scholarly journals Analysis of Variable-Length Codes for Integer Encoding in Hyperspectral Data Compression with the k2-Raster Compact Data Structure

2020 ◽  
Vol 12 (12) ◽  
pp. 1983
Author(s):  
Kevin Chow ◽  
Dion Eustathios Olivier Tzamarias ◽  
Miguel Hernández-Cabronero ◽  
Ian Blanes ◽  
Joan Serra-Sagristà
Keyword(s):  
Data Structure ◽  
Compression Ratio ◽  
Hyperspectral Data ◽  
Variable Length ◽  
Direct Access ◽  
Access Time ◽  
Competitive Performance ◽  
K Value ◽  
Data Elements ◽  
Selection Of

This paper examines the various variable-length encoders that provide integer encoding to hyperspectral scene data within a k 2 -raster compact data structure. This compact data structure leads to a compression ratio similar to that produced by some of the classical compression techniques. This compact data structure also provides direct access for query to its data elements without requiring any decompression. The selection of the integer encoder is critical for obtaining a competitive performance considering both the compression ratio and access time. In this research, we show experimental results of different integer encoders such as Rice, Simple9, Simple16, PForDelta codes, and DACs. Further, a method to determine an appropriate k value for building a k 2 -raster compact data structure with competitive performance is discussed.

Suffix array for multi-pattern matching with variable length wildcards

2021 ◽  
Vol 25 (2) ◽  
pp. 283-303
Author(s):  
Na Liu ◽  
Fei Xie ◽  
Xindong Wu
Keyword(s):  
Dynamic Programming ◽  
Data Structure ◽  
Pattern Matching ◽  
Edit Distance ◽  
State Of The Art ◽  
Suffix Array ◽  
Variable Length ◽  
Distance Method ◽  
Efficient Data ◽  
Comparison Algorithms

Approximate multi-pattern matching is an important issue that is widely and frequently utilized, when the pattern contains variable-length wildcards. In this paper, two suffix array-based algorithms have been proposed to solve this problem. Suffix array is an efficient data structure for exact string matching in existing studies, as well as for approximate pattern matching and multi-pattern matching. An algorithm called MMSA-S is for the short exact characters in a pattern by dynamic programming, while another algorithm called MMSA-L deals with the long exact characters by the edit distance method. Experimental results of Pizza & Chili corpus demonstrate that these two newly proposed algorithms, in most cases, are more time-efficient than the state-of-the-art comparison algorithms.

scholarly journals Using Predictive and Differential Methods with K2-Raster Compact Data Structure for Hyperspectral Image Lossless Compression

2019 ◽  
Vol 11 (21) ◽  
pp. 2461 ◽  
Author(s):  
Kevin Chow ◽  
Dion Tzamarias ◽  
Ian Blanes ◽  
Joan Serra-Sagristà
Keyword(s):  
Data Structure ◽  
Hyperspectral Image ◽  
Lossless Compression ◽  
Hyperspectral Images ◽  
Direct Access ◽  
Bit Rate ◽  
Compression Algorithms ◽  
Real Time Processing ◽  
Time Processing ◽  
Differential Encoding

This paper proposes a lossless coder for real-time processing and compression of hyperspectral images. After applying either a predictor or a differential encoder to reduce the bit rate of an image by exploiting the close similarity in pixels between neighboring bands, it uses a compact data structure called k 2 -raster to further reduce the bit rate. The advantage of using such a data structure is its compactness, with a size that is comparable to that produced by some classical compression algorithms and yet still providing direct access to its content for query without any need for full decompression. Experiments show that using k 2 -raster alone already achieves much lower rates (up to 55% reduction), and with preprocessing, the rates are further reduced up to 64%. Finally, we provide experimental results that show that the predictor is able to produce higher rates reduction than differential encoding.

A Spatial Study of the Influence of Plasma Length on Interference Effects in a Six-Electrode, Direct-Current, Variable-Length Plasma

1988 ◽  
Vol 42 (4) ◽  
pp. 693-698 ◽  
Author(s):  
James P. Shields ◽  
Edward H. Piepmeier
Keyword(s):  
Direct Current ◽  
Variable Length ◽  
Emission Source ◽  
Plasma Emission ◽  
Careful Selection ◽  
Interference Effects ◽  
Simple Effect ◽  
Direct Current Plasma ◽  
Interference Mechanism ◽  
Selection Of

Vertical spatial profiles of a six-electrode, direct-current plasma emission source were used to study the influence of plasma length on the interference of Na on Ca and Zn and P on Ca. For the shortest plasma (11.5 mm), depression of the Ca atom line is observed in the region up to 10 mm above the tip of the sample bullet when Na is present. A cross-over to enhancement occurs for higher regions. This is directly opposite to the observations in the ICP for the Na interference on Ca. Increasing the plasma length causes downward shifts in the cross-over point which are not a simple effect of the lowering of the sample bullet. When P is present, the Ca atom emission is initially depressed in the region directly above the sample bullet. At higher observation heights, little effect is observed. Similar behavior is observed for the Ca ion line in the presence of P, suggesting the possibility of a classical vaporization-type interference mechanism. The interference effects studied here can be virtually eliminated by careful selection of plasma length and observation height.

