Accurate and rapid localization can improve geographic information system (GIS) tasks to support disaster rescue and resource exploration in field operations. However, the existing localization methods suffer from high costs, low efficiency, and poor accuracy of long-distance targets due to environmental factors like terrain and landforms. Therefore, we propose a multilevel localization framework based on mobile phones for different field scenarios. First, we investigated a rapid localization method constrained by scene contextual features in the field with salient features. Second, we designed a single-point localization method that combines DEM data and mobile phones when high-precision DEM data are available for the region. Third, we studied a map-matching corrected joint localization with mobile phone images in the field lacking salient features and high-precision DEM data. Finally, we developed a prototype system for field localization and selected a forest scene for experimental analysis. The results showed that the proposed mobile phone-based localization framework supports long-distance localization in the field. The localization accuracy in three different field scenarios is within 100 meters, and the localization efficiency reaches a minute level, which can effectively support the convenient, rapid, high-precision, and long-distance localization tasks in field scenes.

Despite significant advancements in salient object detection (SOD) in optical remote sensing images (ORSIs), challenges persist due to the intricate edge structures of ORSIs and the complexity of their contextual relationships. Current deep learning (DL) approaches encounter difficulties in accurately identifying boundary features and lack efficiency in collaboratively modeling the foreground and background by leveraging contextual features. To address these challenges, we propose a stronger multifaceted collaborative salient object detector in ORSIs, termed LBA-MCNet, which incorporates aspects of localization, balance, and affinity. The network focuses on accurately locating targets, balancing detailed features, and modeling image-level global context information. Specifically, we design the edge feature adaptive balancing and adjusting (EFABA) module for precise edge localization, using edge features to guide attention to boundaries and preserve spatial details. Moreover, we design the global distributed affinity learning (GDAL) module to model global context. It captures global context by generating an affinity map from the encoder’s final layer, ensuring effective modeling of global patterns. In addition, deep supervision during deconvolution further enhances feature representation. Finally, we compared with 28 state-of-the-art approaches on three publicly available datasets. The results clearly demonstrate the superiority of our method. The codes of our method are available athttps://github.com/little1bold/LBA-MCNet.

Low resolution is a major factor that negatively impacts the accuracy of remote sensing (RS) interpretation. High-quality super-resolution reconstruction (SRR) can help alleviate this problem. In this study, we present a multistage semantic segmentation training strategy called SRSSTS, which is based on detail-optimized SRR. SRSSTS addresses the challenges of poor reconstruction quality and low semantic segmentation (SS) accuracy of RS images. Our approach involves constructing a generative adversarial network (GAN)-based perceptual-loss-dominated SRR network, which generates texture-realistic, high-quality high-resolution images from low-resolution RS images. We then input the generated images into an SS network in steps and propose a three-stage joint loss to generate segmentation results at different resolutions. SRSSTS is straightforward and efficient, and it can be applied to any SS backbone networks. Additionally, this strategy does not increase computational resources compared to other SR-based segmentation methods, since the SRR network is trained separately. We evaluated the effectiveness of our designed SRR network and SRSSTS on five RS datasets (ISPRS Potsdam and Vaihingen, WHDLD, LoveDA Rural and Urban) with different resolutions, achieving excellent performance compared to an SS model for a single task and other SR-based segmentation methods. Moreover, we observed that the learned perceptual image patch similarity (LPIPS) of the super-resolution (SR) reconstructed images, i.e., the stronger the perception ability, the more helpful it is for the SS task.

Building extraction is a significant topic in high-resolution remote sensing. Insufficient integrity, irregular boundaries, and inaccurate corners remain a problem for existing methods. However, individually optimizing one of these aspects may leave problems in others. Unfortunately, few methods consider integrity, boundary, and corner simultaneously. In this study, we propose a three-stage network (IBCO-Net) incorporating integrity-boundary-corner optimization for fine segmentation of buildings. First, long-range dependent and spatial-continuous blocks (LDSCs) are plugged into the decoder to enhance building integrity. Second, the direction field correction module (DFCM) controls the overall shape of the building by learning the direction field and executing an iterative correction algorithm. Finally, the multi-strategy point refinement module (MSPRM) selects boundary and corner points for re-classification to further refine the boundary and relocate corners. And a hybrid loss function supervises IBCO-Net to optimize each stage. Comparative experiments were conducted on three datasets: the Massachusetts building dataset, the ISPRS Potsdam dataset, and the dataset of building instances of typical cities in China. We evaluated common pixel-level metrics and object-level boundary and corner metrics, with experimental results showing that IBCO-Net outperforms 8 state-of-the-art CNN and Transformer-based methods. In addition, the generality of the proposed method is demonstrated via its performance by applying 9 existing backbone networks.

