Landslides pose a greater potential risk to the Sichuan-Tibet Transportation Project, and extensive landslide inventory and mapping are essential to prevent and control geological hazards along the Sichuan-Tibet Transportation Corridor (STTC). Recently proposed landslide detection methods mainly focused on new landslides with high vegetation. In addition, there are still challenges in automatic detection of old landslides using optical images. In this paper, two methods, namely mask region-based convolutional neural networks (Mask R-CNN) and transfer learning Mask R-CNN (TL-Mask R-CNN), are presented for detecting and segmenting new and old landslides, respectively. An optical remote sensing dataset for landslide recognition along the Sichuan-Tibet Transportation Corridor (LRSTTC) is constructed as an evaluation benchmark. Our experimental results show that the recall rate and F1-score of the proposed method for new landslide detection can reach 78.47% and 79.80%, respectively. Transfer learning is adopted to detect old landslides, and our experimental results show that evaluation indices can be further improved by about 10%. Furthermore, TL-Mask R-CNN has been applied to identify ice avalanches based on the characteristics of landslides. It appears that our proposed methods can detect and segment landslides effectively along the STTC with the constructed LRSTTC dataset, which is essential for studying and preventing landslide hazards in mountainous areas.

Space debris detection is important in space situation awareness and space asset protection. In this paper, we propose a method to detect space debris using feature learning of candidate regions. The acquired optical image sequences are first processed to remove hot pixels and flicker noise, and the nonuniform background information is removed by the proposed one dimensional mean iteration method. Then, the feature learning of candidate regions (FLCR) method is proposed to extract the candidate regions and to detect space debris. The candidate regions of space debris are precisely extracted, and then classified by a trained deep learning network. The feature learning model is trained using a large number of simulated space debris with different signal to noise ratios (SNRs) and motion parameters, instead of using real space debris, which make it difficult to extract a sufficient number of real space debris with diverse parameters in optical image sequences. Finally, the candidate regions are precisely placed in the optical image sequences. The experiment is performed using the simulated data and acquired image sequences. The results show that the proposed method has good performance when estimating and removing background, and it can detect low SNR space debris with high detection probability.

Landslide inventory mapping (LIM) is a key prerequisite for landslide susceptibility evaluation and disaster mitigation. It aims to record the location, size, and extent of landslides in each map scale. Machine learning algorithms, such as support vector machine (SVM) and random forest (RF), have been increasingly applied to landslide detection using remote sensing images in recent decades. However, their limitations have impeded their wide application. Furthermore, despite the widespread use of deep learning algorithms in remote sensing, for LIM, deep learning algorithms are limited to less unbalanced landslide samples. To this end, in this study, full convolution networks with focus loss (FCN-FL) were adopted to map historical landslides in regions with imbalanced samples using an improved symmetrically connected full convolution network and focus loss function to increase the feature level and reduce the contribution of the background loss value. In addition, K-fold cross-validation training models (FCN-FLK) were used to improve data utilization and model robustness. Results showed that the recall rate, F1-score, and mIoU of the model were improved by 0.08, 0.09, and 0.15, respectively, compared to FCN. It also demonstrated advantages over U-Net and SegNet. The results prove that the method proposed in this study can solve the problem of imbalanced sample in landslide inventory mapping. This research provides a reference for addressing imbalanced samples in the deep learning of LIM.

Hyperspectral remote sensing image (HSI) classification is very useful in different applications, and recently, deep learning has been applied for HSI classification successfully. However, the number of training samples is usually limited, causing difficulty in use of very deep learning models. We propose a wide and deep Fourier network to learn features efficiently by using pruned features extracted in the frequency domain. It is composed of multiple wide Fourier layers to extract hierarchical features layer-by-layer efficiently. Each wide Fourier layer includes a large number of Fourier transforms to extract features in the frequency domain from a local spatial area using sliding windows with given strides.These extracted features are pruned to retain important features and reduce computations. The weights in the final fully connected layers are computed using least squares. The transform amplitudes are used for nonlinear processing with pruned features. The proposed method was evaluated with HSI datasets including Pavia University, KSC, and Salinas datasets. The overall accuracies (OAs) of the proposed method can reach 99.77%, 99.97%, and 99.95%, respectively. The average accuracies (AAs) can achieve 99.55%, 99.95%, and 99.95%, respectively. The Kappa coefficients are as high as 99.69%, 99.96%, and 99.94%, respectively. The experimental results show that the proposed method achieved excellent performance among other compared methods. The proposed method can be used for applications including classification, and image segmentation tasks, and has the ability to be implemented with lightweight embedded computing platforms. The future work is to improve the method to make it available for use in applications including object detection, time serial data prediction, and fast implementation.

Precise vegetation maps of mountainous areas are of great significance to grasp the situation of an ecological environment and forest resources. In this paper, while multi-source geospatial data can generally be quickly obtained at present, to realize effective vegetation mapping in mountainous areas when samples are difficult to collect due to their perilous terrain and inaccessible deep forest, we propose a novel and intelligent method of sample collection for machine-learning (ML)-based vegetation mapping. First, we employ geo-objects (i.e., polygons) from topographic partitioning and constrained segmentation as basic mapping units and formalize the problem as a supervised classification process using ML algorithms. Second, a previously available vegetation map with rough-scale label information is overlaid on the geo-object-level polygons, and candidate geo-object-based samples can be identified when all the grids’ labels of vegetation types within the geo-objects are the same. Third, various kinds of geo-object-level features are extracted according to high-spatial-resolution remote sensing (HSR-RS) images and multi-source geospatial data. Some unreliable geo-object-based samples are rejected in the candidate set by comparing their features and the rules based on local expert knowledge. Finally, based on these automatically collected samples, we train the model using a random forest (RF)-based algorithm and classify all the geo-objects with labels of vegetation types. A case experiment of Taibai Mountain in China shows that the methodology has the ability to achieve good vegetation mapping results with the rapid and convenient sample collection scheme. The map with a finer geographic distribution pattern of vegetation could clearly promote the vegetation resources investigation and monitoring of the study area; thus, the methodological framework is worth popularizing in the mapping areas such as mountainous regions where the field survey sampling is difficult to implement.

Interferometric Synthetic Aperture Radar (InSAR) technology is the most advanced and effective method for monitoring large-scale slow-moving landslides. However, automatic landslide detection based on InSAR products regarding landslide samples and deep learning models is still challenging. Different InSAR products are inconsistent for slow-moving landslide detection due to the fuzzy boundaries of potential landslides and complex deformation characteristics. In addition, the accuracy of existing landslide detection models is not high because multi-scale factors in feature abstract and feature fusion are rarely considered. This paper proposes a multi-scale Swin Transformer InSAR products detection network (MSIDNet) to detect slow-moving landslides automatically. Firstly, we adopt an advanced Swin Transformer as the backbone to extract features and multi-scale spatial-temporal attention blocks in the neck to improve the feature fusion capability. Meanwhile, to train the new model, we built a landslide dataset based on visual interpretation, including deformation rate, phase gradient, and C-index (GRCI). Experiments demonstrate that our proposed method outperforms the existing deep learning models like Faster R-CNN and Yolov3. The GRCI dataset is more efficient and less biased than the traditional deformation rate and phase gradient datasets. The precision of detected landslides in a given testing area is 0.89, and it has good generalization ability. This study provides a new dataset and method for slow-moving landslide detection based on InSAR products.