NDLI: A Novel Framework for Object-Based Coding and Compression of Hyperspectral Imagery

Content Provider	IET Digital Library
Author	Zhao, Chunhui Li, Xiaohui Ren, Jinchang Marshall, Stephen
Abstract	A novel object-based framework is proposed for HSI compression, where targets of interest are extracted and separately coded. With objects removed, the holes are filled with the background average to form a new but more homogenous background for better compression. An improved sparse representation with adaptive spatial support is proposed for target detection. By applying the proposed framework to 2D/3D DCT approaches, reconstructed images from conventional and proposed approaches are compared. Six criteria in three groups are employed for quantitative evaluations to measure the degree of data reduction, the distortion of reconstructed image quality and accuracy in target detection, respectively. Comprehensive experiments on two datasets are used for performance evaluation. It is found that the proposed approaches yield much improved results.
Starting Page	300
Ending Page	305
Page Count	6
ISSN	10224653
Volume Number	24
e-ISSN	20755597
Issue Number	Issue 2, Apr (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/cje/24/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/cje.2015.04.012
Journal	Chinese Journal of Electronics
Publisher Date	2015-04-01
Access Restriction	Open
Rights Holder	© Chinese Institute of Electronics
Subject Keyword	2D-3D DCT Approach Adaptive Spatial Support Computer Vision And Image Processing Technique Data Compression Data Reduction Discrete Cosine Transform Feature Extraction HSI Compression Hyperspectral Image Hyperspectral Imagery Image And Video Coding Image Coding Image Recognition Image Reconstruction Image Representation Improved Sparse Representation Integral Transforms Object Detection Object-based Coding Object-based Compression Framework Performance Evaluation Reconstructed Image Quality Target Detection
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Electrical and Electronic Engineering

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