Land-Cover Classification of Remotely Sensed Images using Compressive Sensing Having Severe Scarcity of Labeled Patterns

The aim of this letter is twofold. First, we assess the compressive sensing (CS) approach as a classification tool for multispectral remote sensing images, assuming severe scarcity of training samples (at most, ten for each class). Then, we propose a new strategy to perform domain adaptation using a CS approach for classifying images at large spatial scales (continental mapping). In particular, the “most confusing” training samples in the target domain are collected by exploiting plenty of training samples available in the source domain under the transfer learning framework. For assessing the proposed method, experiments are performed on three remotely sensed images captured by the Landsat 8 satellite in different regions of India. Results obtained using the proposed approach are found to be promising.