Fine Land Cover Classification Using Daily Synthetic Landsat-Like Images at 15-m Resolution

PROJECT TITLE :

Fine Land Cover Classification Using Daily Synthetic Landsat-Like Images at 15-m Resolution

ABSTRACT:

There's currently no unified remote sensing system on the market which will simultaneously manufacture images with fine spatial, temporal, and spectral resolutions. This letter proposes a unified spatiotemporal spectral mixing model using Landsat Enhanced Thematic Mapper Plus and Moderate Resolution Imaging Spectroradiometer images to predict artificial daily Landsat-like information with a 15-m resolution. The results of tests using both simulated and actual knowledge over the Poyang Lake Nature Reserve show that the model will accurately capture the general trend of changes for the anticipated period and will enhance the spatial resolution of the information, while at the same time preserving the original spectral data. The proposed model is also applied to boost land cover classification accuracy. The application in Wuhan, Hubei Province shows that the classification accuracy is markedly improved. With the integration of dense temporal characteristics, the user and producer accuracies for land cover varieties also are improved.

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