Image semantic segmentation has been applied more and more widely in the fields of satellite remote sensing, medical treatment, intelligent transportation, and virtual reality. However, in the medical field, the study of cerebral vessel and cranial nerve segmentation based on true-color medical images is in urgent need and has good research and development prospects. We have extended the current state-of-the-art semantic-segmentation network DeepLabv3+ and used it as the basic framework. First, the feature distillation block (FDB) was introduced into the encoder structure to refine the extracted features. In addition, the atrous spatial pyramid pooling (ASPP) module was added to the decoder structure to enhance the retention of feature and boundary information. The proposed model was trained by fine tuning and optimizing the relevant parameters. Experimental results show that the encoder structure has better performance in feature refinement processing, improving target boundary segmentation precision, and retaining more feature information. Our method has a segmentation accuracy of 75.73%, which is 3% better than DeepLabv3+.

With the continuous development of space remote sensing technology, the spatial resolution of visible remote sensing images has been continuously improved, which has promoted the progress of remote sensing target detection. However, due to the limitation of sensor lattice size, it is still challenging to obtain a large range of high-resolution (HR) remote sensing images in practical applications, which makes it difficult to carry out target monitoring in a large range of areas. At present, many object detection methods focus on the detection and positioning technology of HR remote sensing images, but there are relatively few studies on object detection methods using medium- and low-resolution (M-LR) remote sensing images. Because of its wide coverage area and short observation period, M-LR remote sensing imagery is of great significance for obtaining information quickly in space applications. However, the small amount of fine-texture information on objects in M-LR images brings great challenges to detection and recognition tasks. Therefore, we propose a small target detection method based on degradation reconstruction, named DRADNet. Different from the previous methods that use super resolution as a pre-processing step and then directly input the image into the detector, we have designed an additional degenerate reconstruction-assisted framework to effectively improve the detector’s performance in detection tasks with M-LR remote sensing images. In addition, we introduce a hybrid parallel-attention feature fusion module in the detector to achieve focused attention on target features and suppress redundant complex backgrounds, thus improving the accuracy of the model in small target localization. The experimental results are based on the widely used VEDAI dataset and Airbus-Ships dataset, and verify the effectiveness of our method in the detection of small- and medium-sized targets in M-LR remote sensing images.

Multi-object tracking in satellite videos (SV-MOT) is an important task with many applications, such as traffic monitoring and disaster response. However, the widely studied multi-object tracking (MOT) approaches for general images can rarely be directly introduced into remote sensing scenarios. The main reasons for this can be attributed to the following: (1) the existing MOT approaches would cause a significant rate of missed detection of the small targets in satellite videos; (2) it is difficult for the general MOT approaches to generate complete trajectories in complex satellite scenarios. To address these problems, a novel SV-MOT approach enhanced by high-resolution feature fusion and a two-step association method is proposed. In the high-resolution detection network, a high-resolution feature fusion module is designed to assist detection by maintaining small object features in forward propagation. By utilizing features of different resolutions, the performance of the detection of small targets in satellite videos is improved. Through high-quality detection and the use of an adaptive Kalman filter, the densely packed weak objects can be effectively tracked by associating almost every detection box instead of only the high-score ones. The comprehensive experimental results using the representative satellite video datasets (VISO) demonstrate that the proposed HRTracker with the state-of-the-art (SOTA) methods can achieve competitive performance in terms of the tracking accuracy and the frequency of ID conversion, obtaining a tracking accuracy score of 74.6% and an ID F1 score of 78.9%.

Although supervised classification of hyperspectral images (HSI) has achieved success in remote sensing, its applications in real scenarios are often constrained, mainly due to the insufficiently available or lack of labelled data. As a result, unsupervised HSI classification based on data clustering is highly desired, yet it generally suffers from high computational cost and low classification accuracy, especially in large datasets. To tackle these challenges, a novel unsupervised spatial-spectral HSI classification method is proposed. By combining the entropy rate superpixel segmentation (ERS), superpixel-based principal component analysis (PCA), and PCA-domain 2D singular spectral analysis (SSA), both the efficacy and efficiency of feature extraction are improved, followed by the anchor-based graph clustering (AGC) for effective classification. Experiments on three publicly available and five self-collected aerial HSI datasets have fully demonstrated the efficacy of the proposed PCA-domain superpixelwise SSA (PSSA) method, with a gain of 15–20% in terms of the overall accuracy, in comparison to a few state-of-the-art methods. In addition, as an extra outcome, the HSI dataset we acquired is provided freely online.

In recent years, the development of deep learning has brought great convenience to the work of target detection, semantic segmentation, and object recognition. In the field of infrared weak small target detection (e.g., surveillance and reconnaissance), it is not only necessary to accurately detect targets but also to perform precise segmentation and sub-pixel-level centroid localization for infrared small targets with low signal-to-noise ratio and weak texture information. To address these issues, we propose UCDnet (Double U-shaped Segmentation Network Cascade Centroid Map Prediction for Infrared Weak Small Target Detection) in this paper, which completes “end-to-end” training and prediction by cascading the centroid localization subnet with the semantic segmentation subnet. We propose the novel double U-shaped feature extraction network for point target fine segmentation. We propose the concept and method of centroid map prediction for point target localization and design the corresponding Com loss function, together with a new centroid localization evaluation metrics. The experiments show that ours achieves target detection, semantic segmentation, and sub-pixel-level centroid localization. When the target signal-to-noise ratio is greater than 0.4, the IoU of our semantic segmentation results can reach 0.9186, and the average centroid localization precision can reach 0.3371 pixels. On our simulated dataset of infrared weak small targets, the algorithm we proposed performs better than existing state-of-the-art networks in terms of semantic segmentation and centroid localization.

On-orbit geometric calibration is key to improving the geometric positioning accuracy of high-resolution optical remote sensing satellite data. Grouped calibration with geometric consistency (GCGC) is proposed in this paper for the Jilin1-KF01B satellite, which is the world’s first satellite capable of providing 150-km swath width and 0.5-m resolution data. To ensure the geometric accuracy of high-resolution image data, the GCGC method conducts grouped calibration of the time delay integration charge-coupled device (TDI CCD). Each group independently calibrates the exterior orientation elements to address the multi-time synchronization issues between imaging processing system (IPS). An additional inter-chip geometric positioning consistency constraint is used to enhance geometric positioning consistency in the overlapping areas between adjacent CCDs. By combining image simulation techniques associated with spectral bands, the calibrated panchromatic data are used to generate simulated multispectral reference band image as control data, thereby enhancing the geometric alignment consistency between panchromatic and multispectral data. Experimental results show that the average seamless stitching accuracy of the basic products after calibration is better than 0.6 pixels, the positioning accuracy without ground control points(GCPs) is better than 20 m, the band-to-band registration accuracy is better than 0.3 pixels, the average geometric alignment consistency between panchromatic and multispectral data are better than 0.25 multispectral pixels, the geometric accuracy with GCPs is better than 2.1 m, and the geometric alignment consistency accuracy of multi-temporal data are better than 2 m. The GCGC method significantly improves the quality of image data from the Jilin1-KF01B satellite and provide important references and practical experience for the geometric calibration of other large-swath high-resolution remote sensing satellites.