Evaluating the impacts of atmospheric correction, seasonality, environmental settings, and multi-temporal images on subpixel urban impervious surface area mapping with Landsat data

ISPRS Journal of Photogrammetry and Remote Sensing

Volumn 133, Issue , 2017, Pages 89-103

  Deng, Chengbin ^a   Li, Chaojun ^a   Zhu, Zhe ^b   Lin, Weiying ^a   Xi, Li ^a  

^a BINGHAMTON UNIVERSITY   (United States)

^b TEXAS TECH UNIVERSITY   (United States)

Author keywords

Atmospheric correction; Impervious surface; Landsat; Multi temporal images; Random forest; Seasonality

Indexed keywords

ATMOSPHERIC THERMODYNAMICS; DECISION TREES; IMAGE ENHANCEMENT; LEARNING SYSTEMS; PIXELS; REFLECTION;

ATMOSPHERIC CORRECTIONS; IMPERVIOUS SURFACE; LANDSAT; MULTI-TEMPORAL IMAGE; RANDOM FORESTS; SEASONALITY;

MAPPING;

ATMOSPHERIC CORRECTION; CLIMATE CONDITIONS; ENVIRONMENTAL CONDITIONS; HETEROGENEITY; IMAGE ANALYSIS; LAND COVER; LANDSAT; MACHINE LEARNING; MAPPING METHOD; PIXEL; SATELLITE DATA; SATELLITE IMAGERY; SEASONALITY; SURFACE AREA; SURFACE REFLECTANCE; TOP OF ATMOSPHERE; URBAN AREA;

MEDITERRANEAN REGION;

EID: 85031095970     PISSN: 09242716     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isprsjprs.2017.09.015     Document Type: Article

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