Use of object-based Siamese neural network to build change detection from very high resolution remote-sensing images

Building change detection is essential to many applications,such as monitoring of urban areas,land use management,and illegal building detection.It has been seen as an effective means to detect building changes from remote-sensing images. This paper proposes an object-based Siamese neural network,labeled as Obj-SiamNet,to detect building changes from high-resolution remote-sensing images.We combine the advantages of object-based image analysis methods and Siamese neural networks to improve the geometric accuracies of detected boundaries.Moreover,we implement the Obj-SiamNet at multiple segmentation levels and automatically construct a set of fuzzy measures to fuse the obtained results at multi-levels.Furthermore,we use generative adversarial methods to generate target-like training samples from publicly available datasets and construct a relatively sufficient training dataset for the Obj-SiamNet model.Finally,we apply the proposed method into three high-resolution remote-sensing datasets,i.e.,a GF-2 image-pair in Fuzhou City,and a GF2 image pair in Pucheng County,and a GF-2-GF-7 image pair in Quanzhou City.We also compare the proposed method with three other existing ones,namely,STANet,ChangeNet,and Siam-NestedUNet. Experimental results show that the proposed method performs better than the other three in terms of detection accuracy.(1)Compared with the detection results from single-scale segmentation,the detection results from multi-scale increases the recall rate by up to 32％,the F1-Score increases by up to 25％,and the Global Total Classification error(GTC)decreases by up to 7％.(2)When the number of available samples is limited,the adopted Generative Adversarial Network(GAN)is able to generate effective target-like samples for diverting samples.Compared with the detection without using GAN-generated samples,the proposed detection increases the recall rate by up to 16％,increases the Fl-Score by up to 14％,and decreases GTC by 9％.(3)Compared with other change-detection methods,the proposed method improves the detection accuracies significantly,i.e.,the Fl-Score increases by up to 23％,and GTC decreases by up to 9％.Moreover,the boundaries of the detected changes by the proposed method have a high consistency with that of ground truth. We conclude that the proposed Obj-SiamNet method has a high potential for building change detection from high-resolution remote-sensing images.