Abstract Deep learning methods have recently displayed ground‐breaking results for synthetic aperture radar image change detection problem. However, they still face the challenges of intrinsic noise and the difficulty of acquiring labeled data. To sort out these issues, we aim to develop a change detection approach specifically designed for analyzing synthetic aperture radar images based on Deep Belief Network as the deep architecture which includes unsupervised feature learning and supervised network fine‐tuning. The deep neural networks can reach the final change maps directly from the two original images. A pre‐classification based on Morphological Reconstruction and Membership Filtering is employed in order to minimize the effect of noise. Appropriate diversity samples are provided by a virtual sample generation method in order to mitigate overfitting raised by limited synthetic aperture radar data. Visual and quantitative analysis as well as comparisons with advanced algorithms show that our algorithm not only achieves better results but also requires less implementation time.

Abstract Here, a new set of fractional‐order moments, named fractional‐order generalized Laguerre moments (FGLM), is introduced. These proposed moments are defined on the Cartesian coordinate system and their basis functions are represented by the fractional‐order generalized Laguerre polynomials. Contrary to the classical Chebyshev, Legendre and Gegenbauer moments, which provide only global feature, our proposed FGLM have the ability to extract both global and local features. Moreover, a new set of rotation, scale and translation invariants of the FGLM, is derived and introduced for image classification and invariant pattern recognition. Just as important, we have presented a systematic parameter selection method for finding the optimal fractional parameter values with respect to pattern recognition applications. Finally, several recursive methods for reducing the computation time of our proposed invariants are also provided in this study. Therefore, to demonstrate the performance of the introduced fractional‐order moments and moment invariants, a number of experimental analysis are performed in terms of global and local features extraction, robustness to noise, invariance to geometric deformations, object recognition and computational speed. The presented theoretical and experimental results clearly show that the proposed fractional‐order moments and their corresponding invariants could be extremely useful in the field of image analysis.