attribute and shape changes were improved from 84% to 94% for the strong attribute pixel, and from 36% to 81% for the weak attribute pixel in study area. The proposed approach worked well on high-resolution satellite coastal images.