An Efficient Compressive Learning Method on Earth Observation Data

Abstract

Compressive learning (CL) for Synthetic Aperture Radar (SAR) refers to the use of Compressive Sensing (CS) to reduce the amount of data required to represent SAR images while preserving key image features, with the goal of improving efficiency and lowering computational costs. In this paper, we propose a new, highly efficient RS technique based on creating a transcription between several classes. The proposed method is based on a novel CL theory, which is a joint signal processing and machine learning framework for inference from a signal that is represented by a small number of measurements obtained via linear projections of the data without first reconstructing the data. The results showed that, by minimizing the number of measurements or pixels in a data set, the accuracy curve will change depending on the data set and the method that is used. The algorithm reached an accuracy of about 80 % on SAR data, when using a SVM as classifier and a Binary sensing matrix when the number of pixels is reduced to 1/8 of the whole data.