Continuous Change Detection and Classification Using Hidden Markov Model: A Case Study for Monitoring Urban Encroachment onto Farmland in Beijing-Reference-Cited by-同舟云学术

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Continuous Change Detection and Classification Using Hidden Markov Model: A Case Study for Monitoring Urban Encroachment onto Farmland in Beijing

Published:2015-11-13 Issue:11 Volume:7 Page:15318-15339
ISSN:2072-4292
Container-title:Remote Sensing
language:en
Short-container-title:Remote Sensing

Author:

Yuan Yuan,Meng Yu,Lin Lei,Sahli Hichem,Yue Anzhi,Chen Jingbo,Zhao Zhongming,Kong Yunlong,He Dongxu

Funder

National Natural Science Foundation of China

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

Link

http://www.mdpi.com/2072-4292/7/11/15318/pdf

Reference39 articles.

1. Review ArticleDigital change detection methods in ecosystem monitoring: a review

2. Segmentation uncertainty in multiple change-point models

3. A change detection method for sequential patterns

4. Land Cover Change Detection Using Autocorrelation Analysis on MODIS Time-Series Data: Detection of New Human Settlements in the Gauteng Province of South Africa

5. Detecting changes in vegetation trends using time series segmentation

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1. A GIS-Based Assessment of Land Cover Changes and Tree Species Suitability for Urban Greening Policies in the City of Kitwe, Zambia;Green Energy and Environmental Technology;2023-10-18

2. What would be better for urban mapping in the Klang Valley? SPOT or Sentinel-1;IOP Conference Series: Earth and Environmental Science;2022-07-01

3. Mapping the annual dynamics of land cover in Beijing from 2001 to 2020 using Landsat dense time series stack;ISPRS Journal of Photogrammetry and Remote Sensing;2022-03

4. Detecting Changes by Learning No Changes: Data-Enclosing-Ball Minimizing Autoencoders for One-Class Change Detection in Multispectral Imagery;IEEE Transactions on Geoscience and Remote Sensing;2022

5. Anomaly Detection and Classification in Multispectral Time Series Based on Hidden Markov Models;IEEE Transactions on Geoscience and Remote Sensing;2022

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