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Article

6 Citations

4,706 Views

22 Pages

An Unsupervised Method of Change Detection in Multi-Temporal PolSAR Data Using a Test Statistic and an Improved K&I Algorithm

Jinqi Zhao,
Jie Yang,
Zhong Lu,
Pingxiang Li and ,
Wensong Liu and
Le Yang
+ 1 author+ 0 author

Appl. Sci.2017, 7(12), 1297;https://doi.org/10.3390/app7121297

13 December 2017

In recent years, multi-temporal imagery from spaceborne sensors has provided a fast and practical means for surveying and assessing changes in terrain surfaces. Owing to the all-weather imaging capability, polarimetric synthetic aperture radar (PolSA...

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Article

9 Citations

4,221 Views

25 Pages

Extraction and Classification of Flood-Affected Areas Based on MRF and Deep Learning

Jie Wang,
Bensheng Huang and
Fuming Wang

Water2023, 15(7), 1288;https://doi.org/10.3390/w15071288

24 March 2023

Floods can cause huge damage to society, the economy, and the environment. As a result, it is vital to determine the extent and type of land cover in flooded areas quickly and accurately in order to facilitate disaster relief and mitigation efforts....

(This article belongs to the Section New Sensors, New Technologies and Machine Learning in Water Sciences)

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