With the fast development of remote sensing platforms and sensors technology, change detection with heterogeneous remote sensing images (Hete-CD) has become an attractive topic in recent years and plays a vital role in land cover change detection for responding to natural disaster emergencies when homogeneous images are unavailable. Although Hete-CD has been developed for about three decades, and various related methods have been developed and applied successfully in practice, a systematic and comprehensive review of the current achievements regarding Hete-CD remains lacking. Therefore, in this article, we first present an overview of Hete-CD in terms of the related literature. Second, the major techniques of Hete-CD are reviewed in terms of publicly available datasets, the taxonomy of major techniques, results, performance, and quantitative evaluation. Then, some classical methods are selected for comparison and discussion. Finally, based on the discussion and literature review, challenges, opportunities, and future directions for Hete-CD are concluded. The review aims to provide a “one-stop-shop” understanding of the problems with the categories of existing approaches, open opportunities and challenges, and potential future directions for Hete-CD.

Change detection with heterogeneous remote sensing images (HRSIs) is attractive for observing the Earth’s surface when homogeneous images are unavailable. However, HRSIs cannot be compared directly because the imaging mechanisms for bitemporal HRSIs are different, and detecting change with HRSIs is challenging. In this letter, a simple yet effective deep learning approach based on the classical UNet is proposed. First, a pair of image patches are concatenated together to learn a shared abstract feature in both image patch domains. Then, a multiscale convolution module is embedded in a UNet backbone to cover the various sizes and shapes of ground targets in an image scene. Finally, a combined loss function, which incorporates the focal and dice losses with an adjustable parameter, was incorporated to alleviate the effect of the imbalanced quantity of positive and negative samples in the training progress. By comparisons with five state-of-the-art methods in three pairs of real HRSIs, the experimental results achieved by our proposed approach have the best overall accuracy (OA), average accuracy (AA), recall (RC), and F-Score that are more than 95%, 79%, 60%, and 61%, respectively. The quantitative results and visual performance indicated the feasibility and superiority of the proposed approach for detecting land cover change with HRSIs.

Nighttime low illumination image enhancement is highly desired for outdoor computer vision applications. However, few works have been studied towards this goal. In addition, the low illumination enhancement problem becomes very challenging when the depth information of a low illumination image is unknown. To address this problem, in this paper, we propose a dual channel prior-based method for nighttime low illumination image enhancement with a single image, which builds upon two existing image priors: dark channel prior and bright channel prior. We utilize the bright channel prior to get an initial transmission estimate and then use the dark channel as a complementary channel to correct potentially erroneous transmission estimates attained from the bright channel prior. Experimental results show significant credibility of the approach both visually and by quantitative comparison with existing methods.

Deep neural networks have strong feature learning ability, but their vulnerability cannot be ignored. Current research shows that deep learning models are threatened by adversarial examples in remote sensing (RS) classification tasks, and their robustness drops sharply in the face of adversarial attacks. Therefore, many adversarial attack methods have been studied to predict the risks faced by a network. However, the existing adversarial attack methods mainly focus on single-modal image classification networks, and the rapid growth of RS data makes multimodal RS image classification a research hotspot. Generating multimodal adversarial examples needs to consider a high attack success rate, subtle perturbation, and collaborative attack ability between different modalities. In this article, we investigate the vulnerability of multimodal RS classification networks and propose a multifeature collaborative adversarial network (MFCANet) for generating multimodal adversarial examples. Two modality-specific generators are designed to generate the multimodal collaborative perturbations with strong attack ability, and two modality-specific discriminators make the generated multimodal adversarial examples closer to the real instances. In addition, a modality-specific generative loss and a modality-specific discriminative loss are proposed, and an alternating optimization strategy is designed for training the proposed MFCANet. Extensive experiments are carried out on the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen 2D dataset and ISPRS Potsdam 2D dataset. The results show that the attack performance of the proposed method is stronger than that of the fast gradient sign method (FGSM), project gradient descent (PGD), and Carlini and Wagner (C&W) attack methods.

The performance of a neural network is highly dependent on the labeled samples. However, the labeled samples are primarily clean, which prevents the network from capturing the features of the samples near the decision boundary. For hyperspectral images (HSIs), high spectral dimensions and same-spectra foreign matter lead to more boundary samples in the data. In this letter, we investigate an adversarial attack algorithm against these problems for HSIs. A modified DeepFool algorithm is implemented to generate boundary adversarial samples with minimal disturbance, and the generated boundary adversarial samples are simply added to the training set to improve the accuracy of the boundary samples in the data. Furthermore, we iteratively complete network training and boundary adversarial sample generation so that the decision boundary can be adjusted according to the real-time classification situation. Extensive experiments are carried out on the two HSI datasets, and the results demonstrate that the modified DeepFool algorithm can improve the accuracy of the decision boundary. Our findings also show that adversarial attacks are sensitive to high-dimensional and multiple-category data and are worthy of further study.