DCT-based Hyperspectral Image Classification on Resource-Constrained Platforms

Deep learning based approaches to hyperspectral image analysis have attracted large attention and exhibited high performance in image classification tasks. However, deployment of deep learning based hyperspectral image analysis systems is challenging due to the computational complexity of deep learning and the large amount of data involved in hyperspectral images. To address this problem, this paper introduces a novel framework that integrates deep neural network (DNN) based image analysis by learning the network from discrete cosine transform (DCT) coefficients for hyperspectral image classification. The framework allows designers to derive diverse implementation configurations using a variable number of DCT coefficients for training. These configurations can be used to flexibly trade off classification accuracy and computational cost (e.g., based on specific characteristics of the device being used or based on time-varying operating requirements). Through experiments using a publicly available remote sensing dataset and a resource constrained Android platform, we demonstrate that our proposed approach enables the streamlined deployment of DNN-based hyperspectral image classification.