Heterogeneous change detection (HeCD) is a highly valuable yet challenging task in remote sensing. To enable the comparison of heterogeneous images with different imaging mechanisms, some structural consistency-based image transformation methods have been proposed, which utilize graph models to represent image structures and constrain the transformed images and original images to have the same structural characteristics on the graph model. Consequently, these graph-based methods face two challenges: adequately characterizing the image structure and effectively utilizing the change information. To address these challenges, this article proposes a signed graph-based image transformation (SGIT) method for unsupervised HeCD. First, we analyze the limitations of previous unsigned graph-based methods in capturing the image structure, which leads to the failure to detect changes in some scenes. In light of this, we construct signed graph models that utilize positive/negative weights to represent the similarity/dissimilarity relationships within the image, respectively, and employ adaptive weighting, negative sampling, and neighborhood expansion strategies to bolster the structure representation capability of signed graphs. Second, we analyze how the change would induce a bimodal distribution of vertex feature distances in original and transformed images. Subsequently, a distribution-induced reweighted graph Laplacian regularization (RGLR) is proposed to exploit this prior change information. Finally, a more accuracy image transformation model is obtained by incorporating three types of constraints: signed graph-based structural consistency term, bimodal distribution-induced RGLR, and change sparsity-based penalty term. Extensive comparative experiments on five real datasets have demonstrated the effectiveness of the proposed SGIT.

Remote sensing scene classification (RSSC) is a fundamental yet challenging task in the domain of remote sensing (RS). Currently, the methods based on deep features from convolutional neural networks (CNNs) have significantly improved the scene classification accuracy (ACC). However, the standard convolution operations have limited capacity to model the long-range correlations and cannot effectively obtain global contextual understanding ability. In this letter, we propose a novel scene classification framework, termed cross-layer nonlocal network (CL-NL-Net), consisting of a backbone network, a cross-layer nonlocal (CL-NL) module, and a classifier. Among them, the backbone network is used to obtain multilayer convolutional features. The CL-NL module is the core of the proposed method, which captures the long-range correlations between different layers, so as to achieve a better global scene understanding ability. To verify the effectiveness of the proposed CL-NL-Net, we conduct experiments on four benchmark datasets, and the results demonstrate that the proposed method achieves competitive classification ACC and outperforms some state-of-the-art methods.

Change detection of heterogeneous multitemporal satellite images is an important and challenging topic in remote sensing. Since the imaging mechanisms of heterogeneous sensors are different, it is not possible to directly compare heterogeneous images to detect changes as in the homogeneous images. To address this challenge, we propose an unsupervised image regression-based change detection method based on the structure consistency. The proposed method first adaptively constructs a similarity graph to represent the structure of a pre-event image, then uses the graph to translate the pre-event image to the domain of the post-event image, and then computes the difference image. Finally, a superpixel-based Markovian segmentation model is designed to segment the difference image into changed and unchanged classes. The proposed adaptive structure consistency-based image regression model can not only alleviate the impact of noise and changed pixels on the regression process by using the structure-based transformation, but also easily distinguish between changed and unchanged classes in the difference image by using the prior sparse knowledge of changes. Experimental results on six different datasets demonstrate the effectiveness of the proposed method by comparing with some state-of-the-art methods.

Convolutional Neural Networks (CNNs) have been shown remarkable performance in the task of remote sensing image scene classification. Recent works demonstrate that aggregating multi-scale convolutional features can significantly improve the classification accuracy. However, existing methods either use some unsupervised feature encoding methods or based on feature aggregation methods to aggregate multi -scale convolutional features, ignoring the information redundancy and semantic ambiguity between of them. To address the above-mentioned limitations, an end-to-end multi-scale feature aggregation network (MSF A) based on channel-spatial attention module is proposed to learn discriminative scene representation for remote sensing scene classification. The experimental results on the aerial image data set (AID) demonstrate that the proposed method achieves competitive classification performance compared with other state-of-the-art methods.

Existing land cover classification methods mostly rely on either the optical or synthetic aperture radar (SAR) features alone, which ignore the mutual complementary effects between optical and SAR sources. In this article, we compare the distribution histograms of deep semantic features extracted from optical and SAR modalities within land cover categories, which intuitively demonstrates that there are the large complementary potentials between the optical and SAR features. Therefore, we propose a novel collaborative attention-based heterogeneous gated fusion network (CHGFNet), which hierarchically fuses both optical and SAR features for land cover classification. More specifically, the CHGFNet consists of three main components: two-stream feature extractor, multimodal collaborative attention module (MCAM), and the gated heterogeneous fusion module (GHFM). Given optical and SAR patch pairs, two-stream feature extractor introduces multistage feature learning methodology to acquire discriminative optical and SAR features. Then, to explore the inherent complementarity between optical and SAR features, MCAM is embedded into CHGFNet, which provides an efficient stage to capture the correlation between optical and SAR features by jointly calculating the collaborative attention in joint feature space. Finally, to automatically learn the varying contributions of both optical and SAR features for classifying different land categories, GHFM is used to fuse both optical and SAR features. Extensive comparative evaluations demonstrate the advantages of CHGFNet within land cover classification over the state-of-the-art methods on three co-registered optical and SAR data sets.