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When Vectorization Meets Change Detection
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AI
DOI：10.1109/TGRS.2023.3347661.png)
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Abstract
En 中文
In long-term Earth observation, change detection (CD) is a crucial and intricate task with applications spanning diverse fields, including land resource planning and natural disaster monitoring. Most existing CD approaches typically output segmentation results in raster format. However, raster format results suffer from higher memory usage, poorer shape accuracy, magnified distortions, and challenges in topological editing. To address the issues of raster format, we propose a novel end-to-end change vectorization network (CVNet), which is the first attempt to extract changes using vector format. The CVNet directly learns the vector components of changed objects and uses them to construct vectors. Specifically, since the vectorization of CD faces the inherent imbalance between changed and unchanged samples, we first introduce the Change-Collector to collect the changed regions and combine them into more compact samples. Next, the vector components learning model (VCLM) is introduced to capture the fundamental components for constructing the vectors, including change maps, junction positions, and segmentation masks. Finally, the changed instances obtained from the masks are used to divide and connect junctions to generate the vector output. To verify the effectiveness of the proposed framework, we construct two building change vectorization datasets by modifying the WHU-CD and LEVIR-CD benchmarks. Experimental results demonstrate that the CVNet outperforms the existing postprocess vectorization methods in terms of the visual effect and all evaluation metrics. The dataset and source code will be made publicly available at https://github.com/yyyyll0ss/CVNet.
Keywords:
Junctions
Remote sensing
Buildings
Shape
Task analysis
Feature extraction
Data mining
Change detection (CD)
convolutional neural network (CNN)
multitask learning
remote sensing image vectorization
Journal
IF:
8.6
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2.1W
Citations:
10.7W
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