Characterizing Hyperspectral Data Layouts: Performance and Energy Efficiency in Embedded GPUs for PCAbased Dimensionality Reduction The increasing demand for HyperSpectral Imaging (HSI) applications has pushed the appearance of new, smaller and more affordable HS cameras. Their adoption in embedded systems with realtime and energy constraints open research questions given the huge amounts of data involved. Traditionally, this issue is tackled with dimensionality reduction algorithms as Principal Component Analysis (PCA). In this regard, a highly impacting factor in performance is the data layout used to store the 3D HS image into a 1D memory. In this paper, the impact of using the three main HS data layouts (BSQ, BIP and BIL) is analyzed in terms of performance and energy efficiency for two PCA methods in the embedded NVIDIA Jetson TX1 GPUbased accelerator. Results show the bottlenecks found for each layout along with architectural insights that explain the observed behaviours. Consequently, we provide a set of recommendations to select a suitable PCA method for best performance and energy efficiency, depending on the number of principal components required. Additionally, we observe how different orderings with similar results account for a much different developeru0027s productivity given the involved coding complexity in each case.

LowComplexity Adaptive Switched PredictionBased Lossless Compression of TimeLapse Hyperspectral Image Data Timelapse hyperspectral image (HSI) data has an enormous size and demands lossless compression for most of the high fidelity applications. In literature, spatial and spectral correlations in HSI are widely studied and used for compression. We propose a novel adaptive switched predictionbased scheme, which efficiently exploits temporal correlations in addition to spatial, and spectral correlations. The predictor switching uses a threshold, which is chosen based on the residual error distribution of already encoded band. Hence, our method does not need any overhead to be transmitted. The proposed scheme outperforms other stateoftheart methods in bitrate, and the method is computationally efficient too.