“卫星信息智能处理与应用技术”专刊
Theme Keywords： super-resolution reconstructionresidual dense networkremote-sensing imageremote sensing intelligent interpretation
- The Paper
Domain-adaptation algorithm for remotely sensing building changes through instance contrast learning
Abstract：Building-change detection automatically identifies changes in ground buildings in remote-sensing images acquired in the same geographic area at different times. Fully supervised change-detection algorithms require a large amount of labeled remote sensing data to make accurate predictions. Manually labeling a building change detection label is time-consuming and labor intensive because it requires a professional to compare and label two images pixel by pixel. Unsupervised domain adaptation technique is an effective means to alleviate this problem. Although the current-domain adaptation algorithm has achieved good results in building-change detection, the following problems persist: Problem 1: A class-based domain mixing strategy is applicable to a large number of categories. In building-change detection, only positive samples of the category “change” are available. Problem 2: In the current pixel-based contrast learning method, pseudo labels generated by a model must have samples with classification errors because the labels of target domains are unidentifiable. This requirement introduces large noise information during contrast training. Problem 3: The pseudo label generated by high-confidence threshold filtering does not leverage the low confidence prediction results of a teacher model. To solve the above problems, this paper proposes a case-level contrast-learning domain-adaptation algorithm for cross-domain building-change detection task.This paper proposes an instance contrast-learning domain adaptation for change detection (ICDA-CD) method for cross-domain building-change detection. The main contributions are as follows: (1) A region-level domain-mixing method is proposed, which combines data containing the buildings in a source domain and data containing buildings in a target domain on one sample simultaneously. (2) Case-level contrast learning method is proposed. In the encoder, the distance between the biphasic features of a changing building area is pulled apart. In the decoder, the distance between the features of each changing building area is narrowed. (3) A pseudo label quality estimation method is proposed. The pseudo-label quality of each pixel position is estimated by the value predicted by a teacher model, and then loss is weighted.Domain migration experiments were performed on the LEVIR-CD and S2Looking datasets, and comparison and ablation experiments were performed with advanced domain-adaptation algorithms. In the migration of the LEVIR-CD task to the S2Looking task, the proposed algorithm achieved the highest F1 and IOU of 43.91 and 28.31, respectively. In the migration of the S2Looking task to the LEVIR-CD task, the proposed algorithm achieved the highest F1 scores and IOU of 74.75 and 59.68, respectively.To solve the problem of unsupervised domain adaptive change detection algorithm across data domains, an ICDA-CD method was proposed. The accuracy of the cross-domain unsupervised domain adaptive change detection algorithm was effectively improved by using region-level domain mixing, case-level contrast learning, and pseudo label quality estimation-weighted loss.Keywords：remote sensing images;building change detection;contrast learning;domain adaptation;deep learning;pseudo label1201|2800|1
<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2024-07-31- Abstract：Since the 1970s, China has made remarkable achievements in the field of remote-sensing satellite systems. It is moving toward the era of intelligent and networked giant remote-sensing satellite constellations. The realization of networking and intelligence of giant remote sensing satellite constellations is not a one-off process. The development of the national remote sensing satellite industry in China hinges on the current opportunity period of historical development, careful resolution of technical challenges, direction of future technological breakthroughs, and preparations for the era of remote-sensing satellite constellation. This paper reviews the development history of China’s remote-sensing satellite systems and analyzes the technical challenges faced by the development of a giant remote-sensing constellation. First, owing to the massive data processing problem in the remote-sensing constellation, this paper focuses on on-orbit data preprocessing and intelligent information extraction methods, analyzes the problems through on-board intelligent processing, and proposes an on-board processing mode supported by the ground. Second, complex problems in the management of tasks in a large constellation and the current status of on-board autonomous mission planning capabilities are analyzed, and a digital twin-based integrated intelligent mission management and distributed intelligent collaboration model is proposed.Overall, China is currently in the stage of limited intelligent processing, particularly on-board and ground-based processing, and is entering the stage at which it can perform simple mission control on board and ground-based control. Driven by advancements in intelligent processing capabilities on board, the development of remote-sensing constellation systems toward the ideal “organic giant constellation” can be divided into three stages, that is, giant remote-sensing constellation 1.0-3.0. The integrated design and technical laying out of the intelligent giant remote-sensing constellation system as a whole, combining space and ground elements, must be carried out as early as possible. Key