Date of Award
2016
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Electrical and Computer Engineering
Committee Chair
W. David Pan
Committee Member
B. Earl Wells
Committee Member
Maria Z. A. Pour
Committee Member
Ramazan S. Aygun
Committee Member
Sivaguru S. Ravindran
Subject(s)
Remote sensing, Image processing--Digital techniques, Multispectral imaging, Image compression
Abstract
This dissertation addresses the problem of efficient lossless compression of regions of interest (ROIs) in hyperspectral images. To this end, a novel framework for evaluating the performance of predictive lossless compression schemes on ROIs without no-data regions was introduced. Furthermore, mixture geometric distributions were used to model the residual data from the predictors along with an information-theoretic analysis of the compression performance. Then, three practical predictive lossless compression methods were introduced, including a modified least mean square filtering method, a least mean square filtering method based on the maximum correntropy criteria, and a two-stage predictor using context similarity weighted averaging filter and recursive least squares. For entropy coding of the prediction residuals, a new coding scheme using two independent Golomb-Rice coders was proposed to compress boundary and full-context pixels within each ROI separately. Additionally, the impacts of different ROI identification methods on the compression performance were analyzed and compared. Extensive simulations on various hyperspectral image datasets demonstrated that the proposed prediction and coding methods were capable of achieving higher compression on ROIs than state-of-the-art methods, including the ``Fast Lossless" method in the CCSDS Standard for Multispectral and Hyperspectral Data Compression, as well as the shape-adaptive JPEG 2000 method for lossless compression of ROIs in hyperspectral images.
Recommended Citation
Shen, Hongda, "Efficient lossless compression of regions of interest in hyperspectral imagery" (2016). Dissertations. 103.
https://louis.uah.edu/uah-dissertations/103