Unsupervised Change Detection Using Spectrum-Trend and Shape Similarity Measure
The emergence of very high resolution (VHR) images contributes to big challenges in change detection. It is hard for traditional pixel-level approaches to achieve satisfying performance due to radiometric difference. This work proposes a novel feature descriptor that is based on spectrum-trend and s...
Main Authors: | Yi Tian, Ming Hao, Hua Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-11-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/12/21/3606 |
Similar Items
-
A Framework for Unsupervised Wildfire Damage Assessment Using VHR Satellite Images with PlanetScope Data
by: Minkyung Chung, et al.
Published: (2020-11-01) -
An Unsupervised Transformer-Based Multivariate Alteration Detection Approach for Change Detection in VHR Remote Sensing Images
by: Yizhang Lin, et al.
Published: (2024-01-01) -
A Building Shape Vectorization Hierarchy From VHR Remote Sensing Imagery Combined DCNNs-Based Edge Detection and PCA-Based Corner Extraction
by: Xiang Wen, et al.
Published: (2023-01-01) -
Multi-Difference Image Fusion Change Detection Using a Visual Attention Model on VHR Satellite Data
by: Jianhui Luo, et al.
Published: (2023-07-01) -
Building Change Detection Using a Shape Context Similarity Model for LiDAR Data
by: Xuzhe Lyu, et al.
Published: (2020-11-01)