RAxC: Reflexivity-based Approximate Computing techniques for efficient remote sensing

Hyperspectral images (HSI) have a huge size, which makes their processing through neural networks cumbersome. We propose novel approximate computing techniques that leverage physical properties of the reflectance spectra to accelerate the processing of HSI images. This makes the images interpretable across various applications. We propose three spectral dimensionality reduction techniques. These techniques use spectral clustering methods that rely on reflectance values to capture inherent characteristics from hyperspectral images across diverse domains. We also evaluate existing spatial dimension reduction techniques and a combination of spatial + spectral dimension reduction techniques. We conduct extensive experiments on three real-world open-source datasets, encompassing urban and rural landscapes. Our techniques reduce the training time by up to 8x and inference time by up to 5x, while reducing the model size by up to 3x. Our techniques have a negligible impact on accuracy. By contrast, PCA and MNF techniques incur 3X higher pre-processing latency overheads than our techniques and also degrade the accuracy. Our techniques are promising for addressing the computational challenges of HSI processing.