Detecting changes in complicated remote sensing images have been gaining much attention. One of the main challenges lies in how to detect intrinsic changes robustly while avoiding the false alarms caused by various challenging factors, such as spatial illumination variations, small viewpoint differences, noises and outliers between multi-temporal remote sensing images. To reduce the influence of these factors, in this paper, we propose an unsupervised joint learning model based on a total variation regularization and bipartite deep convolutional neural network, called total variation regularized bipartite network (TVRBN). In this model, parametric feature differences are initialized by a bipartite autoencoder. An objective function is defined as a parametric feature difference term integrated with a change pre-detection constraint term and a total variation regularization to the change probability map with intrinsic changes. Then the unified objective function is optimized jointly to learn the bipartite network parameters and detect an intrinsic change map. Due to the intrinsic difference constraints in feature space and total variation regularization, the proposed TVRBN method can compute higher-quality and smoother change maps, suppress non-intrinsic changes, and overcome small viewpoint differences in comparable images. Extensive experiments on both homogeneous and heterogeneous images demonstrate the robustness of the proposed method by comparing it with state-of-the-art methods.

Heterogeneous bi-temporal images have different visual appearances and inconsistent data distribution for the same scene, making it challenging to detect changes, which need to align the shared information and reduce various unwanted sensor-related noises for comparability. Mainstream methods usually adopt two types of techniques: feature transformation and image translation. The former relies on handcrafted priors while the latter lacks constraints on unwanted backgrounds, leading to limitations such as a lack of robustness to non-intrinsic changes (e.g., seasonal and atmospheric changes, and sensor-related noise) and unsatisfactory detection performance. To overcome these drawbacks, we propose a novel unsupervised predict-refining-involved bidirectional contrastive difference network (PRBCD-Net) composed of a coarse prediction module and iterative refining modules. Each refining module utilizes feature extractors with a cross-reconstruction constraint and bidirectional contrastive constraint to extract discriminative features, and then generate a refined change map by change map optimizers. Two advantages of the proposed PRBCD-Net are: 1) the cross-reconstruction constraint is used to promote the feature distribution consistency of the bi-temporal images by using the forward and backward transformations; 2) the bidirectional contrastive constraint is used to improve the discriminability of features by narrowing the gap between non-intrinsic changes while widening intrinsic changes under the guidance of a coarse change map. Thus, the refining module can generate a finer change map than the coarse one, and the performance can be further improved through multiple iterations. Experimental results demonstrate the effectiveness and robustness of the proposed method compared with state-of-the-art methods.

Remote sensing image fusion is a critical technique for enhancing the quality of remote sensing images. Typically, it is presumed that images have been accurately registered to facilitate effective fusion. Traditional methods involve a sequential process of registration followed by fusion, or a parallel approach to registration and fusion, which do not fully leverage the interplay between these two stages. To overcome this limitation, we propose a novel joint learning method termed the Dual-Conditionally Guided Registration and Fusion Diffusion Network (RFDifNet) for unregistered remote sensing images. This method integrates image registration and fusion into a unified framework. The RFDifNet comprises two conditional diffusion-based subnetworks: the Registration Diffusion Module (RegDM) and the Fusion Diffusion Module (FusDM). In this architecture, the RegDM corrects the misalignment of the unregistered image and provides it as a conditional input to the FusDM to generate the fused image. Conversely, the fused image is also fed back into the RegDM as a conditional input, enabling a closed-loop iteration of the registration and fusion processes. To further refine the image registration by reducing noise interference and preserving edge details within the RegDM, we introduce a joint learning loss function based on fractional-order derivatives, demonstrating superior performance in geometric and detail preservation compared to traditional methods. Experimental results validate the outstanding performance of the proposed RFDifNet in both image registration and fusion tasks. The source code is available at: https://github.com/DDXNJUST/RFDifNet.

Unsupervised change detection algorithms typically identify changed areas by comparing pixels or ground objects in co-registered bi-temporal images. However, due to small viewpoint variations in misaligned images and unknown changed regions between bi-temporal images, image registration becomes extremely challenging, and misaligned pixels may lead to a mass of false alarms. To address this issue, we develop a bidirectional spatial transformer network (BidSTN) capable of identifying the spatial shifts of corresponding key points in pre- and post-event images. It allows us to compute the coordinate offset relationships between misaligned images with small viewpoint variations and achieve spatial interpolation and registration using a thin-plate spline function. Building on the BidSTN, the bipartite spatial transformer difference network (BSTDN) is constructed by integrating encoders and decoders with feature difference minimization and cross-reconstruction constraints. It concurrently learns the distribution-consistent features of bi-temporal images in the feature space and their coordinate offset relationships, enabling the spatial registration and change detection of heterogeneous images within a single learning process. Furthermore, we incorporate a series of iterative refining modules (RMs) based on the aligned images and deep features. These modules enhance the accuracy of change detection by learning real changes within two distinct feature spaces and continuously refining the change map. Finally, by cascading the BSTDN and RMs, an unsupervised cross-modal registration-detection joint framework is constructed. Extensive experiments on both real and simulated datasets validate the framework’s superiority in suppressing misalignment artifacts, with F1 scores improving by an average of 15.83% (real dataset) and 11.12% (simulated dataset) compared to the best baseline methods. Code is available at https://github.com/lh-rs/BSTDN.

Multi-temporal remote sensing images acquired at different times inherently exhibit variations due to sensor noise, weather variations, and sensor heterogeneity, often leading to false alarms in change detection. Despite significant progress in heterogeneous bi-temporal change detection, accurate pixel-level comparison remains challenging due to inconsistent data representations of the same geographical scenes. To tackle this challenge, a contrastive regularized bipartite refinement network (CRBRN) is proposed. First, an unsupervised image style transfer method with structural consistency constraints is employed to transform the pre-event image into the domain of the post-event image, yielding bi-temporal images with unified visual styles for effective comparison. Subsequently, we propose an iterative bipartite refinement network that consists of a bipartite feature extraction and a difference discriminative module for homogeneous images. The former extracts deep features from the inputs, while the latter identifies changed regions from feature difference maps. To overcome the non-differentiability of traditional contrastive terms, a smoothed contrastive version is formulated. By regularizing feature constraints on deep feature differences, our approach suppresses pseudo-changes and prevents gradient instability, thereby improving the robustness of detection. Extensive experiments on four heterogeneous datasets validate the effectiveness of the proposed CRBRN. Quantitative results demonstrate that the proposed method effectively alleviates the interference of pseudo-changes caused by spatial-domain appearance inconsistencies, achieving competitive CD performance compared to state-of-the-art approaches.