Subpixel Change Detection of Multitemporal Remote Sensed Images Using Variability of Endmembers

Due to the existence of mixed pixels in a remote sensed image, traditional change detection (CD) methods at “full-pixel level” are often unable to provide detailed changed information effectively. A subpixel change detection (SCD) technique can deal with this issue with two steps: soft classification is applied to derive proportional differences from coarse multitemporal images, and then a sharpened thematic map with fine spatial resolution is generated based on subpixel mapping. However, changes in endmember combination within pixels are ignored, which can result in flawed differences and degraded accuracy of SCD. The aim of this letter is to present a new SCD algorithm using variability of endmembers (SCD_VE), where a simple but effective model is proposed to take into consideration the real change of endmember combination. In order to evaluate the performance of the new algorithm, experiment is conducted on simulated images. Experimental results demonstrated that the proposed SCD_VE offers better performance than traditional SCD methods in providing more detailed CD map.

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