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Land Cover Change Detection via Semantic Segmentation
This paper presents a change detection method that identifies land cover...
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High Resolution Semantic Change Detection
Change detection is one of the main problems in remote sensing, and is e...
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Hi-UCD: A Large-scale Dataset for Urban Semantic Change Detection in Remote Sensing Imagery
With the acceleration of the urban expansion, urban change detection (UC...
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Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks
The Copernicus Sentinel-2 program now provides multispectral images at a...
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Multi-Temporal Scene Classification and Scene Change Detection with Correlation based Fusion
Classifying multi-temporal scene land-use categories and detecting their...
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Unsupervised Change Detection in Multi-temporal VHR Images Based on Deep Kernel PCA Convolutional Mapping Network
With the development of Earth observation technology, very-high-resoluti...
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Hierarchical Paired Channel Fusion Network for Street Scene Change Detection
Street Scene Change Detection (SSCD) aims to locate the changed regions ...
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Asymmetric Siamese Networks for Semantic Change Detection
Given two multi-temporal aerial images, semantic change detection aims to locate the land-cover variations and identify their categories with pixel-wise boundaries. The problem has demonstrated promising potentials in many earth vision related tasks, such as precise urban planning and natural resource management. Existing state-of-the-art algorithms mainly identify the changed pixels through symmetric modules, which would suffer from categorical ambiguity caused by changes related to totally different land-cover distributions. In this paper, we present an asymmetric siamese network (ASN) to locate and identify semantic changes through feature pairs obtained from modules of widely different structures, which involve different spatial ranges and quantities of parameters to factor in the discrepancy across different land-cover distributions. To better train and evaluate our model, we create a large-scale well-annotated SEmantic Change detectiON Dataset (SECOND), while an adaptive threshold learning (ATL) module and a separated kappa (SeK) coefficient are proposed to alleviate the influences of label imbalance in model training and evaluation. The experimental results demonstrate that the proposed model can stably outperform the state-of-the-art algorithms with different encoder backbones.
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