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Identifying exposed sewage outlets through visual interpretation of UAV images is a time-consuming and inefficient process. This article proposes a method for enhancing the efficacy of investigations by detecting changes in remote sensing images. The aim is to aid in the preliminary identification of potential sewage outlets. Firstly, the surf algorithm is employed to register the UAV image with the remote sensing image that was utilized in the previous investigation. Then, we employed gamma correction and Histogram matching techniques to adjust the brightness and color histograms of the two images. Subsequently, the two images are classified separately using a supervised classification technique to distinguish segments of man-made objects from the natural background. Finally, the changes in classification between the two images were identified. The area where the natural background transitions into an artificial structure was combined with the previously identified locations of sewage outlets, as the key areas for further visual interpretations. The practical results in the Shichuan River Basin demonstrate the potential for enhancing the UAV-based survey technology for ecological inspections, and improving the efficiency of river drainage outlet investigation through the analysis of UAV images.
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