Heterogeneous images are captured through different wavelength bands, providing rich and complementary information for change detection (CD), and domain transformation has emerged as a popular and effective solution. However, existing domain transformation-based CD methods overly rely on the quality of reconstructed features, making them appear inadequate for practical complex scenarios. In this paper, we propose a Style Transfer-based CD (STCD) method through unsupervised learning. STCD improves the quality and is robust to the reconstructed images by simultaneously employing a cautious labeling strategy and classifying. Specifically, we initially converted the two heterogeneous images provided into a shared domain by constructing a convolutional autoencoder based on adaptive instance normalization, which could improve the quality of reconstructed features and mitigate the data heterogeneity. Furthermore, we extract some significant pixel pairs based on fuzzy local information $c$-means to reduce the over-reliance on reconstructed features. Then we propose a Dynamic Superpixel-based Label Assignment (DSLA) rule to increase the reliable pseudo-labels employed in training a binary classifier. Finally, STCD can obtain great CD results even with poor reconstruction quality. Experimental results conducted on four heterogeneous datasets have demonstrated the effectiveness of STCD over other related CD methods.

Nowadays, remote sensing (RS) change detection (CD) plays an important role in Earth observation applications. Recently, the value of cross-modal CD has gradually emerged because of the complementary features in content. However, existing domain adaption-based methods are generally limited to the optical domain and suffer from imbalanced information between modalities. In this article, a novel CD method based on dual image translation and dual contrastive learning (C3D) is proposed for heterogeneous RS images, including a translation module and a CD module. First, the translation module aims to learn a comparable representation between the different domains through a C3D structure based on feature samples, which can break the consistency constraint and better solve the imbalanced information. Then the similarity metric of patches is compared by contextual features at different scales in the CD module to achieve a more accurate classification of changed and unchanged pixels. The C3D is compared with state-of-the-art methods and validated by the basic experimental results on five datasets. In addition, further experiments on the translation module were also performed to explore the effectiveness of contrastive learning in the CD task.

Infrared small target detection refers to the extraction of small targets in a complex, low signal-to-noise ratio background. Depthwise convolution makes it difficult to comprehensively characterize small infrared targets and ignores the importance of image background for the detection task. In this paper, we propose the generative adversarial differential analysis (GADA) model for infrared small target detection, the core of which aims to weaken the reliance on target features and enhance the use of background information. Specifically, we first construct pseudo background labels by the fast marching method. Then, the background-guided generative adversarial network is used to learn the background data distribution. On this basis, the differential image containing interest regions of small targets is obtained by differential analysis. Finally, the detection results are obtained by performing an elaborate characterization of the interest regions. The effectiveness of GADA is verified with three public datasets. Compared to several state-of-the-art methods, GADA achieves better performance in terms of $F1$ , $IoU$ , and $AUC$ .

Infrared small target detection from complex backgrounds is increasingly vital for military and civilian fields. Nonetheless, most of the existing methods are too restrictive to portray infrared targets from multidimensional and omnidirectional. In this article, we propose a low-rank differential cascade network (LDCNet) to integrate the physical properties and deep cascade features of infrared images. First, the cascade feature extraction module is designed via a multilevel coplanar cascade encoder–decoder structure, which integrates the deep-level and low-level features of infrared targets and backgrounds. Then, to provide a better understanding of the context capture of the scene, the differential attention mechanism based on the change differential analysis and robust principal component analysis is introduced. Finally, the multilevel feature fusion module is designed to adaptively integrate the spatial and semantic information of different depth feature maps to predict the final detection result. During the research, a new maritime small targets detection dataset is also constructed. Experimental results compared with other related methods on three datasets have demonstrated the effectiveness of LDCNet.

Smoke image segmentation plays a vital role in the accuracy of target extraction. In order to improve the performance of the traditional fire image segmentation algorithm, a new smoke segmentation method based on improved double truncation distance self-adaptive density peak clustering(TSDPC) is proposed. Firstly, the smoke image is over-segmented into multiple superpixels to reduce the time cost, and the local density of sample points corresponding to each superpixel is redefined by location information and color space information. Secondly, TSDPC combines the information entropy theory to find the optimal double truncation distance. Finally, TSDPC uses trigonometric functions to determine clustering centers in the decision diagram, which can solve the problem of over-segmentation. Then, it assigns labels to the remain sample points for obtaining the clustering result. Compared with other algorithms, the accuracy of TSDPC is increased by 5.68% on average, and the $F_1$ value is increased by 6.69% on average, which shows its high accuracy and effectiveness. In public dataset, TSDPC has also demonstrated its effectiveness.