Earthquake-Damaged Buildings Detection in Very High-Resolution Remote Sensing Images Based on Object Context and Boundary Enhanced Loss

Wang, Chao; Qiu, Xing; Huan, Hai; Wang, Shuai; Zhang, Yan; Chen, Xiaohui; He, Wei

doi:10.3390/rs13163119

Cited by 11 publications

(4 citation statements)

References 42 publications

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“…This third method is a deep learning method based on object context and boundary enhanced loss (OCR-BE). In this method, a novel loss function, BE loss, is designed according to the distance between pixels and the boundary, forcing the network to pay more attention to learning of boundary pixels [35]. The fourth method is a deep learning method based on UNet 3+ (UNet 3+).…”

Section: Experimental Settings and Methods For Comparisonmentioning

confidence: 99%

A Detection Method for Collapsed Buildings Combining Post-Earthquake High-Resolution Optical and Synthetic Aperture Radar Images

et al. 2022

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The detection of collapsed buildings based on post-earthquake remote sensing images is conducive to eliminating the dependence on pre-earthquake data, which is of great significance to carry out emergency response in time. The difficulties in obtaining or lack of elevation information, as strong evidence to determine whether buildings collapse or not, is the main challenge in the practical application of this method. On the one hand, the introduction of double bounce features in synthetic aperture radar (SAR) images are helpful to judge whether buildings collapse or not. On the other hand, because SAR images are limited by imaging mechanisms, it is necessary to introduce spatial details in optical images as supplements in the detection of collapsed buildings. Therefore, a detection method for collapsed buildings combining post-earthquake high-resolution optical and SAR images was proposed by mining complementary information between traditional visual features and double bounce features from multi-source data. In this method, a strategy of optical and SAR object set extraction based on an inscribed center (OpticalandSAR-ObjectsExtraction) was first put forward to extract a unified optical-SAR object set. Based on this, a quantitative representation of collapse semantic knowledge in double bounce (DoubleBounceCollapseSemantic) was designed to bridge a semantic gap between double bounce and collapse features of buildings. Ultimately, the final detection results were obtained based on the improved active learning support vector machines (SVMs). The multi-group experimental results of post-earthquake multi-source images show that the overall accuracy (OA) and the detection accuracy for collapsed buildings (Pcb) of the proposed method can reach more than 82.39% and 75.47%. Therefore, the proposed method is significantly superior to many advanced methods for comparison.

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Section: Experimental Settings and Methods For Comparisonmentioning

confidence: 99%

A Detection Method for Collapsed Buildings Combining Post-Earthquake High-Resolution Optical and Synthetic Aperture Radar Images

et al. 2022

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“…When images are at a higher spatial resolution, more pixels are included in an image of the same object and thus more details are found [65]. That's why, this paper analyzed the impact of resolution changes on OA.…”

Section: Analysis Of the Influence Of The Image Resolutionmentioning

confidence: 99%

Zero-Shot Remote Sensing Scene Classification Method Based on Local-Global Feature Fusion and Weight Mapping Loss

Wang,

Li,

Tanvir

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Zero-shot remote sensing scene classification refers to making the model to have the ability to identify the unseen class scenes based on seen class scenes, and has become a research hotspot in the field of remote sensing. Contemporary approaches in zero-shot remote sensing scene classification primarily focus on extracting global information from scenes, neglecting nuanced local landscape features. This oversight diminishes the discriminative capabilities of recognition models. Furthermore, these methods overlook the semantic relevance between seen and unseen class scenes in training, leading to reduced emphasis on learning from varied scenes and subsequent declines in classification performance. To address these challenges, this paper proposes the "Zero-Shot Remote Sensing Scene Classification Method Based on Local-Global Feature Fusion and Weight Mapping Loss (LGFFWM)." The design incorporates a local-global feature fusion (LGFF) module enabling adaptive labeling and feature modeling of internal local landscapes, effectively merging them with global features for a more discriminative representation of remote sensing scenes. Furthermore, a weight mapping loss (WM Loss) function is introduced, leveraging a semantic correlation matrix to compel the model to prioritize learning seen class scenes that exhibit strong correlations with unseen class scenes by assigning higher training weights. Extensive experiments have been conducted on classical remote sensing scene datasets, including UCM, AID, and NWPU, demonstrate the superiority of the proposed LGFFWM method over ten advanced comparative methods, yielding overall accuracy improvements of over 2.25%, 3.47%, and 0.44%, respectively. Additional experiments on the SIRI-WHU and RSSCN7 datasets underscore the transferability of LGFFWM, achieving overall accuracies of 53.50% and 47.37%, respectively.

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“…In the experiments, Precision (P), Recall (R), F1-score (F1) and IoU were employed as evaluation indicators, and their respective formulas can be found in literature [51]. Given the greater comprehensive of IoU and F1 compared to P and R [52], our experimental comparison will focus primarily on the first two indicators.…”

Section: A Experimental Designmentioning

confidence: 99%

A Label Correction Learning Framework for Gully Erosion Extraction Using High-Resolution Remote Sensing Images and Noisy Labels

Zhao,

Shen,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Rapid and reliable gully erosion (GE) extraction from high-resolution remote sensing (HRRS) images is crucial for the development of land protection measures. For this task, semantic segmentation methods are widely considered state-of-theart solutions. Nevertheless, providing sufficient and clean training labels for segmentation models demands substantial expenses and time investment. In this context, a label correction learning (LCL) framework is proposed to effectively extract GE from HRRS images by leveraging more readily available noisy labels. The core objective of this framework is to suppress the adverse impact of noise within noisy labels on the model performance. To achieve this, we introduce three key components in the framework, including an adaptive correction loss function, a multitree refinement module, and a noise correction module. These components collaborate to rectify noisy labels during training, thereby providing the model with a training set containing less noise. To validate the effectiveness of the LCL framework, three severely eroded regions in Northeast China are selected as study areas and corresponding noisy datasets are generated. Extensive experiments on these datasets demonstrate that our framework can significantly mitigate the negative influence of label noise and ultimately achieve superior GE extraction performance. Moreover, by employing the proposed framework, we generate GE coverage maps for the study areas and obtain measurements of gully area and length that are very close to the true statistics. Such a framework that can effectively learn from noisy labels holds promise as a practical and cost-efficient means to provide reliable data references for land resource protection.

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Earthquake-Damaged Buildings Detection in Very High-Resolution Remote Sensing Images Based on Object Context and Boundary Enhanced Loss

Cited by 11 publications

References 42 publications

A Detection Method for Collapsed Buildings Combining Post-Earthquake High-Resolution Optical and Synthetic Aperture Radar Images

A Detection Method for Collapsed Buildings Combining Post-Earthquake High-Resolution Optical and Synthetic Aperture Radar Images

Zero-Shot Remote Sensing Scene Classification Method Based on Local-Global Feature Fusion and Weight Mapping Loss

A Label Correction Learning Framework for Gully Erosion Extraction Using High-Resolution Remote Sensing Images and Noisy Labels

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