A Study on Selecting Segmentation Object of Object-Oriented Inversion Based on Hyperspectral Chlorophyll-A in Chaohu Lake

2014 ◽  
Vol 675-677 ◽  
pp. 1153-1157
Author(s):  
Xue Jiao Hou ◽  
Jing Liu ◽  
Wei Cui
Keyword(s):  
Chlorophyll A ◽  
Object Oriented ◽  
Hyperspectral Data ◽  
Single Band ◽  
Quality Data ◽  
Chaohu Lake ◽  
Band Model ◽  
Water Quality Data ◽  
Combination Model ◽  
Selection Of

Beased on in Situ Water Quality Data and Hyperspectral Data from HJ-1A satellite in Chaohu Lake, through Contrasting the Object-Oriented Chlorophyll-a Inversion Precision Of single Band with Two-Band Model, the Results Show: (1) In Hyperspectral Object-Oriented Remote Sensing inversion, the Inversion Effect of Choosing Combination Model to Segment Is superior to that of Choosing the Single Band Directly, and Using Combination model to Segment can Certain Degree Solve the Problem that Commercial Softwares cannot Segment all Hyperspectral Data at the same Time.(2)When Inversing Chlorophyll-a Concentration with Hyperspectral Data, the Single Bands Constituting the Optimal Model Are not Always in the Traditional Characteristics Band Range of Chlorophyll-a. so All bands should be Comprehensively Analyzed to take Full Advantages Of hyperspectral Data when Inversing. these Conclusions Will provide Basis for the Future Segmentation Object Selection of Object-Orientedon Hyperspectral Lakes Chlorophyll-a Inversion and Certain Reference for Band Selection of Hyperspectral Inversion Model.

scholarly journals Quality and size assessment of quantized images using K-Means++ clustering

2020 ◽  
Vol 9 (3) ◽  
pp. 1183-1188
Author(s):  
Davin Ongkadinata ◽  
Farica Perdana Putri
Keyword(s):  
Market Segmentation ◽  
Clustering Algorithm ◽  
Color Quantization ◽  
File Size ◽  
K Value ◽  
Color Distribution ◽  
Color Palette ◽  
Segmentation Image ◽  
True Color ◽  
Selection Of

In this paper, an amended K-Means algorithm called K-Means++ is implemented for color quantization. K-Means++ is an improvement to the K-Means algorithm in order to surmount the random selection of the initial centroids. The main advantage of K-Means++ is the centroids chosen are distributed over the data such that it reduces the sum of squared errors (SSE).  K-Means++ algorithm is used to analyze the color distribution of an image and create the color palette for transforming to a better quantized image compared to the standard K-Means algorithm. The tests were conducted on several popular true color images with different numbers of K value: 32, 64, 128, and 256. The results show that K-Means++ clustering algorithm yields higher PSNR values and lower file size compared to K-Means algorithm; 2.58% and 1.05%. It is envisaged that this clustering algorithm will benefit in many applications such as document clustering, market segmentation, image compression and image segmentation because it produces accurate and stable results.

scholarly journals Modelling Site Index in Forest Stands Using Airborne Hyperspectral Imagery and Bi-Temporal Laser Scanner Data

2019 ◽  
Vol 11 (9) ◽  
pp. 1020 ◽  
Author(s):  
Bollandsås ◽  
Ørka ◽  
Dalponte ◽  
Gobakken ◽  
Næsset
Keyword(s):  
Atmospheric Correction ◽  
Laser Scanner ◽  
Field Measurements ◽  
Site Index ◽  
Hyperspectral Data ◽  
Average Height ◽  
Single Time ◽  
Development Site ◽  
Corrected Data ◽  
Selection Of

In forest management, site index information is essential for planning silvicultural operations and forecasting forest development. Site index is most commonly expressed as the average height of the dominant trees at a certain index age, and can be determined either by photo interpretation, field measurements, or projection of age combined with height estimates from remote sensing. However, recently it has been shown that site index can be accurately predicted from bi-temporal airborne laser scanner (ALS) data. Furthermore, single-time hyperspectral data have also been shown to be correlated to site index. The aim of the current study was to compare the accuracy of modelling site index using (1) data from bi-temporal ALS; (2) single-time hyperspectral data with different types of preprocessing; and (3) combined bi-temporal ALS and single-time hyperspectral data. The period between the ALS acquisitions was 11 years. The preprocessing of the hyperspectral data included an atmospheric correction and/or a normalization of the reflectance. Furthermore, a selection of pixels was carried out based on NDVI and compared to using all pixels. The results showed that bi-temporal ALS data explained about 70% (R2) of the variation in the site index, and the RMSE values from a cross-validation were 3.0 m and 2.2 m for spruce- and pine-dominated plots, respectively. Corresponding values for the different single-time hyperspectral datasets were 54%, 3.9 m, and 2.5 m. With bi-temporal ALS data and hyperspectral data used in combination, the results indicated that the contribution from the hyperspectral data was marginal compared to just using bi-temporal ALS. We also found that models constructed with normalized hyperspectral data produced lower RMSE values compared to those constructed with atmospherically corrected data, and that a selection of pixels based on NDVI did not improve the results compared to using all pixels.

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