Novel Higher-Order Clique Conditional Random Field to Unsupervised Change Detection for Remote Sensing Images

Change detection (CD) is one of the most important topics in remote sensing. In this paper, we propose a novel higher-order clique conditional random field model to unsupervised CD for remote sensing images (termed (HOCRF)-R-2), by defining a higher-order clique potential. The clique potential, constructed based on a well-designed higher-order clique of image objects, takes the interaction between the neighboring objects in both feature and location spaces into account. (HOCRF)-R-2 consists of five principle steps: (1) Two difference images with complementary change information are produced by change vector analysis and using the spectral correlation mapper, which describe changes from the perspective of the vector magnitude and angle, respectively. (2) The fuzzy partition matrix of each difference image is calculated by fuzzy clustering, and the fused partition matrix is obtained by fusing the calculated partition matrices with evidence theory. (3) An object-level map is created by segmenting the difference images with an adaptive morphological reconstruction based watershed algorithm. (4) The energy function of the proposed (HOCRF)-R-2, composed of unary, pairwise, and higher-order clique potentials, is computed based on the difference images, the fusion partition matrix, and the object-level map. (5) The energy function is minimized by the graph cut algorithm to achieve the binary CD map. The proposed (HOCRF)-R-2 CD approach combines the complementary change information extracted from the perspectives of vector magnitude and angle, and synthetically exploits the pixel-level and object-level spatial correlation of images. The main contributions of this article include: (1) proposing the idea of using the interaction between neighboring objects in both feature and location spaces to enhance the CD performance; and (2) presenting a method to construct a higher-order clique of objects, developing a higher-order clique potential function, and proposing a novel CD method (HOCRF)-R-2. In the experiments on three real remote sensing images, the Kappa coefficient/overall accuracy values of the proposed (HOCRF)-R-2 are 0.9655/0.9967, 0.9518/0.9910, and 0.7845/0.9651, respectively, which are superior to some state-of-the-art CD methods. The experimental results confirm the effectiveness of the proposed method.