Fully supervised change detection (CD) methods in remote sensing (RS) perform well but depend on costly and time-consuming pixel-level annotations, which are impractical to obtain at scale. Therefore, it is essential to develop annotation-efficient alternatives that can narrow the performance gap with fully supervised methods. To this end, we propose a novel weakly supervised CD framework, named RCNet, which employs dual networks to implement reliable co-training using image-level annotations. Our framework is grounded in multi-view learning of co-training and the localization ability of class activation mapping (CAM). In our approach, two sub-nets with the same architecture perform image-level change classification and pixel-level segmentation from different views. Although CAM roughly localizes changes, ambiguity and noise in its pseudo labels may cause confirmation bias, limiting performance. Our approach mitigates this bias by introducing a feature discrepancy loss to enable cross-supervision between two sub-nets. Meanwhile, CAM tends to highlight a single object, but RS images commonly contain many dense and small changed objects with complexity, resulting in decreased reliability of pseudo labels. Therefore, we present an IoU-based reliable pseudo label screening (RPLS) strategy, which minimizes the likelihood of changed areas being misidentified as unchanged, enhancing the reliability of changed information obtained. Besides, to further improve boundary fineness and internal integrity of changed areas, we incorporate an additional strong perturbation branch for each sub-net and develop a consistency regularization loss. Extensive experiments on three challenging RS image CD datasets demonstrate that our RCNet achieves competitive performance with image-level labels. The source code is available athttps://github.com/Youzhihui/RCNet.

Currently, a lot of work is focused on aerial object detection and has achieved good results. Though these methods have achieved promising results on the conventional datasets, it is still challenging to locate objects from the low-quality images captured in adverse weather conditions. Currently, there are limited approaches that combine aerial object detection with hazy conditions, and there are few publicly available datasets for real hazy weather based on aerial images. For this purpose, we propose a dataset HRSI, hazy remote sensing images in the real world, which is mainly divided into three categories: airport, large vehicle, and ship. All images in HRSI are from real hazy conditions. In addition, we propose an object detection model DFENet, a dehazing feature enhancement model for hazy remote sensing images, which is suitable for hazy weather. DFENet consists of a two-branch and a dehazing module. The two-branch structure helps to fully learn hazy and dehazing features. In order to avoid the impact of noise caused by the dehezing module, we also designed a haze-predict module (HPM) to predict the information containing haze in the image. We introduce the cross-fuse module (CFM) to utilize the information of haze to guide the feature fusion of two branches. By utilizing the information of haze, DFENet can dynamically adjust the feature weight in the two-branch to avoid the impact of noise generated by the dehazing module. Compared with traditional object detection methods, DFENet not only has good performance in hazy conditions but also improves performance in clear conditions. We tested DFENet on DOTA, HRSI, and Foggy-DOTA to demonstrate that DFENet performs better under hazy conditions.

Remote sensing (RS) image change detection (CD) has been a research hotspot in recent years, which plays an important role in urban planning and disaster assessment. However, since CD labels are difficult to obtain, how to utilize semantic information in RS images to improve the change prediction performance is a problem worth exploring. To solve this problem, we propose a transfer learning-based CD method that utilizes a diffusion generation model to translate high-level semantic information into low-level change information. First, we propose a pseudo-change image pair generation method that utilizes semantic labels to guide the diffusion model to generate change images. Then, the refined loss (RL) is designed to improve the model’s ability to recognize change features based on the difference between pseudo-change image pairs and unlabeled image pairs. Experimental results on WHU-CD, LEVIR-CD, and GoogleGZ-CD datasets show that the proposed method effectively transfers the semantic information into change information and finally improves the model’s feature recognition ability for change objects. Compared with recent CD and transfer learning methods, the proposed transfer learning model (TLM) achieves the best performance. The source code is available athttps://github.com/VCISwang/STCD.

With the continuous progress of deep learning in remote sensing (RS) visual tasks, considerable advancements have been achieved in RS image change detection (CD). However, prevailing CD methods heavily rely on extensive sets of fully pixelwise hand-annotated training data, a time-consuming and costly process, and they fail to fully harness the potential benefits of deep feature representations within the deep feature domain. To tackle the mentioned issues, we propose a novel semi-supervised CD method called PDLCD, which strategically leverages useful information from massive unlabeled data to complement labeled data with just a few samples. Specifically, changed objects and unchanged backgrounds of bitemporal RS images are various and complex, our approach advocates dividing each category into multiple subclasses in the deep feature domain. In this scheme, the high-level feature of each subclass follows a Gaussian distribution. Then, the prototype discriminative learning (PDL) is introduced to explicitly encourage deep features of samples closer to the nearest prototype within their respective category, and away from all prototypes of other categories. We design feature discriminative loss (FDL) to implement PDL for constructing more pronounced intraclass compactness and interclass variability. Finally, we compute the supervised loss based on a limited set of labeled data, incorporate the unsupervised loss leveraging a substantial volume of unlabeled data, and include FDL within the deep feature domain to collectively optimize the model. Extensive experiments carried out on three challenging RS image CD datasets illustrate that our proposed semi-supervised CD method obtains better CD performance than previous counterparts. The source code is available at:https://github.com/Youzhihui/PDLCD.

