A Semi-Supervised Algorithm to Map Major Vegetation Zones Using Satellite Hyperspectral Data

Continuous mapping of vegetation zones is vital to identify ecological changes as early as possible. In this paper, a novel, semi-supervised method is discussed that can be used to map boundary between two vegetation zones using satellite hyperspectral data. For this task, a hyperspectral image strip of Sri Lanka obtained by the Earth Observing-1 (EO-1) satellite’s Hyperion sensor is used. In the proposed method, a Maximum Likelihood Classification is used to identify the pure vegetation pixels. Then, based on the degree of correlation among pixels containing vegetation, at different spatial coordinates, a possible region of existence of a boundary between the two major vegetation zones is obtained. Thereafter, a systematic procedure consisting of Fisher’s Discriminant Analysis (FDA) and Spectral Clustering is used to separate the vegetation pixels in the narrowed down region into two vegetation zones. The results are validated through data obtained from the Survey Department of Sri Lanka.

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