首页期刊导航|IEEE journal of selected topics in applied earth observations and remote sensing
期刊信息/Journal information
IEEE journal of selected topics in applied earth observations and remote sensing
Institute of Electrical and Electronics Engineers
Institute of Electrical and Electronics Engineers
季刊
1939-1404
IEEE journal of selected topics in applied earth observations and remote sensing/Journal IEEE journal of selected topics in applied earth observations and remote sensingSCI
查看更多>>摘要：Accurate building semantic segmentation in remote sensing imagery is essential for urban planning, environmental monitoring, and map creation. While deep learning has achieved significant advancements in this field, precisely segmenting building edges and shadows in complex scenarios remains challenging. Shadows often introduce boundary ambiguities, affecting the local shape and texture information of buildings. Current methods do not fully perceive or utilize shadows. To address these challenges, we propose an advanced high-resolution image segmentation network, high-resolution network, integrated with a shadow-inclusive edge perception module. Our approach involves introducing a shadow-inclusive contour transition module (SCTM) during the feature extraction stage to enhance the features of blurry boundaries. The proposed SCTM and shadow-aware attention module significantly enhance attention maps, improve responses in blurry boundary regions, and increase consistency between predictions and ground truth, setting a new benchmark for building semantic segmentation in remote sensing imagery. This enriched information is then fed into an attention module that concurrently focuses on boundary and channel features, surpassing traditional semantic segmentation methods. We validated our method on three datasets: Massachusetts, WHU, and Inria. Our approach outperformed state-of-the-art methods on the WHU Building Dataset across all metrics, including mIoU, Accuracy, Kappa, and Dice coefficient.
查看更多>>摘要：The accurate mapping of Spartina alterniflora (S. alterniflora) invasion is crucial for controlling its spread and reducing severe ecological problems. Satellite images have been extensively employed for S. alterniflora invasion monitoring; however, there are still several issues that need to be addressed. The spectral similarities between S. alterniflora and surrounding ground objects make it challenging for traditional classifiers to achieve satisfactory extraction accuracy. Since the phenological information and red-edge spectral differences have been considered as informative features for identifying S. alterniflora, current studies mainly used them separately as classification features and seldom considered the differences of red-edge information at different phenological periods. Therefore, we proposed a pixel-based phenological and red-edge feature composite method (PpRef-CM) for S. alterniflora extraction considering both phenological information and red-edge bands derived from Sentinel-2 time series based on the existing pixel-based phenological feature composite method (Ppf-CM). The proposed PpRef-CM and machine-learning algorithms were employed for S. alterniflora extraction in two typical mangrove forests along coastal China. Results indicated that red-edge information at different phenological periods is essential for detecting S. alterniflora. S. alterniflora extraction achieved the highest accuracy of 96.57% by using the eXtreme gradient boost algorithm when compared with other machine-learning algorithms. The PpRef-CM gave 2.72% and 2.61% more extraction accuracies of S. alterniflora than the Ppf-CM in two study sites, separately. These findings provide insights for selecting suitable classification features for S. alterniflora extraction studies and serve as an effective control and management of S. alterniflora.
查看更多>>摘要：The freeze-thaw cycle of near-surface soils significantly affects energy and water exchanges between the atmosphere and land surface. Passive microwave remote sensing is commonly used to observe the freeze-thaw state. However, existing algorithms face challenges in accurately monitoring near-surface soil freeze/thaw in alpine zones. This article proposes a framework for enhancing freeze/thaw detection capability in alpine zones, focusing on band combination selection and parameterization. The proposed framework was tested in the three river source region (TRSR) of the Qinghai-Tibetan Plateau. Results indicate that the framework effectively monitors the freeze/thaw state, identifying horizontal polarization brightness temperature at 18.7 GHz (TB18.7H) and 23.8 GHz (TB23.8H) as the optimal band combinations for freeze/thaw discrimination in the TRSR. The framework enhances the accuracy of the freeze/thaw discrimination for both 0 and 5-cm soil depths. In particular, the monitoring accuracy for 0-cm soil shows a more significant improvement, with an overall discrimination accuracy of 90.02%, and discrimination accuracies of 93.52% for frozen soil and 84.68% for thawed soil, respectively. Furthermore, the framework outperformed traditional methods in monitoring the freeze-thaw cycle, reducing root mean square errors for the number of freezing days, initial freezing date, and thawing date by 16.75, 6.35, and 12.56 days, respectively. The estimated frozen days correlate well with both the permafrost distribution map and the annual mean ground temperature distribution map. This study offers a practical solution for monitoring the freeze/thaw cycle in alpine zones, providing crucial technical support for studies on regional climate change and land surface processes.