The development of deep learning in remote sensing (RS) visual tasks has led to remarkable progress in RS image change detection (CD). However, RS bi-temporal images cover complex and confusing scenes due to natural environmental factors, which presents challenges for CD task. How to effectively exploit long-range dependencies and sensitively discriminate real-changes with various scales from pseudo-changes are urgent problems. It is especially obvious for the changes of building structures man-made. This paper presents a CD approach named CSViG, which utilizes Siamese Vision Graph neural network (SViG) with crossed feature fusion. SViG acts as a feature extractor to capture richer short- and long-range dependencies. Crossed feature fusion consists of a horizontal feature fusion module (HFFM) and a vertical feature fusion module (VFFM). HFFM designs cross-concatenation (CC) way to reveal real-changes from pseudo-change in the same horizontal stage, after which global and local features are extracted by using attention mechanism and multi-scale depth-wise separable convolution. VFFM further fuses complementary content from vertical multiple stages to effectively represent change regions of different sizes (tiny or huge) by using attention mechanism. Extensive comparative experiments conducted on three available building change detection datasets demonstrate that the proposed method achieves better CD performance than previous counterparts.

The past several years have witnessed the rapid development of the task of road extraction in high-resolution remote sensing images. However, due to the complex background and road distribution, road extraction is still a challenging research in remote sensing images. In convolutional neural networks (CNNs), the U-shaped architecture network has shown its effectiveness. But the global representation cannot be captured effectively by CNNs. While in the transformer, the self-attention (SA) module can capture the long-distance feature dependencies. A hybrid encoder-decoder method called BDTNet is proposed in this letter, which enhance the extraction of global and local information in remote sensing images. Firstly, feature maps of different scales are obtained through the backbone network. And then, on the basis of reducing the computational cost of self-attention, the Bi-Direction Transformer Module (BDTM) is constructed to capture the contextual road information in feature maps of different scales. Finally, the Feature Refinement Module (FRM) is introduced to integrate the features extracted from the backbone network and BDTM, which enhances the semantic information of the feature maps and obtains more detailed segmentation results. The results show that the proposed method achieved a high IoU of 67.09% in the DeepGlobe dataset. Extensive experiments also verify the effectiveness of the proposed method on three public remote sensing road datasets.

With the development of deep learning, semantic segmentation of remote sensing images has made great progress. However, segmentation algorithms based on deep learning usually require a huge number of labeled images for model training. For remote sensing images, pixel-level annotation usually consumes expensive resources. To alleviate this problem, this letter proposes a semi-supervised segmentation method of remote sensing images based on an iterative contrastive network. This method combines few labeled images and more unlabeled images to significantly improve the model performance. First, contrastive networks continuously learn more potential information by using better pseudo labels. Then, the iterative training method keeps the differences between models to better improve the segmentation performance. The semi-supervised experiments on different remote sensing datasets prove that this method has a better performance than the related methods. Code is available athttps://github.com/VCISwang/ICNet.

With the development of deep learning, remote sensing (RS) image segmentation has been applied with marked success. However, in the process of model training, the large number of labeled images required more expensive annotation. A key challenge is how to make full use of extensive unlabeled images available to improve the segmentation model. In this article, we propose a semisupervised remote sensing image semantic segmentation method defined as RanPaste, which combines labeled images with unlabeled images to improve segmentation performance. First, we obtain pseudo label by randomly pasting part of the ground truth label into the predicted segmentation map. Then, we combine the labeled and unlabeled images to generate rough predictions after strong augmentation. Finally, by using the semisupervised loss, we achieve better performance on remote sensing image segmentation. Our method combines consistency regularization and pseudo label and then utilizes thresholds to gradually improve the model performance. RanPaste enables the model to learn more underlying information in the unlabeled data. Experimental results on six datasets show that RanPaste can learn more latent information from unlabeled data to improve segmentation performance. Besides, our approach achieves better segmentation results on different network structures and datasets.

With the development of deep learning in remote sensing (RS) image change detection (CD), the dependence of CD models on labeled data has become an important problem. To make better use of the comparatively resource-saving unlabeled data, the CD method based on semi-supervised learning (SSL) is worth further study. This article proposes a reliable contrastive learning (RCL) method for semi-supervised RS image CD. First, according to the task characteristics of CD, we design the contrastive loss based on the changed areas to enhance the model’s feature extraction ability for changed objects. Then, to improve the quality of pseudo labels in SSL, we use the uncertainty of unlabeled data to select reliable pseudo labels for model training. Combining these methods, semi-supervised CD models can make full use of unlabeled data. Extensive experiments on three widely used CD datasets demonstrate the effectiveness of the proposed method. The results show that our semi-supervised approach has a better performance than related methods. The code is available athttps://github.com/VCISwang/RC-Change-Detection.