Change Detection in Optical Satellite Images Based on Local Binary Similarity Pattern Technique

In this letter, a binary descriptor-based approach is proposed to provide change detection in bitemporal optical satellite images. Multitemporal satellite images suffer from illumination variation. In order to tackle this problem, a variant of a local binary similarity pattern (LBSP) descriptor, which has a good resistance to illumination variation, is proposed. The proposed technique consists of two steps: binary feature vector creation and generation of binary change map using Hamming distance as a similarity metric. To get the binary feature vectors, inter-LBSP, which utilizes the region information across images, is applied on both images. Before applying LBSP, both images are partitioned into overlapping patches. This letter proposes two methods taking different combinations of patches at each pixel position. First one uses the center of one image and neighborhood information of two images, whereas the second one uses the center of two images and neighborhood information of one image. Unlike the conventional LBSP technique, a new threshold calculation technique is used for the generation of binary feature vectors. Experiments are conducted on three data sets acquired by the Landsat satellite, and results show that the proposed technique performs better compared with the earlier reported techniques.