查看更多>>摘要：This article is the first of a two-part study on disturbance-informed land use/land cover changes in the Clay Belt region of Northern Ontario, Canada. Despite the drive to convert forests to agricultural land, detailed information on land use changes and the resulting impacts on soil carbon and greenhouse gas (GHG) emissions in the region is lacking. Therefore, this work aims to address the information gap by estimating the amount of land cover changes. The study is driven by the urgent need to develop suitable methodologies for detecting and mapping land cover dynamics in Northern Ontario for forest and agricultural lands. Predominant land cover classes in the study area are mapped in order to quantify the changes from 2002 to 2022. Drawing on nascent technology and tools such as machine learning and Google Earth Engine's cloud computing, the satellite images from Landsat and Sentinel are used to examine the trend of land cover and land use changes. This study proposes a reliable methodology of multisensor fusion with data free of cloud contamination—a method which can be deployed anywhere for large-scale monitoring—yielding high accuracy results for regional or national accounting of ecosystem carbon stocks and GHG emissions.
查看更多>>摘要：Semantic change detection (SCD) represents a challenging task in the interpretation of remote sensing images (RSIs), with the goal of identifying change regions and extracting semantic information from bitemporal RSIs simultaneously. The recent integration of deep neural networks leveraging multitask learning has shown promise in enhancing SCD performance. However, there is still a challenge in improving SCD performance, specifically in designing a fine-grained network structure that can handle the two subtasks of change region localization and semantic information recognition in parallel. In this context, a novel multitask Siamese network, termed EGMS-Net, is proposed to boost the performance of SCD, which consists of three core components. First, a coarse-to-fine multitask Siamese network is constructed to obtain semantic information and change information at multiple levels. Second, an adaptive change information enhancement method based on spatial-spectral collaborative attention mechanism is proposed, which can assist the accurate localization of change regions without significantly increasing the model parameters. Third, a change information guidance module is developed to strengthen the interaction between multitask branches and reduce the difficulty of network training. Experiments on three benchmark datasets demonstrate that the proposed EGMS-Net outperforms existing state-of-the-art methods in the SCD community.
查看更多>>摘要：Infrared moving small target detection is an important and challenging task in infrared search and track system, especially in the case of low signal-to-clutter ratio (SCR) and complex scenes. The spatial–temporal information has not been fully utilized, and there is a serious imbalance in their exploitation, especially the lack of long-term temporal characteristics. In this article, a novel method based on the spatial–temporal feature fusion tensor model is proposed to solve these problems. By directly stacking raw infrared images, the sequence can be transformed into a third-order tensor, where the spatial–temporal features are not reduced or destroyed. Its horizontal and lateral slices can be viewed as 2-D images, showing the change of gray values of horizontal/vertical fixed spatial pixels over time. Then, a new tensor composed of several serial slices are decomposed into low-rank background components and sparse target components, which can make full use of the temporal similarity and spatial correlation of background. The partial tubal nuclear norm is introduced to constrain the low-rank background, and the tensor robust principal component analysis problem is solved quickly by the alternating direction method of multipliers. By superimposing all the decomposed sparse components into the target tensor, small target can be segmented from the reconstructed target image. Experimental results of synthetic and real data demonstrate that the proposed method is superior to other state-of-the-art methods in visual and numerical results for targets with different sizes, velocities, and SCR values under different complex backgrounds.
