Hyperspectral (HS) images hold both spatial and spectral information of an imaged scene. This allows one to take advantage of the distinct spectral signatures of materials to perform classification tasks. Since HS data are also typically very large and redundant, it is appealing to utilize compressive sensing (CS) techniques for HS acquisition. CS avoids the need for postacquisition digital compression, as the compression is inherently performed electrooptically during acquisition. We research the performance of deep learning classification applied directly on the compressive measurements. We show that by using a spectral CS technique we previously developed, it is possible to reduce the captured data by an order of magnitude without significant loss in the classification performance.
The application of compressive sensing (CS) techniques for the hyperspectral (HS) imaging is very appealing since the acquisition of HS images is demanding in terms hardware and acquisition time, and since the application of CS framework matches well the HS imaging task, which involves capturing huge amount of typically very redundant data. During the last decade, we developed several CS HS imaging systems, which have demonstrated orders of magnitude reduction of the acquisition time and of storage requirements, improved signal-to-noise ratio, and reduction of the systems’ size and weight. In this paper we demonstrate how these systems can further benefit from employing deep learning (DL) tools for post-processing of the compressively sensed hyperspectral data. We overview some DL techniques that we have developed for improving the HS image reconstruction and target detection.
The utilization of compressive sensing (CS) techniques for hyperspectral (HS) imaging is appealing since HS data is typically huge and very redundant. The CS design offers a significant reduction of the acquisition effort, which can be manifested in faster acquisition of the HS datacubes, acquisition of larger HS images and removing the need for postacquisition digital compression. But, do all these benefits come at the expense of the ability to extract targets from the HS images? The answer to this question, of course, depends on the specific CS design and on the target detection algorithm employed. In a previous study we have shown that there is virtually no target detection performance degradation when a classical target detection algorithm is applied on data acquired with CS HS imaging techniques of the kind we have developed during the last years. In this paper we further investigate the robustness of our CS HS techniques for the task of object classification by deep learning methods. We show preliminary results demonstrating that deep neural network classifiers perform equally well when applied on HS data captured with our compressively sensed methods, as when applied on conventionally sensed HS data.
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