The registration of synthetic aperture radar (SAR) and optical images is a challenging task due to the potential nonlinear intensity differences between the two images. In this paper, a novel image registration method, which combines nonlinear diffusion and phase congruency structural descriptor (PCSD), is proposed for the registration of SAR and optical images. First, to reduce the influence of speckle noise on feature extraction, a uniform nonlinear diffusion-based Harris (UND-Harris) feature extraction method is designed. The UND-Harris detector is developed based on nonlinear diffusion, feature proportion, and block strategy, and explores many more well-distributed feature points with potential of being correctly matched. Then, according to the property that structural features are less sensitive to modality variation, a novel structural descriptor, namely, the PCSD, is constructed to robustly describe the attributes of the extracted points. The proposed PCSD is built on a PC structural image in a grouping manner, which effectively increases the discriminability and robustness of the final structural descriptor. Experimental results conducted on SAR and optical image pairs demonstrate that the proposed method is more robust against speckle noise and nonlinear intensity differences and improves the registration accuracy effectively.

The selection of training samples is important for the accuracy and efficiency of the synthetic aperture radar (SAR) image change detection task. However, training samples are traditionally extracted from the whole image, which leads to longer training time and an unbalanced number of pixels in the changed and unchanged classes. To overcome this problem, we propose a novel change detection method combining saliency detection with a principal component analysis network, named SDPCANet. To enhance the reliability of the training samples and reduce the amount of training samples, the SDPCANet uses context-aware saliency detection to obtain the salient region, from which the training samples are extracted. In addition, to alleviate the gap between the numbers of training samples in two classes, we regulate the candidate samples using the uniform-selecting strategy to enhance the reliability of the training samples for the SDPCANet. Then, the SDPCANet is trained with the extracted training samples and the remaining pixels are classified in the salient region to obtain the final change map. The experimental results on four sets of multitemporal SAR images demonstrate that the SDPCANet outperforms the reference methods proposed recently.

Although complex-valued (CV) neural networks have shown better classification results compared to their real-valued (RV) counterparts for polarimetric synthetic aperture radar (PolSAR) classification, the extension of pixel-level RV networks to the complex domain has not yet thoroughly examined. This paper presents a novel complex-valued deep fully convolutional neural network (CV-FCN) designed for PolSAR image classification. Specifically, CV-FCN uses PolSAR CV data that includes the phase information and uses the deep FCN architecture that performs pixel-level labeling. The CV-FCN architecture is trained in an end-to-end scheme to extract discriminative polarimetric features, and then the entire PolSAR image is classified by the trained CV-FCN. Technically, for the particularity of PolSAR data, a dedicated complex-valued weight initialization scheme is proposed to initialize CV-FCN. It considers the distribution of polarization data to conduct CV-FCN training from scratch in an efficient and fast manner. CV-FCN employs a complex downsampling-then-upsampling scheme to extract dense features. To enrich discriminative information, multi-level CV features that retain more polarization information are extracted via the complex downsampling scheme. Then, a complex upsampling scheme is proposed to predict dense CV labeling. It employs the complex max-unpooling layers to greatly capture more spatial information for better robustness to speckle noise. The complex max-unpooling layers upsample the real and the imaginary parts of complex feature maps based on the max locations maps retained from the complex downsampling scheme. In addition, to achieve faster convergence and obtain more precise classification results, a novel average cross-entropy loss function is derived for CV-FCN optimization. Experiments on real PolSAR datasets demonstrate that CV-FCN achieves better classification performance than other state-of-art methods.

High-resolution (HR) synthetic aperture radar (SAR) image classification is a challenging task for the limitation of its complex semantic scenes and coherent speckles. Convolutional neural networks (CNNs) have been proven the superior local spatial features representation capability for SAR images. However, it is hard to capture global information of images by convolutions. To solve such issues, this letter proposes an end-to-end network named global–local network structure (GLNS) for HR SAR classification. In the GLNS framework, a lightweight CNN and a compact vision transformer (ViT) are designed to learn local and global features, and two types of features are fused in quality to mine complementary information through the fusion net. Then, our research devolves the twofold loss function to reduce the interclass distance of SAR images, which brings more compactness to classification features and less interference of coherent speckles. Experimental results on real HR SAR images indicate that the proposed method has more strong feature extraction capability and noise resistance performance. This method achieves the highest classification accuracy on both datasets compared with other related approaches based on CNN.

For random-field-based image segmentation, the conditional random field (CRF) model offers theoretic advantages over the generative Markov random field one, since it directly models the posterior distribution of label field conditioned on an observable image. In this paper, we propose an adaptive hybrid CRF (AHCRF) model for synthetic aperture radar (SAR) image segmentation. Based on the generation of superpixels and their boundary feature analysis, the proposed method adaptively divides SAR image into different parts, namely, homogeneous regions, heterogeneous regions, and edges. In homogeneous regions, the regional-level CRF is defined on superpixels, and the pixels within each superpixel force to have the same segmentation label. Oppositely, the pixel-level CRF is defined on pixels within heterogeneous regions or edges, and local autocovariance features are extracted for constructing the unary and pairwise potentials to incorporate effective local contextual information. The integration of regional-level and pixel-level CRFs gives the proposed AHCRF model, and it is validated by experiments on several real SAR images. The experimental analysis indicates that the AHCRF is robust to speckle noise and preserves detailed features well in segmentation.