Change detection from synthetic aperture radar images based on neighborhood-based ratio and extreme learning machine-Reference-Cited by-同舟云学术

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Change detection from synthetic aperture radar images based on neighborhood-based ratio and extreme learning machine

Published:2016-12-01 Issue:4 Volume:10 Page:046019
ISSN:1931-3195
Container-title:Journal of Applied Remote Sensing
language:en
Short-container-title:J. Appl. Remote Sens

Author:

Gao Feng¹,Dong Junyu¹,Li Bo²,Xu Qizhi²,Xie Cui¹

Affiliation:

1. Ocean University of China, Department of Computer Science and Technology, 238 Songling Road, Qingdao 266100, China

2. Beihang University, School of Computer Science and Engineering, Beijing Key Laboratory of Digital Media, 37 Xueyuan Road, Beijing 100191, China

Publisher

SPIE-Intl Soc Optical Eng

Subject

General Earth and Planetary Sciences

Reference30 articles.

1. SAR Image Change Detection Based on Iterative Label-Information Composite Kernel Supervised by Anisotropic Texture

2. Potential flood hazard assessment by integration of ALOS PALSAR and ASTER GDEM: a case study for the Hoa Chau commune, Hoa Vang district, in central Vietnam

3. Estimating plant area index for monitoring crop growth dynamics using Landsat-8 and RapidEye images

4. Using multitemporal Landsat imagery to monitor and model the influences of landscape pattern on urban expansion in a metropolitan region

5. Long-term effects of land use/land cover change on surface runoff in urban areas of Beijing, China

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1. Self-supervised change detection of heterogeneous images based on difference algorithms;European Journal of Remote Sensing;2024-07

2. Detecting Video Image Changes Based on Improved Difference Map and Image Fusion;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

3. Multi-scale region-level graph convolutional network for unsupervised SAR image change detection;International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024);2024-06-21

4. Change detection in SAR image based on weighted difference image generation and optimized random forest;IET Image Processing;2024-05-26

5. Synthetic aperture radar image change detection based on image difference denoising and fuzzy local information C-means clustering;Journal of Applied Remote Sensing;2024-04-02

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