Simplex Projection for Land Cover Information Mining from Landsat-5 TM Data

In this paper we explore the efficacy of simplex projection for land cover (LC) information mining. LC is the observed biophysical cover on the surface of the Earth and describes how much of a region is covered by forests, wetlands, impervious surfaces, etc. LC information can be extracted by processing remotely sensed data acquired through sensors mounted either on space borne satellites or aircrafts. Since these data are a mixture of more than two LC class types, unmixing algorithms based on linear mixture model such as simplex projection, aims to resolve the different components of mixed pixels in the data. This method is based on the equivalence of the fully constrained least squares problem of projecting a point onto a simplex. The algorithm does not perform optimization and is analytical, thus reducing the computational complexity. The algorithm is tested on computer-simulated data of various signal to noise ratio and Landsat-5 TM data of an agricultural landscape and an urban scenario. The results are validated using descriptive statistics, correlation coefficient, root mean square error, probability of success and bivariate distribution function.