Remote sensing images often suffer low contrast, and the efficiency and robustness of contrast enhancement for remote sensing images is still a challenge. To meet with the requirements of applications, Liu et al recently proposed a self-adaptive contrast enhancement method (HCTLS) based on the histogram compacting transform (HCT). In this method, some gray levels on which the frequency is smaller than a certain reference, will merged into their adjacent levels for a compact level distribution. However, if the merged levels whose corresponding pixels in some connected regions, local contrast of these connected regions will decrease, even disappear. In this paper, an improved enhancement method (DPHCT) for remote sensing image based on the HCTLS is presented for preserving more the local detail and contrast. Firstly, extracting the connected regions from the enhanced result by HCT where the local contrast is decreased or disappeared. These connected regions are decomposed into the inner regions and the boundary regions adaptively. Then, construct pixel values by using the unified brightness function to maintain the contrast for the connected regions inside. At the same time eliminate stitching lines by using a weighted fusion spliced algorithm to eliminate the problem of borders outstanding in result of intensity roughness. Finally, the image is normalized into [0, 255] by linear stretch. Experimental results indicate that the proposed algorithm not only can enhance the global contrast but also can preserve local contrast and details. ? 2018 IEEE.