This letter presents a novel change detection method for multitemporal synthetic aperture radar images based on PCANet. This method exploits representative neighborhood features from each pixel using PCA filters as convolutional filters. Thus, the proposed method is more robust to the speckle noise and can generate change maps with less noise spots. Given two multitemporal images, Gabor wavelets and fuzzy c-means are utilized to select interested pixels that have high probability of being changed or unchanged. Then, new image patches centered at interested pixels are generated and a PCANet model is trained using these patches. Finally, pixels in the multitemporal images are classified by the trained PCANet model. The PCANet classification result and the preclassification result are combined to form the final change map. The experimental results obtained on three real SAR image data sets confirm the effectiveness of the proposed method.

Hyperspectral image (HSI) denoising is critical for the effective analysis and interpretation of hyperspectral data. However, simultaneously modeling global and local features is rarely explored to enhance HSI denoising. In this letter, we propose a hybrid convolution and attention network (HCANet), which leverages both the strengths of convolution neural networks (CNNs) and Transformers. To enhance the modeling of both global and local features, we have devised a convolution and attention fusion module aimed at capturing long-range dependencies and neighborhood spectral correlations. Furthermore, to improve multi-scale information aggregation, we design a multi-scale feed-forward network to enhance denoising performance by extracting features at different scales. Experimental results on mainstream HSI datasets demonstrate the rationality and effectiveness of the proposed HCANet. The proposed model is effective in removing various types of complex noise. Our codes are available at https://github.com/summitgao/HCANet.

Automatic ship detection in high-resolution optical satellite images with various sea surfaces is a challenging task. In this letter, we propose a novel detection method based on sea surface analysis to solve this problem. The proposed method first analyzes whether the sea surface is homogeneous or not by using two new features. Then, a novel linear function combining pixel and region characteristics is employed to select ship candidates. Finally, Compactness and Length-width ratio are adopted to remove false alarms. Specifically, based on the sea surface analysis, the proposed method cannot only efficiently block out no-candidate regions to reduce computational time, but also automatically assign weights for candidate selection function to optimize the detection performance. Experimental results on real panchromatic satellite images demonstrate the detection accuracy and computational efficiency of the proposed method.

The joint hyperspectral image (HSI) and LiDAR data classification aims to interpret ground objects at more detailed and precise level. Although deep learning methods have shown remarkable success in the multisource data classification task, self-supervised learning has rarely been explored. It is commonly nontrivial to build a robust self-supervised learning model for multisource data classification, due to the fact that the semantic similarities of neighborhood regions are not exploited in existing contrastive learning framework. Furthermore, the heterogeneous gap induced by the inconsistent distribution of multisource data impedes the classification performance. To overcome these disadvantages, we propose a Nearest Neighbor-based Contrastive Learning Network (NNCNet), which takes full advantage of large amounts of unlabeled data to learn discriminative feature representations. Specifically, we propose a nearest neighbor-based data augmentation scheme to use enhanced semantic relationships among nearby regions. The intermodal semantic alignments can be captured more accurately. In addition, we design a bilinear attention module to exploit the second-order and even high-order feature interactions between the HSI and LiDAR data. Extensive experiments on four public datasets demonstrate the superiority of our NNCNet over state-of-the-art methods. The source codes are available at https://github.com/summitgao/NNCNet.

Change detection is of high practical value to hazard assessment, crop growth monitoring, and urban sprawl detection. A synthetic aperture radar (SAR) image is the ideal information source for performing change detection since it is independent of atmospheric and sunlight conditions. Existing SAR image change detection methods usually generate a difference image (DI) first and use clustering methods to classify the pixels of DI into changed class and unchanged class. Some useful information may get lost in the DI generation process. This paper proposed an SAR image change detection method based on neighborhood-based ratio (NR) and extreme learning machine (ELM). NR operator is utilized for obtaining some interested pixels that have high probability of being changed or unchanged. Then, image patches centered at these pixels are generated, and ELM is employed to train a model by using these patches. Finally, pixels in both original SAR images are classified by the pretrained ELM model. The preclassification result and the ELM classification result are combined to form the final change map. The experimental results obtained on three real SAR image datasets and one simulated dataset show that the proposed method is robust to speckle noise and is effective to detect change information among multitemporal SAR images.