With the increasing application of deep learning in various domains, salient object detection in optical remote sensing images (ORSI-SOD) has attracted significant attention. However, most existing ORSI-SOD methods predominantly rely on local information from low-level features to infer salient boundary cues and supervise them using boundary ground truth, but fail to sufficiently optimize and protect the local information, and almost all approaches ignore the potential advantages offered by the last layer of the decoder to maintain the integrity of saliency maps. To address these issues, we propose a novel method named boundary-semantic collaborative guidance network (BSCGNet) with dual-stream feedback mechanism. First, we propose a boundary protection calibration (BPC) module, which effectively reduces the loss of edge position information during forward propagation and suppresses noise in low-level features without relying on boundary ground truth. Second, based on the BPC module, a dual feature feedback complementary (DFFC) module is proposed, which aggregates boundary-semantic dual features and provides effective feedback to coordinate features across different layers, thereby enhancing cross-scale knowledge communication. Finally, to obtain more complete saliency maps, we consider the uniqueness of the last layer of the decoder for the first time and propose the adaptive feedback refinement (AFR) module, which further refines feature representation and eliminates differences between features through a unique feedback mechanism. Extensive experiments on three benchmark datasets demonstrate that BSCGNet exhibits distinct advantages in challenging scenarios and outperforms the 17 state-of-the-art (SOTA) approaches proposed in recent years. Codes and results have been released on GitHub: https://github.com/YUHsss/BSCGNet.

Ionospheric phase delay is a critical error source in Time Series Interferometric Synthetic Aperture Radar (TS-InSAR) for the purpose of monitoring ground surface deformation with SAR data obtained from low-frequency radar systems. Recently, the Range Split-Spectrum Interferometry (RSSI) method has been employed to estimate and rectify ionospheric errors in TS-InSAR. However, the performance of the RSSI method is largely restricted by the significant linear scale factors resulting from the current small SAR bandwidth. In this study, we propose a Reformulating RSSI (Re-RSSI)-based method for correcting the ionospheric error in TS-InSAR by optimizing the linear scale factors, with the aim of improving the accuracy of TS-InSAR measurements. We evaluate the Re-RSSI method using 121 ALOS-1 PALSAR images that cover two distinct regions: the low-latitude Lazufre volcano region and the high-latitude Anaktuvuk River tundra fire region. Our results demonstrate that the Re-RSSI method can effectively remove time series ionospheric errors at both test sites, where we detected ionospheric delays of approximately 2.5 cm/yr and 2.0 cm/yr, respectively. Using Global Navigation Satellite System (GNSS) measurements as ground truth, we achieved an 86.59% improvement rate in root mean square error (RMSE) with the Re-RSSI method, which is significantly higher than the 66.40% improvement rate achieved with the traditional RSSI method.

Damaged building detection from remote sensing imagery helps to quickly and rapidly assess losses after an earthquake. In recent years, deep learning technology has become a favorable tool for remote sensing image information detection. Based on the characteristics of damaged buildings in remote sensing images, in this paper, a framework for damaged building detection that considers heterogeneity characteristics is proposed. First, a local-global context attention module is proposed to improve the feature detection ability of the network, which can extract the features of damaged buildings from different directions and effectively aggregate global and local features. In addition, the module takes the correlation between feature maps at different scales into account while extracting information. Second, a feature fusion module with self-attention is established to replace the simple connection between the encoding and decoding processes, which improves the detail feature recovery ability of the network during the upsampling process. Finally, to fully aggregate semantic and detail features at different scales, a multibranch auxiliary classifier is established by adding two separate branches in the prediction stage. The effectiveness of the proposed approach is verified based on data from the 2010 Haiti earthquake, and comparisons with 3 object-oriented methods and 16 existing excellent deep learning models are performed. The IOU increase of 0.03%-7.39% is achieved using the proposed approach compared with excellent deep learning models.

Shadow extraction is an important and challenging task in remote sensing image analysis because the presence of shadows not only reduces radiation information but also affects the interpretation of remote sensing images. In this article, a clustering feature constraint multiscale attention network for shadow extraction from remote sensing images is proposed. First, in addition to the pixel-level description of the traditional neural network, our method focuses on the clustering relationships between pixel pairs to obtain the pixel group features of shadows. The feature extraction capability of the network is improved with a reweighting mechanism at the pixel level and pixel group features. Second, we employ a feature fusion algorithm by considering contextual information to improve the network’s attention toward shadow areas and enhance the nonlinear expression ability during the encoding and decoding layers. Furthermore, considering the most prominent multiscale features of shadows in remote sensing images, a deep multiscale feature aggregation structure is established to better fit the multiscale feature expression of shadows. Finally, we construct a shadow extraction dataset to verify the proposed approach. We compare our method with the results of state-of-the-art deep learning models. The results show that the intersection over union (IOU) of our method is improved by 0.85%–9.51% and that the$F1$-score is improved by 0.73–6.48. In addition, the test results for images with different resolutions prove that the proposed approach is more robust than the other methods.

Virtual scenes can present rich and clear disaster information, which can significantly improve the level of public disaster perception. However, existing methods for constructing scenes of debris flow disasters have some deficiencies. First, the construction process does not consider public knowledge, which makes it difficult for the constructed scenes to meet the requirements of the public. Second, the scene representation emphasizes visual effects but lacks augmented visualization, leading to scarcity of semantic information and inefficient public perception. In this paper, the optimal selection of scene objects, semantic augmentation through the combination of various visual variables and dynamic augmented representation are discussed in detail. Finally, a debris flow that occurred Shuimo town is selected for experiment analysis. The experimental results show that most people are unaware of the risks posed by debris flow disasters. The public is more concerned about the consequences of a disaster than its spatiotemporal process, especially when the consequences are related to their own interests. Furthermore, an augmented representation can increase the amount of semantic information of scene objects, which is essential for enhancing public understanding of the causes, processes and effects of debris flows and thereby changing people’s attitudes and enhancing their risk perception.