Remote sensing image changedetection has important applications in many fields. However, current studies mostly focus on identifying pixel-levelchanges. Although these methods can achieve better performance, this pa...
详细信息
ISBN:
(纸本)9798350360332;9798350360325
Remote sensing image changedetection has important applications in many fields. However, current studies mostly focus on identifying pixel-levelchanges. Although these methods can achieve better performance, this paradigm fails to determine changes in specific object instances due to the definition of the task itself. For this reason, we conduct preliminary exploration and propose a method named OBJ-CD, which can detect the changes of object instance. Specifically, OBJ- CD initially employs a lightweight Siamese object detector to detect objects within two temporal images. Subsequently, OBJ-CD calculates the metric matrix for the detected objects in these images. Finally, the conditions of the object instance change are limited by a certain threshold, and the final object-level change detection results can be obtained. We conducted several experiments on our constructed dataset, and the experimental results indicate that the proposed method can achieve object-level change detection with good performance.
Remote sensing and computer vision technologies are increasingly leveraged for rapid post-disaster building damage assessment, becoming a crucial and practical approach. In this context, the accuracy of employing vari...
详细信息
Remote sensing and computer vision technologies are increasingly leveraged for rapid post-disaster building damage assessment, becoming a crucial and practical approach. In this context, the accuracy of employing various AI models in pixel-levelchangedetection methods is significantly dependent on the consistency between pre- and post-disaster building images, particularly regarding variations in resolution, viewing angle, and lighting conditions;in object-level feature recognition methods, the low richness of semantic details of damaged buildings in images leads to a poor detection accuracy. This paper proposes a novel method, OCD-BDA (object-level change detection for Post-Disaster Building Damage Assessment), as an alternative to pixel-levelchangedetection and object-level feature recognition methods. Inspired by human cognitive processes, this method incorporates three key steps: an efficient sample acquisition for object localization, labeling via HGC (Hierarchical and Gaussian Clustering), and model training and prediction for classification. Furthermore, this study establishes a changedetection dataset based on Google Earth imagery of regions in Hatay Province before and after the Turkish earthquake. This dataset is characterized by pixel inconsistency and significant differences in photographic angles and lighting conditions between pre- and post-disaster images, making it a valuable test dataset for other studies. As a result, in the experiments of comparative generalization capabilities, OCD-BDA demonstrated a significant improvement, achieving an accuracy of 71%, which is twice that of the second-ranking method. Moreover, OCD-BDA exhibits superior performance in tasks with small sample amounts and rapid training time. With only 1% of the training samples, it achieves a prediction accuracy exceeding that of traditional transfer learning methods with 60% of samples. Additionally, it completes assessments across a large disaster area (450 km(2)) with 93
changedetection aims to reveal the changes of specific regions or objects in a time series. object-level change detection methods are more suitable for existing needs because they can locate and identify changed obje...
详细信息
changedetection aims to reveal the changes of specific regions or objects in a time series. object-level change detection methods are more suitable for existing needs because they can locate and identify changed objects more accurately. Attempting to solve the problems in existing object-level change detection methods, such as ignoring the relationship between dual-branch features, insufficient utilization of feature point information, and unreasonable fusion weight allocation mechanism, this article proposes an object-level change detection network, CGA-Net, based on metric, which combines similarity measurement with the feature extraction and fusion. Specifically, the feature exchange module (FEM) exchanges the feature point information of the dual branches under the guidance of cosine similarity;the feature aggregation module driven by graph attention network (GAT) aggregates features locally and globally using cosine similarity and GAT;the dual-time feature fusion module assigns weights to different parts for fusion based on feature similarity and correlation. The experimental results show that CGA-Net exhibits excellent performance on the LEVIR-CD and WHU-CD datasets. Our method achieves 94.20% and 86.85% in mAP@0.5 on the two datasets, respectively, and 75.12% and 77.39% in mAP@0.5:0.95, respectively, which has a significant improvement compared to both the algorithms based on bounding boxes and the algorithms based on masks. It is fully proven that CGA-Net can effectively improve the accuracy of changedetection in different scenarios.
In the realm of remote sensing changedetection, deep learning-based pixel-level methods have shown commendable accuracy and speed. However, due to the difficulty in distinguishing between each changed object and the ...
详细信息
ISBN:
(纸本)9798350360332;9798350360325
In the realm of remote sensing changedetection, deep learning-based pixel-level methods have shown commendable accuracy and speed. However, due to the difficulty in distinguishing between each changed object and the high matching accuracy required, there are still limitations in practical applications. To address these issues, we propose change DINO, a novel unified object-level change detection and segmentation framework and the inaugural Transformer-based object-level change detection framework, which leverages the Hierarchical Temporal Fusion Module (HTFM) with dual branches to extract change features from bi-temporal images, integrating these features into the Transformer's encoder-decoder and segmentation branches. Experimental results show that compared to other pixel-level (including Transformer-based) changedetection methods, change DINO exhibits superior performance even on pixel-level evaluation strategy, achieving F1 score improvements of 5.09% and 10.31% compared with Transformer-based methods. This capability significantly mitigates the limitations inherent in pixel-leveldetection, showcasing change DINO's substantial potential for diverse applications in changedetection tasks.
暂无评论