Hyperspectral Image Compression Using an Online Learning Method

2015-04-24
Ulku, Irem
TÖREYİN, BEHÇET UĞUR
A hyperspectral image compression method is proposed using an online dictionary learning approach. The online learning mechanism is aimed at utilizing least number of dictionary elements for each hyperspectral image under consideration. In order to meet this "sparsity constraint", basis pursuit algorithm is used. Hyperspectral imagery from AVIRIS datasets are used for testing purposes. Effects of non-zero dictionary elements on the compression performance are analyzed. Results indicate that, the proposed online dictionary learning algorithm may be utilized for higher data rates, as it performs better in terms of PSNR values, as compared with the state-of-the-art predictive lossy compression schemes.
Conference on Satellite Data Compression, Communications, and Processing XI

Suggestions

PROGRESSIVE COMPRESSION OF DIGITAL ELEVATION DATA USING MESHES
Kose, Kivanc; Yılmaz, Erdal; ÇETİN, AHMET ENİS (2009-07-17)
In this paper a new Digital Elevation Map (DEM) image compression algorithm is proposed. DEM image can be threated as a grayscale image, whose pixel values are the elevation values of the map points. The grayscale DEM image is compressed using an adaptive wavelet based image compression algorithm. The method, which is an extension of the progressive mesh compression takes advantage of the multiresolution property of the wavelets while coding the map images. This makes it possible to decode different resolut...
On lossless intra coding in HEVC with 3-tap filters
Alvar, Saeed Ranjbar; Kamışlı, Fatih (2016-09-01)
This paper presents a pixel-by-pixel spatial prediction method for lossless intra coding within High Efficiency Video Coding (HEVC). Previous pixel-by-pixel spatial prediction methods use only two neighboring pixels for prediction, based on the angular projection idea borrowed from block-based intra prediction in lossy coding, or are based on ad hoc methods applied in some intra modes. This paper explores a pixel-by-pixel prediction method which uses three neighboring pixels for prediction according to a tw...
Collaborative Direction of Arrival estimation by using Alternating Direction Method of Multipliers in distributed sensor array networks employing Sparse Bayesian Learning framework
Nurbas, Ekin; Onat, Emrah; Tuncer, Temel Engin (2022-10-01)
In this paper, we present a new method for Direction of Arrival (DoA) estimation in distributed sensor array networks by using Alternating Direction Method of Multipliers (ADMM) in Sparse Bayesian Learning (SBL) framework. Our proposed method, CDoAE, has certain advantages compared to previous distributed DoA estimation methods. It does not require any special array geometry and there is no need for inter -array frequency and phase matching. CDoAE uses the distributed ADMM to update the parameter set extrac...
Hyperspectral Image Classification via Kernel Basic Thresholding Classifier
TOKSÖZ, Mehmet Altan; Ulusoy, İlkay (2017-02-01)
We propose a nonlinear kernel version of recently introduced basic thresholding classifier (BTC) for hyperspectral image (HSI) classification. BTC is a sparsity-based linear classifier, which utilizes inner product for similarity measure based on the fact that the hyperspectral data are linearly separable in the feature space. In practice, the pixels of different classes of a given HSI are not always distinct and may overlap. This is known as the inseparability problem, which reduces the performance of a li...
Piecewise-planar 3D reconstruction in rate-distortion sense
Imre, Evren; Gueduekbay, Ugur; Alatan, Abdullah Aydın (2007-05-09)
In this paper, a novel rate-distortion optimization inspired 3D piecewise-planar reconstruction algorithm is proposed. The algorithm refines a coarse 3D triangular mesh, by inserting vertices in a way to minimize the intensity difference between an image and its prediction. The preliminary experiments on synthetic and real data indicate the validity of the proposed approach.
Citation Formats
I. Ulku and B. U. TÖREYİN, “Hyperspectral Image Compression Using an Online Learning Method,” Baltimore, MD, 2015, vol. 9501, p. 0, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/65371.