Subpixel Change Detection Based on Improved Abundance Values for Remote Sensing Images
Author:
Affiliation:
1. College of Civil Engineering, Hefei University of Technology, Hefei, China
2. Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Hong Kong
Funder
Natural Science Foundation of Anhui Province, China
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Atmospheric Science,Computers in Earth Sciences
Link
http://xplorestaging.ieee.org/ielx7/4609443/9656571/09961878.pdf?arnumber=9961878
Reference45 articles.
1. Class Allocation for Soft-Then-Hard Subpixel Mapping Algorithms With Adaptive Visiting Order of Classes
2. Unsupervised Change Detection With Expectation-Maximization-Based Level Set
3. A Novel Approach to Subpixel Land-Cover Change Detection Based on a Supervised Back-Propagation Neural Network for Remotely Sensed Images With Different Resolutions
4. Land Cover Change Detection at Subpixel Resolution With a Hopfield Neural Network
5. Subpixel Land Cover Mapping by Integrating Spectral and Spatial Information of Remotely Sensed Imagery
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