An Investigation in the Use of Hyperspectral Imagery Using Machine Learning for Vision-Aided Navigation

Date of Award


Degree Name

M.S. in Electrical and Computer Engineering


Department of Electrical and Computer Engineering


Bradly Ratliff


Vision aided navigation has become an important topic of research for assisting with navigational tasks when traditional navigation technology is not functioning properly, such as GPS devices. Approaches based upon traditional visible and thermal camera data have been shown to be effective across a wide range of scenarios; however, there are situations (such as over deserts and large bodies of water) where there is not enough features in the imagery for it to be effective. Hyperspectral imagery (HSI) has not been heavily explored for vision-aided navigation applications and can provide a wealth of features to aid with these tasks. In this work we investigate the use of HSI in vision-aided navigation using machine learning. This work aims to show that HSI can provide utility in vision-aided navigation and can be more effective than conventional imagery approaches for certain tasks. We explore the use of HSI data selected from different geographic locations across the United States to demonstrate that spectral features from these different locales can be used to distinguish and classify them. The HSI data used was collected with NASA's AVIRIS sensor due to the availability of large amounts of data across the United States. The neural network that was trained and tested was a convolutional neural network that uses a multi-scale filter bank and residual learning to improve the network. A second network was created, trained, and tested on equivalent RGB images so that performance could be compared against the HSI network. We tested the networks in two ways. First, we made use of all selected data from each location class that resulted in an unequal number of location samples, and second where we selected an equal number of samples from each location class. We optimized the hyper parameters to yield best performance in each case and found that the best HSI network out performed the best RGB network in every test case. While this work is preliminary, it demonstrates the strong potential of HSI for vision aided navigation applications.


Electrical Engineering, Machine learningHyperspectral ImageryVision aided navigationDeep Convolutional Neural networkImage Classification

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