查看更多>>摘要：In future multiagent Mars detection schemes, the Mars helicopter can assist the scientific missions of Mars rovers by providing navigation information and scientific objects. However, Mars surface exhibits a complex topography with diverse objects and similar textures to the background, posing a great challenge for existing CNN-based object detection networks. In this article, we propose a novel deep learning-based object detection framework, AirFormer, for Mars helicopter. AirFormer embeds a new feature-fusion attention module, MAT, which injects various receptive field sizes into labels. This fusion module is capable of capturing the interrelations between objects with each other while simultaneously reducing computational complexity. In addition, we published a synthetic dataset from the viewpoint of the Mars helicopter: SynMars-Air, which refers to the data collected by the ZhuRong rover. Extensive experiments are conducted to validate the performance of AirFormer compared to SOTA methods. The results show that our method achieved the highest accuracy both on synthetic and real Mars landscapes.
查看更多>>摘要：Hyperspectral image (HSI) classification is a challenging task in remote sensing applications, aiming to determine the category of each pixel by utilizing rich spectral and spatial information in HSI. Convolutional neural networks (CNNs) have been effective in processing HSI data by extracting local features, but they are deficient in capturing global contextual information. Recently, transformer has become proficient in attending to global information due to their self-attention mechanisms, yet they may fall short in capturing multiscale features of HSI. To address these limitations, a global–local multigranularity transformer (GLMGT) network is proposed for HSI classification. The GLMGT combines CNN with the transformer to comprehensively capture multigranularity spectral and spatial features across global and local scales. Specifically, we introduce a multigranularity spatial feature extraction block to extensively extract spatial information at different granularities, including multiscale local spatial features and global spatial features. In addition, we introduce a multigranularity spectral feature extraction block to fully leverage spectral information across different granularities. The validity of the proposed method is demonstrated through experimental validation using seven publicly available datasets, which include two Chinese satellite hyperspectral datasets (ZY1-02D Huanghekou and GF-5 Yancheng) and one UAV-based hyperspectral dataset.
查看更多>>摘要：Oriented remote sensing object detection (ORSOD) has gained increasing significance in both military and civilian applications due to the necessity of accurately identifying objects with varying shapes and orientations in remote sensing data. Traditional ORSOD methods often employ fixed label assignment strategies to differentiate between positive and negative samples. However, most of them frequently overlook the impact of object shape on sample quality, leading to an imbalanced distribution of positive samples and exacerbating the inconsistency between classification and regression tasks, thereby limiting detection performance. To address these challenges, we propose a novel shape-dependent assignment (SDA) method that dynamically differentiates positive and negative samples based on object shape. It introduces a new metric for evaluating sample box quality by considering angular differences relative to ground truth (GT) boxes and adjusts the sample scoring threshold according to the aspect ratio of each GT box. In addition, we present a DIoU-adaptive weighting (DAW) module that enhances the interaction between classification and regression tasks by leveraging the distance-IoU metric. This approach not only balances the quantity of samples but also improves their quality, enabling more effective training schemes for samples of varying qualities. We validate our proposed methods through extensive experiments on three challenging ORSOD datasets: DOTA-1.0, HRSC2016, and UCAS-AOD. The results demonstrate that our approach achieves significant improvements, especially for objects with large aspect ratios.
查看更多>>摘要：The sparse characteristics of target features poses significant challenges when using deep learning methods for infrared dim small targets. To tackle this issue, this article proposes a novel multilevel sparse feature fusion network for detecting infrared dim small targets. A feature-level sparse feature fusion network fuses target features of the same level and different depths to express small target features. A decision-level sparse feature fusion network fuses features from different decision spaces to improve decision confidence. To enrich the feature representation of the target, different levels of target global features are introduced into the decision-level sparse feature fusion network. During the network training process, a deep joint supervision training strategy is proposed to supervise and train the multilevel sparse feature fusion network, aiming to fully learn the feature representation of the target. According to the experimental results, the proposed infrared dim small targets detection method outperforms existing popular methods under sparse target features.
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IEEE