Polyline Feature Extraction for Land Cover Classification using Hyperspectral Data

Prediction of landcover types from airborne/spaceborne sensors is an important classification problem in remote sensing. Due to recent advances in sensor technology, it is now possible to acquire hyperspectral data simultaneously in ∼200 bands, each of which measures the integrated response of a target over a narrow window of the electromagnetic spectrum. This unprecedented spectral resolution can provide vastly improved mapping of several types of landcover and monitoring of ecological changes. However, the increased dimensionality also constitutes a challenge in terms of storage and analysis. This paper presents a Polyline Feature Extraction (PFE) technique that exploits the spectral correlations between certain adjacent bands in hyperspectral data, to reduce dimensionality without sacrificing discrimination power. It uses an interpretable piecewise linear representation of the data that is somewhat robust to environmental changes. Using the Binary Hierarchical Classifier Framework for multi-class problems, PFE’s effectiveness is demonstrated on two large hyperspectral datasets obtained over the Texas and Florida coasts respectively.