For the task of change detection (CD) in remote sensing images, deep convolution neural networks (CNNs)-based methods have recently aggregated transformer modules to improve the capability of global feature extraction. However, they suffer degraded CD performance on small changed areas due to the simple single-scale integration of deep CNNs and transformer modules. To address this issue, we propose a hybrid network based on multi-scale CNN-transformer structure, termed MCTNet, where the multi-scale global and local information is exploited to enhance the robustness of the CD performance on changed areas with different sizes. Especially, we design the ConvTrans block to adaptively aggregate global features from transformer modules and local features from CNN layers, which provides abundant global-local features with different scales. Experimental results demonstrate that our MCTNet achieves better detection performance than existing state-of-the-art CD methods.

A new change detection method for heterogeneous remote sensing images (i.e. SAR & optics) has been proposed via pixel transformation. It is difficult to directly compare the pixels from heterogeneous images for detecting changes. We propose to transfer the pixels in different images to a common feature space for convenience of comparison. For each pixel in the 1stimage, it will be transferred to the 2ndfeature space associated with the 2ndimage according to the given unchanged pixel pairs. In fact, this transformation is done assuming that the pixel is not affected by the events. Then the difference value between the estimation of transferred pixel and the actual one in the same location of the 2ndimage can be calculated. The bigger difference value, the higher possibility of change happening. We can similarly do the opposite transformation from the 2ndimage to the 1stimage, and one more difference value is obtained in the 1stfeature space. Change occurrences will be detected using Fuzzy C-means clustering method based on the sum of two difference values. The flood detection in the SAR and optical images is given in the experiments, and it shows that the proposed method is able to efficiently detect changes.