Impact craters are the most significant topographic features on the lunar surface. They are also essential factors affecting the construction of lunar bases and activities on the lunar surface in the future. Nevertheless, it is difficult to extract the impact craters by image processing methods, due to the influence of illumination in the lunar polar region. Furthermore, the digital elevation model (DEM) is unable to extract small impact craters. Therefore, we propose an extraction framework for small craters, namely AE-TransUNet+, for the future lunar South Pole region of interest. The algorithm is applied to grayscale images (0.5 m/pixel) captured by the Lunar Reconnaissance Orbital (LRO) Narrow-Angle Camera (NAC). To improve the channel extract and spatial relationship between features, the Convolutional Block Attention Modules (CBAM) and Depthwise-separable Convolution (DSC) are added to the core TransUNet model, and the redesigned jump connection enhancement module focuses more on small impact crater features. Experiments on the NAC dataset show that AE-TransUNet+ has a higher F1-Score (0.743) and IOU (0.847) compared to other crater detection models. Besides, based on transfer learning, the visualization results of Mars and Mercury shows that the proposed method is more efficient in the processing of remote sensing data for different deep space explorations providing better mobility.

Optical remote sensing videos, as a new source of remote sensing data that has emerged in recent years, have significant potential in remote sensing applications, especially national defense. In this paper, a tracking pipeline named TDNet (tracking while detecting based on a neural network) is proposed for optical remote sensing videos based on a correlation filter and deep neural networks. The pipeline is used to simultaneously track ships and planes in videos. There are many target tracking methods for general video data, but they suffer some difficulties in remote sensing videos with low resolution and those influenced by weather conditions. The tracked targets are usually misty. Therefore, in TDNet, we propose a new multi-target tracking method called MT-KCF and a detecting-assisted tracking (i.e., DAT) module to improve tracking accuracy and precision. Meanwhile, we also design a new target recognition (i.e., NTR) module to recognise newly emerged targets. In order to verify the performance of TDNet, we compare our method with several state-of-the-art tracking methods on optical video remote sensing data sets acquired from the Jilin No. 1 satellite. The experimental results demonstrate the effectiveness and the state-of-the-art performance of the proposed method. The proposed method can achieve more than 90% performance in terms of precision for single-target tracking tasks and more than 85% performance in terms of MOTA for multi-object tracking tasks.

Detecting changes in multisource heterogeneous images is a great challenge for unsupervised change detection methods. Image-translation-based methods, which transform two images to be homogeneous for comparison, have become a mainstream approach. However, most of them primarily rely on information from unchanged regions, resulting in networks that cannot fully capture the connection between two heterogeneous representations. Moreover, the lack of a priori information and sufficient training data makes the training vulnerable to the interference of changed pixels. In this paper, we propose an edge-oriented generative adversarial network (EO-GAN) for change detection that indirectly translates images using edge information, which serves as a core and stable link between heterogeneous representations. The EO-GAN is composed of an edge extraction network and a reconstructive network. During the training process, we ensure that the edges extracted from heterogeneous images are as similar as possible through supplemented data based on superpixel segmentation. Experimental results on both heterogeneous and homogeneous datasets demonstrate the effectiveness of our proposed method.

Parallel coordinates plots are popular tools for high-dimensional data visualization. To alleviate the difficulties caused by the inherent defects that arise when the dimensions are increased, this study attached two-dimensional kernel density scatter plots to parallel coordinates plot, which integrates the Cartesian and parallel coordinate systems, to combine their benefits and conveniently explore the relationships between paired attributes. The collaborative design combining the two visual types was realized through the corelative axis swap interaction method, which allows users to freely exchange parallel coordinate axes while also updating the associated kernel density scatter plots based on the Cartesian coordinate system. In this study, we obtained six cases of real datasets and performed a field trial study to assess the usefulness of our method in assisting users in evaluating correlations between paired attributes. The results show that our method is more useful for analyzing the relationships between paired attributes.

As the world has become increasingly digitalized in recent years, high-dimensional data with geographical location coordinate attributes, mainly referring to latitude and longitude, have been accumulated and spread to many disciplines. It is challenging to analyze such data. The map-in-parallel-coordinates plot (MPCP) is an incorporate visual analysis method that can express, filter, and highlight high-dimensional geographical data to facilitate data exploration and comprehension. In this paper, the MPCP underwent a series of field trial studies to verify its applicability, adaptability, and high efficacy in the real-world. The results of the evaluation were positive, which provides reasonable proof and new insights into the benefits of using MPCP to visually analyze high-dimensional geographical datasets.

Automatic visual encoding is frequently employed in automatic visualization tools to automatically map data to visual elements. This paper proposed an automatic visual encoding approach based on deep learning. This approach constructs visual encoding dataset in a more comprehensive and reliable manner to extract and label widely available visualization graphics on the Internet in accordance with three essentials of visualization. The deep learning model is then trained to create a visual encoding model with powerful generalization performance, enabling automated effective visual encoding recommendations for visual designers. The results demonstrated that our approach extends the automatic visual encoding techniques used by existing visualization tools, enhances the functionality and performance of visualization tools, uncovers previously undiscovered data, and increases the coverage of data variables.