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|Title||Land Cover Mapping over Urban Areas with Rich Texture Using Multitemporal TerraSAR-X Datasets|
|Investigator||JIANG, Mi - Hohai Univeristy, School of Earth Science and Engineering|
The objective of this project will focus on the applications of TerraSAR-X data for land surface classification and change detection based on Multitemporal (MT)TerraSAR-X datasets and advanced InSAR techniques. More specifically, the project will study:(1) MT - InSAR covariance matrix estimation over urban areas with rich textures; (2) Land cover classification by combining optical remote sensing and TerraSAR-X datasets; (3) A long-term scheme aimed at observing the urban changes with TerraSAR-Xdata at year scales.
This project contributes an assessment of the potential of TerraSAR-X images in classifying land cover within and around urban areas and in monitoring their changes. The decision task is performed on a pixel basis and is carried out by different classification algorithms fed by radar image features including backscattering intensity, coherence and textural parameters. The extension is also considered by integrating both optical and MT radar images. The methodology consists of three main parts: parameter estimation of MT-InSAR over areas with rich textures, selection of information layers and classifiers and integration of optical and radar images to classification and change detection.
We focus our study on Yangtze River Delta, centered at E118.77, N32.04 in eastern China. The other test site, Pearl River Delta, centered at E114.10, N22.55 in southern China will be considered as an alternative area. Both areas have much in common since they are one of the fastest-growing areas in China. However, the meteorological condition in two cities is quite different. Yangtze River Delta has a humid subtropical climate and the four seasons are distinct. Pearl River Delta is situated about a degree south of the Tropic of Cancer, due to the Siberian anticyclone, it has a warm, monsoon-influenced, humid subtropical climate. This implies different surface features for long-term monitoring and therefore different classification strategies. When assuming one scene for each of the two areas for each satellite pass, a total of about 300 TerraSAR-X scenes will be acquired for the project.
This project is funding bythe Fundamental Research Funds for the Central Universities (Grant No. 2013/B15020007) and the Natural Science Foundation of China (Grant No. 41404009).Scientific publications and reports will be prepared as the deliverables.
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