technical research on highly efficient and reliable intersatellite communication technologies should be undertaken. Furthermore, the performance of algorithms interpreting remote sensing data based on random sample data, digital twin-based integrated intelligent mission planning technology for space and ground, on-orbit unsupervised continuous self-learning theories, methods for the intelligent processing of remote sensing satellite data, and event-driven rapid task allocation technologies for large-scale constellations should be evaluated and optimized. These efforts can promote the development of national remote-sensing satellites for a new era of intelligence.Keywords：giant remote sensing constellation;development history;Intelligent;networked;application technology;satellite constellation mission control;data processing;development plan6795|9355|11<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2024-07-31
- Abstract：Optical remote sensing is a widely used technology in aerospace reconnaissance and geological exploration. Visible light images captured by this technology provide a wealth of information and have important applications in intelligence gathering, object monitoring, and situational forecasting. Considerable progress in remote sensing image object perception has been achieved, particularly in ship and airplane detection. However, technical challenges, including with large object-scale variations and numerous small objects, in remote sensing image object perception remain. Existing work has mainly focused on improving boundary box representations, and single-object detection models fail to fully exploit spatial correlation information from surrounding or similar objects. To address the inherent inefficiency of existing remote sensing image object detection algorithms that detect different objects independently, this paper proposes a novel detection framework called group object detection. By detecting the state information of a group object, our framework alleviates problems, such as insufficient perception information and poor reliability of single-object perception, generating reliable multi-object detection results. This paper introduces a concept of group objects and proposes an automated annotation scheme for group objects. By analyzing existing labels on a public dataset, the proposed scheme obtains annotated information with group object labels without manual annotation. Based on the automated annotation of group targets, a group target detection algorithm is presented, which enhances single-object detection results by utilizing the spatial constraints of group objects. Experimental results on the DOTA dataset, a widely-used remote sensing object detection benchmark, demonstrate that the proposed group target detection algorithm outperforms state-of-the-art methods.Keywords：remote sensing image;image object;group object detection;automatic annotation;DOTA;object sensing;multi object1783|3362|2<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2024-07-31
- Abstract：In recent years, remote sensing intelligent interpretation technologies have advanced rapidly, but most established models are task oriented. Therefore, generalizing them to different tasks is difficult, and considerable amounts of resources are wasted. The foundation model is a straightforward approach that has recently attracted considerable interest in the field of remote sensing. Although many works have achieved remarkable results in some tasks for perception recognition and cognitive prediction by using remote sensing single-temporal or multitemporal data, a comprehensive review that provides a systematic overview of the remote sensing foundation model is lacking. Thus, this paper begins by summarizing developments in research on existing remote sensing foundation models from the perspectives of data, methods, and applications. Then, after analyzing the current situation’s limits, we proposed a novel general predictive foundation model. Finally, some essential research areas were highlighted, and past achievements were linked with the future possibilities of remote sensing foundation model.Existing remote sensing foundation models were categorized into three groups according to the data types used (single-temporal/multitemporal) and the tasks involved (perceptual recognition/cognitive prediction): the foundation model of perceptual recognition based on single-temporal data, the foundation model of perceptual recognition based on multitemporal data, and the foundation model of cognitive prediction based on multitemporal data. According to the different self-supervised learning methods adopted, we divided the existing foundation models of perceptual recognition based on single-temporal data into those based on contrastive learning and those based on generative learning. According to the number of tasks, the foundation model of perceptual recognition based on multitemporal data was divided into a single-task-oriented foundation model and a multitask-oriented foundation model. According to different model architectures, the cognitive prediction foundation models based on multitemporal data were divided into transformer-based and graph network-based foundation models. In accordance with the aforementioned categorization, we described the current state of each type of remote sensing foundation models and summarized their data, methods, and application restrictions.Based on the summary and analysis of the existing remote sensing foundation models, a novel general predictive foundation model assumption was proposed. The information pipeline for multidomain or temporal data input and multitime or spatial scale task output can be opened up by extracting stable and generalized