Urban area detection from high-spatial resolution remote sensing imagery using Markov random field-based region growing-Reference-Cited by-同舟云学术

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Urban area detection from high-spatial resolution remote sensing imagery using Markov random field-based region growing

Published:2014-08-15 Issue:1 Volume:8 Page:083566
ISSN:1931-3195
Container-title:Journal of Applied Remote Sensing
language:en
Short-container-title:J. Appl. Remote Sens

Author:

Zheng Chen¹,Wang Leiguang²,Zhao Hui³,Chen Xiaohui⁴

Affiliation:

1. Henan University, School of Mathematics and Information Sciences, Jinming Road, Kaifeng 475000, China

2. Southwest Forestry University, School of Forestry, Panlong District, Kunming 650224, China

3. Henan University, School of Software, Jinming Road, Kaifeng 475000, China

4. Henan University, Library, Jinming Road, Kaifeng 475000, China

Publisher

SPIE-Intl Soc Optical Eng

Subject

General Earth and Planetary Sciences

Reference31 articles.

1. Classification and feature extraction for remote sensing images from urban areas based on morphological transformations

2. Detection of urban expansion in an urban-rural landscape with multitemporal QuickBird images

3. Robust Extraction of Urban Area Extents in HR and VHR SAR Images

4. Classification and Extraction of Spatial Features in Urban Areas Using High-Resolution Multispectral Imagery

5. Comparative Study on the Performance of Multiparameter SAR Data for Operational Urban Areas Extraction Using Textural Features

Cited by 8 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Individual Tree Crown Delineation From UAS Imagery Based on Region Growing by Over-Segments With a Competitive Mechanism;IEEE Transactions on Geoscience and Remote Sensing;2022

2. High Spatial Resolution Remote Sensing Imagery Classification Based on Markov Random Field Model Integrating Granularity and Semantic Features;Pattern Recognition and Computer Vision;2022

3. Rule-based, hierarchical land use and land cover classification of urban and peri-urban agriculture in data-poor regions with RapidEye satellite imagery: a case study of Nakuru, Kenya;Journal of Applied Remote Sensing;2019-02-09

4. Urban Area Detection in Very High Resolution Remote Sensing Images Using Deep Convolutional Neural Networks;Sensors;2018-03-18

5. Super-Pixel Based Segmentation of Urban Area and Its Detection Using Machine Learning Techniques;Lecture Notes in Networks and Systems;2017-11-10

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