Geological remote sensing interpretation can extract elements of interest from multiple types of images, which is vital in geological survey and mapping, especially in inaccessible regions. However, due to numerous classes, high interclass similarities, complex distributions, and sample imbalances of geological elements, the interpretation results of machine learning (ML)-based methods are understandably worse than manual visual interpretation. In addition, scholars in remote sensing have mainly carried out their works to interpret a single geological element category, such as mineral, lithological, soil, and structure. The interpretation of multiple geological elements is missing, which is more in line with the open world. To improve the interpretation results of ML-based methods and reduce the labor cost in geological survey and mapping, we propose a deep learning (DL)-feature-based adaptive multisource data fusion network (AMSDFNet) for the efficient interpretation of multiple geological remote sensing elements. The AMSDFNet has two branches for learning valuable spatial and spectral information from two kinds of data sources, in which the atrous spatial pyramid pooling (ASPP) operation and an attention block are applied to adaptively extract and fuse multiscale informative features. A hard example mining algorithm was also added to select important training examples to address sample imbalance. A large-scale region in western China with sufficient geological elements was set as the research area. The proposed model improved the two critical metrics by about 2% in the experiment section. As far as we know, this research work is the first time DL features and multisource remote sensing images have been utilized to simultaneously interpret geological elements of lithology, soil, surface water, and glaciers. The extensive experimental results demonstrated the superiority of DL features and our model in geological remote sensing interpretation.

Accurate bathymetric maps are essential to understand marine and coastal ecosystems. With the development of satellite and sensor technology, satellite-derived bathymetry (SDB) has been widely used to measure the depth of nearshore waters. Employment of physics-based methods requires a series of optical parameters of the water column and seafloor, which limits the application of these methods to shallow-water bathymetry. Due to convenience, low costs, and high efficiency, empirical methods based on in situ measurements and satellite imagery are increasingly used for nearshore bathymetry. These measurements are required to calibrate empirical models, so that reasonable accuracy can be achieved. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), equipped with the advanced topographic laser altimeter system, provides a novel opportunity for nearshore bathymetry. Using the new measurement strategy of photon counting, ICESat-2 can provide accurate bathymetric points from spaceborne observations, which can be used in place of in situ water depth data. In this study, ICESat-2 bathymetric points and multispectral images were used to train four typical models and produce bathymetric maps for Shanhu Island, Ganquan Island, and Lingyang Reef in the Xisha Islands of China. We evaluated the bathymetric results by comparing them with reference depth data from airborne light detection and ranging. All models had a satisfactory accuracy, as well as multimodel and multisource image consistency. With the ICESat-2 bathymetric points, SDB is no longer limited by in situ measurements. Hence, this approach could be extended to a larger scale to obtain nearshore bathymetric maps of coastal areas, surrounding islands, and reefs using free and open-access satellite data.

With the significant improvements in Earth observation (EO) technologies, remote sensing (RS) data exhibit the typical characteristics of Big Data. Propelled by the powerful feature extraction capabilities of intelligent algorithms, RS image interpretation has drawn remarkable attention and achieved progress. However, the semantic relationship and domain knowledge hidden in massive RS images have not been fully exploited. To the best of our knowledge, a comprehensive review of recent achievements regarding semantic graph-based methods for comprehension and interpretation of RS images is still lacking. Specifically, this article discusses the main challenges of RS image interpretation and presents a systematic survey of typical semantic graph-based methodologies for RS knowledge representation and understanding, including the Ontology Model, Geo-Information Tupu, and Semantic Knowledge Graph. Furthermore, we categorize and summarize how the existing technologies address different challenges in RS image interpretation based on semantic graph-based methods, which indicates that the semantic information about potential relationships and prior knowledge of variant RS targets are central to the solution. In addition, a case study of RS geological interpretation based on the semantic knowledge graph is demonstrated to show the promising capability of intelligent RS image interpretation. Finally, the future directions are discussed for further research.

Deep learning networks have facilitated the automated scene recognition of landforms based on geomorphogenesis. However, current genetic landform classification methods do not consider regional geological context, which can more accurately reflect the formation and evolution mechanism of geomorphic landforms than local ones. Therefore, this study proposes a multimodal, deep learning landform recognition framework based on a joint contextual geological and channel attention module (GCMENET). First, the multibranch feature extraction network of DenseNet121 is used to extract the respective features from the target scene and the contextual geological scene. Second, the features similar to the landform features of the target scene are extracted from the contextual geological features based on the cosine method and then combined with geomorphic features of the target scene. Third, channel attention mechanism is used to reduce the interference caused by redundant contextual geological information after fusion of data. To measure the classification accuracy of GCMENET, we establish a fine geomorphogenic dataset consisting of remote sensing images of six landform types with a $64\times64$ -pixel size and 10-m resolution (JOS10m). During the training process of two geomorphogenic datasets, the feature extraction network without batchnorm2d (batch normalization) could preserve the distribution and spatial alignment of data from the components. Using different training-to-validation data ratios and combinations of input components, the results of the GCMENET supplemented with the joint contextual geological and channel attention module exhibited greater accuracy than those obtained without the module. This observation confirms the importance of contextual geological information in automated geomorphogenic landforms.

Global warming is melting glaciers. Changes in mountain glaciers have a tremendous impact on human life. Regular identification and extraction of glaciers from satellite images are necessary. However, when studying glaciers, materials surrounding the glacier have high spectral similarity to glaciers and are easily misclassified in the identification process. Therefore, in this study of glacier extraction, we used an improved U-Net model (a channel-attention U-Net) to map glaciers. The model was trained on Landsat 8 Operational Land Imager (OLI) data and a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), and was tested on glaciers in the Pamir Plateau. The results show that the channel-attention U-Net identifies glaciers with relatively high accuracy compared to U-Net and GlacierNet. The obtained results were fine-tuned by the conditional random field model, effectively reducing background misidentification.

This paper compares the accuracy of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM), Shuttle Radar Topography Mission (SRTM) C-band and German Aerospace Centre (DLR)-SRTM X-band digital elevation models (DEMs) with the Ziyuan 3 (ZY-3) stereoscopic DEM and ground control points (GCPs). To date, the horizontal error of these DEMs has received little attention in accuracy assessments. Using the ZY-3 DEM as reference, this study examines (1) the horizontal offset between the three DEMs and the reference DEM using the normalised cross-correlation method, (2) the vertical accuracy of those DEMs using kinematic GPS data and (3) the relationship between the three DEMs and the reference ZY-3 DEM. The results show that the SRTM and DLR-SRTM have greater vertical accuracy after applying horizontal offset correction, whereas the vertical accuracy of the ASTER GDEM is less than the other two DEMs. These methods and results can be useful for researchers who use DEMs for various applications.