Change detection is an essential and widely utilized task in remote sensing that aims to detect and analyze changes occurring in the same geographical area over time, which has broad applications in urban development, agricultural surveys, and land cover monitoring. Detecting changes in remote sensing images is a complex challenge due to various factors, including variations in image quality, noise, registration errors, illumination changes, complex landscapes, and spatial heterogeneity. In recent years, deep learning has emerged as a powerful tool for feature extraction and addressing these challenges. Its versatility has resulted in its widespread adoption for numerous image-processing tasks. This paper presents a comprehensive survey of significant advancements in change detection for remote sensing images over the past decade. We first introduce some preliminary knowledge for the change detection task, such as problem definition, datasets, evaluation metrics, and transformer basics, as well as provide a detailed taxonomy of existing algorithms from three different perspectives: algorithm granularity, supervision modes, and frameworks in the Methodology section. This survey enables readers to gain systematic knowledge of change detection tasks from various angles. We then summarize the state-of-the-art performance on several dominant change detection datasets, providing insights into the strengths and limitations of existing algorithms. Based on our survey, some future research directions for change detection in remote sensing are well identified. This survey paper sheds some light the topic for the community and will inspire further research efforts in the change detection task.

Object detection in drone-captured images is a popular task in recent years. As drones always navigate at different altitudes, the object scale varies considerably, which burdens the optimization of models. Moreover, high-speed and low-altitude flight cause motion blur on densely packed objects, which leads to great challenges. To solve the two issues mentioned above, based on YOLOv5, we add an additional prediction head to detect tiny-scale objects and replace CNN-based prediction heads with transformer prediction heads (TPH), constructing the TPH-YOLOv5 model. TPH-YOLOv5++ is proposed to significantly reduce the computational cost and improve the detection speed of TPH-YOLOv5. In TPH-YOLOv5++, cross-layer asymmetric transformer (CA-Trans) is designed to replace the additional prediction head while maintain the knowledge of this head. By using a sparse local attention (SLA) module, the asymmetric information between the additional head and other heads can be captured efficiently, enriching the features of other heads. In the VisDrone Challenge 2021, TPH-YOLOv5 won 4th place and achieved well-matched results with the 1st place model (AP 39.43%). Based on the TPH-YOLOv5 and CA-Trans module, TPH-YOLOv5++ can further increase efficiency while achieving comparable and better results.

Semantic segmentation for high-resolution remote sensing images is one of the most significant tasks in the field of remote sensing applications. Remote sensing images contain substantial detailed information of ground objects, such as shape, location, and texture. Therefore, these objects make the images exhibit large intraclass variance and small interclass variance, which makes it very difficult to be recognized. In this study, an end-to-end attention-based semantic segmentation network (SSAtNet) is proposed. A pyramid attention pooling module is proposed to introduce the attention mechanism into the multiscale module for adaptive features refinement. To correct the detailed information, the pooling index correction module integrates pooling index maps from the encoder with high-level feature maps, which can help recover the fine-grained features. In the encoder phase, a more effective ResNet-101 backbone is designed to capture detailed features. What is more, a series of data augmentation methods are proposed to enhance the model’s robustness. The proposed model is compared with several previous advanced networks and achieves the state of the art on the ISPRS Vaihingen dataset. The experiment results prove the effectiveness of the SSAtNet.

Remote sensing (RS) scene classification is a challenging task to predict scene categories of RS images. RS images have two main issues: large intraclass variance caused by large resolution variance and confusing information from large geographic covering area. To ease the negative influence from the above two issues. We propose a multigranularity multilevel feature ensemble network (MGML-FENet) to efficiently tackle the RS scene classification task in this article. Specifically, we propose multigranularity multilevel feature fusion branch (MGML-FFB) to extract multigranularity features in different levels of network by channel-separate feature generator (CS-FG). To avoid the interference from confusing information, we propose a multigranularity multilevel feature ensemble module (MGML-FEM), which can provide diverse predictions by full-channel feature generator (FC-FG). Compared to previous methods, our proposed networks have the ability to use structure information and abundant fine-grained features. Furthermore, through the ensemble learning method, our proposed MGML-FENets can obtain more convincing final predictions. Extensive classification experiments on multiple RS datasets (AID, NWPU-RESISC45, UC-Merced, and VGoogle) demonstrate that our proposed networks achieve better performance than previous state-of-the-art (SOTA) networks. The visualization analysis also shows the good interpretability of MGML-FENet.

Remote sensing image classification task is challenging due to the characteristics of complex composition, so different geographic elements in the same image will interfere with each other, resulting in misclassification. To solve this problem, we propose a multibranch ensemble network to enhance the feature representation ability by fusing final output logits and intermediate feature maps. However, simply adding branches will increase the complexity of models and decline the inference efficiency. To reduce the complexity of multibranch network, we make multibranch share more weights and add feature augmentation modules to compensate for the lack of diversity caused by weight sharing. To improve the efficiency of inference, we embed self-distillation (SD) method to transfer knowledge from ensemble network to main branch. Through optimizing with SD, the main branch will have close performance as an ensemble network. In this way, we can cut other branches during inference. In addition, we simplify the process of SD and totally adopt two loss functions to self-distill the logits and feature maps. In this article, we design a compact multibranch ensemble network, which can be trained in an end-to-end manner. Then, we insert an SD method on output logits and feature maps. Our proposed architecture (ESD-MBENet) performs strongly on classification accuracy with compact design. Extensive experiments are applied on three benchmark remote sensing datasets, AID, NWPU-RESISC45, and UC-Merced with three classic baseline models, VGG16, ResNet50, and DenseNet121. Results prove that ESD-MBENet can achieve better accuracy than previous state-of-the-art complex deep learning models. Moreover, abundant visualization analyses make our method more convincing and interpretable.

Few-shot segmentation focuses on the generalization of models to segment unseen object with limited annotated samples. However, existing approaches still face two main challenges. First, huge feature distinction between support and query images causes knowledge transferring barrier, which harms the segmentation performance. Second, limited support prototypes cannot adequately represent features of support objects, hard to guide high-quality query segmentation. To deal with the above two issues, we propose self-distillation embedded supervised affinity attention model to improve the performance of few-shot segmentation task. Specifically, the self-distillation guided prototype module uses self-distillation to align the features of support and query. The supervised affinity attention module generates high-quality query attention map to provide sufficient object information. Extensive experiments prove that our model significantly improves the performance compared to existing methods. Comprehensive ablation experiments and visualization studies also show the significant effect of our method on few-shot segmentation task. On COCO-20i dataset, we achieve new state-of-the-art results. Training code and pretrained models are available at https://github.com/cv516Buaa/SD-AANet.