Scene classification is one of the areas of remote sensing image processing that is gaining much attention. Aiming to solve the problem of the limited precision of optical scene classification caused by complex spatial patterns, a high similarity between classes, and a high diversity of classes, a feature cross-layer interaction hybrid algorithm for optical remote sensing scene classification is proposed in this paper. Firstly, a number of features are extracted from two branches, a vision transformer branch and a Res2Net branch, to strengthen the feature extraction capability of the strategy. A novel interactive attention technique is proposed, with the goal of focusing on the strong correlation between the two-branch features, to fully use the complementing advantages of the feature information. The retrieved feature data are further refined and merged. The combined characteristics are then employed for classification. The experiments were conducted by using three open-source remote sensing datasets to validate the feasibility of the proposed method, which performed better in scene classification tasks than other methods.

Semantic segmentation stands as a prominent domain within remote sensing that is currently garnering significant attention. This paper introduces a pioneering semantic segmentation model based on TransUNet architecture with improved coordinate attention for remote-sensing imagery. It is composed of an encoding stage and a decoding stage. Notably, an enhanced and improved coordinate attention module is employed by integrating two pooling methods to generate weights. Subsequently, the feature map undergoes reweighting to accentuate foreground information and suppress background information. To address the issue of time complexity, this paper introduces an improvement to the transformer model by sparsifying the attention matrix. This reduces the computing expense of calculating attention, making the model more efficient. Additionally, the paper uses a combined loss function that is designed to enhance the training performance of the model. The experimental results conducted on three public datasets manifest the efficiency of the proposed method. The results indicate that it excels in delivering outstanding performance for semantic segmentation tasks pertaining to remote-sensing images.

As a kind of natural disasters, geological hazards pose serious threats to the ecological security and socio-economic development of human society. Evaluating the sensitivity of regional geological hazards is essential for making disaster-preventing/mitigating strategies and land use plans. Based on multi-source remote sensing data, this study evaluates the geological hazard sensitivity in Liaoning Province by using the method of spatial principal component analysis and spatial autocorrelation finding that such sensitivity is generally moderate, with an average value of 5.25, and is mainly concentrated in three levels:Mild sensitivity, Moderate sensitivity and Insensitivity, accounting for more than 67% of the total area. Liaodong Mountain Area and Liaoxi Hilly Area are found with the highest sensitivity of geological hazards, showcasing obvious spatial differences and aggregations, mainly because these areas have large terrain fluctuations, poor stability of rock and soil bodies, and strong erosion by precipitation. The main factors affecting the sensitivity of geological hazards include land use mode, elevation, slope and lithology. Among them, the land use mode has the highest contribution rate to the first principal component, reaching 0.86. This study provides a theoretical reference for the prevention and control over geological hazards and the protection of ecological security in Liaoning Province.

Blowouts are an important ground indication of wind‐sand activity in the Hulunbuir grassland. They include two basic geomorphologic units, erosion depression and sand deposition, and three typical morphological types: saucer type, trough type, and compound type. In this study, the dynamic changes of typical blowouts within the past decade were analyzed via multiperiod high‐resolution remote sensing images. RTK was used to repeatedly measure the blowouts to obtain their high‐precision 3D terrain data in 2010, 2011, and 2012. Short‐term dynamic changes in 3D blowout morphology were carefully analyzed to discover the following. (1) From 2002 to 2012, the depressions of typical blowouts exhibited downwind extension and lateral expansion trends, as they continuously grew in size. Regarding the sand deposition zones, those of the saucer blowout grew continuously, while those of the trough and compound blowouts fluctuated between growth and contraction. (2) The erosion depression of saucer blowouts eroded downward and spread horizontally; that of trough blowouts first accumulated then eroded but also spread horizontally. The erosion depression of compound blowouts exhibited horizontal spreading accompanied with bottom accumulation. The sand deposition zones of all three types of blowouts exhibited decreasing length with increasing width and height.