Land-Use Classification with Compressive Sensing Multifeature Fusion

Abstract

In this letter, we formulate a land-use (LU) classification problem within a compressive sensing (CS) fusion framework. CS aims at providing a compact representation form after a given query image has been processed with an opportune feature extraction type. In particular, residuals are generated from the image reconstruction with dictionaries associated with the available set of possible LUs and gathered to form a single-feature image pattern. The patterns obtained from different types of features are then fused to provide the final LU estimate. Two simple fusion strategies are adopted for such purpose. As demonstrated by experiments ran on the basis of a public benchmark database, the proposed method can achieve substantial classification accuracy gains over reference methods.