Dynamic, Data-Driven Hyperspectral Image Classification on Resource-Constrained Platforms

Abstract

Hyperspectral image processing has attracted increasing research interest in recent years, due in part to the high spectral resolution of hyperspectral images together with the emergence of deep neural networks (DNNs) as a promising class of methods for analysis of hyperspectral images. An important challenge in realizing the full potential of hyperspectral imaging technology is the problem of deploying image analysis capabilities on resource-constrained platforms, such as unmanned aerial vehicles (UAVs) and mobile computing platforms. In this paper, we develop a novel approach for designing DNNs for hyperspectral image processing that are targeted to resource-constrained platforms. Our approach involves optimizing the design of a single DNN for operation across a variable number of spectral bands. DNNs that are developed in this way can then be adapted dynamically based on the availability of resources and real-time performance constraints. The proposed approach supports the DDDAS paradigm as an integrated part of the design and training process to enable dynamic-data driven adaptation of the DNN structure—that is, the set of computational modules and connections that are active when the DNN operates. We demonstrate the effectiveness of the proposed class of adaptive and scalable DNNs through experiments using publicly available remote sensing datasets.