Bi-Temporal change detection of high-resolution images by referencing time series medium-resolution images

Seasonal changes usually exist and cause false alarms in the bi-temporal change detection from high-resolution remote sensing images. It is difficult to remove these false alarms only using bi-temporal images for traditional change detection methods. A change detection method is proposed to remove seasonal false alarms in bi-temporal change detection by introducing time series information of medium-resolution remote sensing images. First, the mid-resolution time series results are mapped to the ground objects obtained by multiscale segmentation of high-resolution remote sensing images. Second, set the thresholds for the proportion of each category of pixels in the object to obtain high-resolution time series results. Finally, the high-resolution change detection results are optimized by the improved high-resolution time series results. Experimental results show that this method can optimize the results of high-resolution change detection, and the accuracy of this method was improved by at least 0.23 than that of traditional change detection by reducing seasonal errors. The proposed method was an effective change detection approach for high-resolution images to reduce detection errors due to seasonal differences.