Approximate Computing of Remotely Sensed Data: SVM Hyperspectral Image Classification as a Case Study

Onboard processing systems are becoming very important in remote sensing data processing. However, a main problem with specialized hardware architectures used for onboard processing is their high power consumption, which limits their exploitation in earth observation missions. In this paper, a novel strategy for approximate computing is proposed for reducing energy consumption in remotely sensed onboard processing tasks. As a case study, the implementation of support vector machine (SVM) hyperspectral image classification is considered by using the proposed approximate computing framework. Experimental results show that the proposed approximate computing scheme achieves up to 70% power savings in the kernel accumulation computation procedure with negligible degradation of classification accuracy as compared to the traditional ripple carry adder (RCA) precise computation. This is an important achievement to meet the restrictions of onboard processing scenarios.