A novel retrieval method for remote sensing image based on statistical model

With the increasing number of high-resolution remote sensing (HRRS) image technologies, there is an interest in seeking a way to retrieve images efficiently. In order to describe the images with abundant texture information more concisely and accurately, we propose a novel remote sensing image retrieval approach based on the statistical features of non-subsampled shearlet transform (NSST) coefficients, according to which we set up a model using Bessel K form (BKF). First, the remote sensing (RS) image is decomposed into several subbands of frequency and orientation using the non-subsampled shearlet transform. Then, we use the Bessel K distribution model is utilized to describe the coefficients of NSST high-frequency subband. Next, the BKF parameters are selected to serve as the texture feature to represent the characteristics of image, namely BKF statistical model feature (BSMF), and the feature vector of each image is created by combination with parameters at each high-pass subband. Both the experiment and theory indicate that the BKF distribution is highly matched with the statistical features of NSST coefficients within high-pass subbands. In our experiments, we applied the proposed method to two general RS image datasets- The UC Merced land use dataset and the Sydney dataset. The results show that our proposed method can achieve a more robust and commendable performance than the state-of-the-art approaches.