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Pre-processing And Classification Of Hyperspectral Imagery Via Selective Inpainting Presentation

Citation Author(s):
Victoria Chayes, Kevin Miller, Rasika Bhalerao, Jiajie Luo, Wei Zhu, Andrea L. Bertozzi, Wenzhi Liao, Stanley Osher
Submitted by:
Victoria Chayes
Last updated:
6 March 2017 - 2:08pm
Document Type:
Presentation Slides
Document Year:
2017
Event:
Presenters:
Victoria Chayes
Paper Code:
SS-L4.4
 

We propose a semi-supervised algorithm for processing and classification of hyperspectral imagery. For initialization, we keep 20% of the data intact, and use Principal Component Analysis to discard voxels from noisier bands and pixels. Then, we use either an Accelerated Proximal Gradient algorithm (APGL), or a modified APGL algorithm with a penalty term for distance between inpainted pixels and endmembers (APGL Hyp), on the initialized datacube to inpaint the missing data. APGL and APGL Hyp are distinguished by performance on datasets with full pixels removed or extreme noise. This inpainting technique results in band-by-band datacube sharpening and removal of noise from individual spectral signatures. We can also classify the inpainted cube by assigning each pixel to its nearest endmember via Euclidean distance. We demonstrate improved accuracy in classification over data-mining techniques like k-means, unmixing techniques like Hierarchical Non-Negative Matrix Factorization, and graph-based methods like Non-Local Total Variation.

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