Neural network-based band selection on hyperspectral imagery

Preston C. Darling

doi:10.1117/12.2588237

12 April 2021 Neural network-based band selection on hyperspectral imagery

Preston C. Darling

Proceedings Volume 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III; 117460E (2021) https://doi.org/10.1117/12.2588237
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Data reduction techniques are becoming increasingly important in modern data analysis problems{especially in fields involving big data. Although not quite on the same scale as high velocity and volume big data problems, remote sensing analysis on hyperspectral imagery (HSI) can suffer from the increase in dimensionality provided by this sensing modality. Common hyperspectral sensors divide the sensed optical bandwidth into as many as 270 different channels. Depending on the specific bandwidth and application, some channels may be unusable due to water absorption, while others may provide little-to-no discriminative information when considering their impact on algorithm performance. With this in mind, there are several techniques devised to subsample the original number of bands to a more manageable size allowing for increased algorithm performance both in classification accuracy and computation time. Herein, we propose a neural network-based band selection technique that seeks to generalize existing autoencoder-based methods and experiment with tracking input contributions throughout the network. We explore the ability to perform task-specific band selection by applying this method to a general multilayer perceptron architecture. We apply our proposed band selection methodology to a novel HSI dataset captured by the U.S. Army ERDC and compare performance to alternative band selection algorithms.

Conference Presentation

Citation Download Citation

Preston C. Darling "Neural network-based band selection on hyperspectral imagery", Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, 117460E (12 April 2021); https://doi.org/10.1117/12.2588237

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