SMALL OBJECT CHANGE DETECTION BASED ON MULTITASK SIAMESE NETWORK

This paper presents a small object, represented by approximately ten pixels in an image, change detection method based on multitask Siamese network for multi-temporal SAR images. In our proposed method, not only change detection task but also object classification task is introduced to the network. The classification task is expected to enhance the performance of change detection by providing semantic information of changes and to focus attention of the network towards the target small object class. We tested the proposed method for a real-world application of car parking lot monitoring with 1-meter resolution TerraSAR-X images. Experimental results show that the f-measure of change class is improved by more than 7% over conventional methods based on post-classification, PCA+K-means and Siamese network. Furthermore, car-to-car type change is detected by the proposed method with 25% higher accuracy over the method without the classification task.