Estimating impervious surfaces by linear spectral mixture analysis under semi-constrained condition
- DOI
- 10.2991/rsete.2013.87How to use a DOI?
- Keywords
- inpervious surface; LSMA; semi-constrained condition
- Abstract
Impervious surface played an important role in monitoring urban sprawl and understanding human activities. Linear spectral mixture analysis (LSMA) is commonly used to estimate impervious surface due to its simple structure and clear physical meaning. But previous researches found that LSMA seems to overestimate slightly impervious surface fraction in less developed areas (0–20%), while underestimating it in the central business district (CBD) (over 80%). This paper using LSMA model, under fully constrained and semi-constrained condition, developed impervious surface of Fujin town, Heilongjiang Province from the Landsat Thematic Mapper (TM) image. Accuracy evaluation was estimated between town and rural areas under the two different constraints. The results indicated that impervious surface developed by four endmembers(high albedo, low albedo, soil, and vegetation) under fully constrained and semi-constrained conditions overestimated slightly in less developed areas. Impervious surface developed by three endmembers (high albedo, soil, and vegetation) under semi-constrained condition provided a fine performance with a RMS reduced from 19.79% to 17.73%.
- Copyright
- © 2013, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Zhu Honglei AU - Li Ying AU - Fu Bolin PY - 2013/08 DA - 2013/08 TI - Estimating impervious surfaces by linear spectral mixture analysis under semi-constrained condition BT - Proceedings of the 2013 the International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE 2013) PB - Atlantis Press SP - 355 EP - 358 SN - 1951-6851 UR - https://doi.org/10.2991/rsete.2013.87 DO - 10.2991/rsete.2013.87 ID - Honglei2013/08 ER -