time-series hyper-pixel features. This approach enabled the accurate cognitive prediction of the future state. Tens of millions of multiplatform, multitype, multimodal, and multitemporal data were included. By combining the benefits of the transformer model and the graph network, a new foundation model architecture was created, which increased the model’s capacity and enhanced generalization while predicting multitarget interactions in large remote sensing scenes over the long term. In terms of application, the general predictive foundation model can be applied to diverse cognitive prediction tasks with multiple spatial and time scales. Under this assumption, we proposed four exploratory directions: multidomain time series data representation, stable feature extraction, object-environment interaction modeling, and multitask interaction reasoning, aiming to provide a reference for researchers exploring remote sensing foundation models.In general, foundation models with generalization ability are crucial to development of remote sensing intelligent interpretation. We provided an overview of current advances in this field by collating the current state of research on remote sensing foundation models. By analyzing the limitations of current remote sensing foundation models in terms of data, methods, and applications, we proposed a novel general predictive foundation model assumption and further clarified four exploratory directions that urgently need breakthroughs under this idea. The follow-up work will make specific and important technological breakthroughs in multidomain time series data representation, stable feature extraction, object-environment interaction modeling, and multitask interaction reasoning. We explored a general remote sensing foundation model integrating perception recognition and cognitive prediction into a single architecture.Keywords：remote sensing intelligent interpretation;remote sensing foundation models;general prediction;multi temporal data;multi-task5900|11060|13<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2024-07-31
- Abstract：Super-resolution reconstruction technology plays an important role in the intelligent processing of satellite remote sensing images. Existing deep learning methods for super-resolution remote sensing image reconstruction can only handle super-resolution tasks with a single scale factor, lacking generalization at the multiscale level and failing to meet the requirements of real super-resolution remote sensing image reconstruction for continuous zooming at multiple magnification levels.To address the problem of arbitrary-scale super-resolution reconstruction and enhance the quality of reconstructed real remote sensing images, this paper proposes a super-resolution reconstruction method called the Meta-RDCAN, which utilizes meta-learning and residual dense channel attention network.The proposed method employs a meta-upscale module that incorporates three functions: weight prediction, location projection, and feature mapping. The module adaptively adjusts the internal parameters of a model according to different scale factors for arbitrary-scale super-resolution reconstruction. From the perspective of extracting detailed information of local land objects in a remote sensing image, a dense residual network with an attention mechanism is used as a feature extractor, enabling the reconstructed results to possess clear and distinguishable details.Extensive experiments are conducted on various datasets, including DIV2K, AID, UCMerced, WIDS, Set5, and real remote sensing image from Macao Science Popularization Satellite. The influence of variations in spatial resolution on the super-resolution reconstruction results is analyzed, and the effectiveness of the training scheme, which involves pretraining on a general dataset followed by fine-tuning on a remote sensing dataset, is validated using the loss curve.The test results with different scale factors demonstrate that the proposed model is suitable for arbitrary-scale super-resolution reconstruction tasks in remote sensing images, having scale factors of up to 4.0. Comparative experimental results show that the improved model with added channel attention achieves enhanced performance in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural SIMilarity (SSIM), compared with the baseline model. On real remote sensing data, the reconstruction results of the proposed model achieve a PSNR of over 40 dB and an SSIM of over 0.95. The comparison based on the no-reference quality indicator NIQE confirms that the perceived quality of the super-resolution reconstruction results of the improved model surpasses that of the baseline model.The proposed method for arbitrary-scale super-resolution reconstruction is effective for remote sensing images by utilizing meta-learning and dense residual channel attention. The main contributions of this paper include two aspects. First, for the arbitrary-scale super-resolution reconstruction of remote sensing image, the meta-learning approach is employed to adaptively adjust the internal parameters of a model. This approach enables continuous integer- and non-integer-scale super-resolution reconstruction of a single remote sensing image with a single model. Second, to address issues, such as missing details and unclear edges of geographic features, in reconstruction results, a channel-attention mechanism is applied to enhance the dense residual network and improve the quality of super-resolution reconstruction results.Keywords：super-resolution reconstruction;remote sensing image;arbitrary-scale;meta-learning;residual dense network;channel attention mechanism1396|2789|0<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2024-07-31
