Scene Changes Understanding Framework Based on Graph Convolutional Networks and Swin Transformer Blocks for Monitoring LCLU Using High-Resolution Remote Sensing Images

Yang, Sihan; Song, Fei; Jeon, Gwanggil; Sun, Rui

doi:10.3390/rs14153709

Cited by 7 publications

(2 citation statements)

References 28 publications

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“…Supervised learning methods for detecting land cover change require a large amount of labeled data and are widely utilized in the field of LCCD. Various algorithms have been proposed by researchers to address different challenges in this domain, and these methods are primarily categorized into two types: pixel-based and object-based approaches [64][65][66][67][68][69]. This section reviews and discusses in depth the existing methods of these two classes.…”

Section: Supervised Learning Methodsmentioning

confidence: 99%

The Use of Artificial Intelligence and Satellite Remote Sensing in Land Cover Change Detection: Review and Perspectives

Gu,

Zeng

2023

Sustainability

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The integration of Artificial Intelligence (AI) and Satellite Remote Sensing in Land Cover Change Detection (LCCD) has gained increasing significance in scientific discovery and research. This collaboration accelerates research efforts, aiding in hypothesis generation, experiment design, and large dataset interpretation, providing insights beyond traditional scientific methods. Mapping land cover patterns at global, regional, and local scales is crucial for monitoring the dynamic world, given the significant impact of land cover distribution on climate and environment. Satellite remote sensing is an efficient tool for monitoring land cover across vast spatial extents. Detection of land cover change through satellite remote sensing images is critical in influencing ecological balance, climate change mitigation, and urban development guidance. This paper conducts a comprehensive review of LCCD using remote sensing images, encompassing exhaustive examination of satellite remote sensing data types and contemporary methods, with a specific focus on advanced AI technology applications. Furthermore, the study delves into the challenges and potential solutions in the field of LCCD, providing a comprehensive overview of the state of the art, offering insights for future research and practical applications in this domain.

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Section: Supervised Learning Methodsmentioning

confidence: 99%

The Use of Artificial Intelligence and Satellite Remote Sensing in Land Cover Change Detection: Review and Perspectives

Gu,

Zeng

2023

Sustainability

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“…To evaluate the performance of our method, we employ three 659 evaluation metrics [63]: precision (P ), recall (R), and F1-660 score (F 1). These metrics are defined as follows:…”

Section: B Evaluation Metrics 658mentioning

confidence: 99%

Local-Specificity and Wide-View Attention Network With Hard Sample Aware Contrastive Loss for Street Scene Change Detection

Guo,

2023

IEEE Access

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Following the intuitive idea of detecting changes by directly measuring dissimilarities between pairs of features, change detection methods based on feature similarity learning have emerged as a crucial field. However, large variances in the scale and location of required contextual information and heavy imbalance between easy and hard samples remain challenging issues. To address the first issue, we propose the Local-Specificity and Wide-View Attention Network (LSWVANet), which features a series of attention modules named Local-Specificity and Wide-View Attention Modules (LSWVAMs). Each LSWVAM consists of two contextual feature units: (1) the Local-Specificity Feature Pyramid unit, which extracts part-specific contexts at the fine-grained level to focus on subtle changes within local discriminative parts, and the Wide-View Feature Pyramid unit, which extracts wide-view contexts at the long-range level to highlight significant changes in large-scale regions. To tackle the second issue, we introduce a novel sample-specific loss function called Hard Sample-Aware Contrastive Loss (HSACL), which is designed to downweight easy samples from both changed and unchanged categories, thereby rapidly shifting the training focus towards the informative hard samples. We demonstrate the effectiveness of our method through experiments on three challenging datasets, VL-CMU-CD, PCD2015 and PSCD, and report the experimental results showing that our approach achieves state-of-the-art accuracy. INDEX TERMSChange detection, hard sample, feature similarity learning, attention mechanism I. INTRODUCTION 1 Street-view scene change detection (SCD) is a crucial com-2 puter vision task with a wide range of applications, in-3 cluding urban planning [1] [2] [3], traffic surveillance [4] 4 [5], abandoned object detection [6] [7], disaster evaluation 5 [8], action recognition [9] [10] and self-driving [11] [12]. 6 With the emergence of self-driving cars and robotic patrols, 7 accurate navigation and planning based on map information 8 have become increasingly important. Many researchers [1] 9 [11] [12] use street-view change detection algorithms to 10 update map information. Therefore, improving the accuracy 11 of change detection model is a critical challenge in SCD. 12 With the powerful feature representation of convolutional 13 neural networks [13] [14] [15] (CNNs), fully convolutional 14 networks [14] [16] (FCNs) have been widely used in the 15 field of change detection. FCN-based methods can be broadly 16

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Graph-based deep learning techniques for remote sensing applications: Techniques, taxonomy, and applications — A comprehensive review

Khlifi,

Boulila,

Farah

2023

Computer Science Review

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Scene Changes Understanding Framework Based on Graph Convolutional Networks and Swin Transformer Blocks for Monitoring LCLU Using High-Resolution Remote Sensing Images

Cited by 7 publications

References 28 publications

The Use of Artificial Intelligence and Satellite Remote Sensing in Land Cover Change Detection: Review and Perspectives

The Use of Artificial Intelligence and Satellite Remote Sensing in Land Cover Change Detection: Review and Perspectives

Local-Specificity and Wide-View Attention Network With Hard Sample Aware Contrastive Loss for Street Scene Change Detection

Graph-based deep learning techniques for remote sensing applications: Techniques, taxonomy, and applications — A comprehensive review

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