A comparative study of remotely sensed data classification using principal components analysis and divergence

This work accomplishes a comparative study between two distinct image compression techniques, namely the Lifting technique and the Principal Components Analysis (PCA), in order to determine what of these two approaches is more appropriate for cutting tool wear images analysis. Lifting and Principal Components Analysis were applied in original images of a cutting tool for producing a low resolution version, while keeping the more important details of the image. The low-loss image compression quality provided by these techniques was expressed in terms of the compression factor (ρ), the Mean Square Error (MSE) and the Peak Signal-to-Noise Rate (PSNR) provided by the image compression process. The tests were accomplished using the high-performance language for technical computing MATLAB®, and the results shown that the PCA technique presented the best values of PSNR with low compression rates. However, with high values of compression rates the lifting technique gave the highest PSNR.

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Cloud Masking for Remotely Sensed Data Using Spectral and Principal Components Analysis

Engineering, Technology & Applied Science Research ◽

10.48084/etasr.148 ◽

2012 ◽

Vol 2 (3) ◽

pp. 221-225 ◽

Cited By ~ 3

Author(s):

A. Ahmad ◽

S. Quegan

Keyword(s):

Spectral Analysis ◽

Principal Components Analysis ◽

Principal Components ◽

Principal Component ◽

Geographical Information ◽

Remotely Sensed Data ◽

Tropical Conditions ◽

Components Analysis ◽

Moderate Resolution Imaging Spectroradiometer ◽

The Difference

Two methods of cloud masking tuned to tropical conditions have been developed, based on spectral analysis and Principal Components Analysis (PCA) of Moderate Resolution Imaging Spectroradiometer (MODIS) data. In the spectral approach, thresholds were applied to four reflective bands (1, 2, 3, and 4), three thermal bands (29, 31 and 32), the band 2/band 1 ratio, and the difference between band 29 and 31 in order to detect clouds. The PCA approach applied a threshold to the first principal component derived from the seven quantities used for spectral analysis. Cloud detections were compared with the standard MODIS cloud mask, and their accuracy was assessed using reference images and geographical information on the study area.

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Low-loss image compression techniques for cutting tool images: a comparative study of compression quality measures

Exacta ◽

10.5585/exacta.v8i2.2000 ◽

2010 ◽

Vol 8 (2) ◽

pp. 225-235

Author(s):

Fábio Henrique Pereira ◽

Elesandro Antonio Baptista ◽

Nivaldo Lemos Coppini ◽

Rafael Do Espírito-Santo ◽

Ademir João de Oliveira

Keyword(s):

Image Compression ◽

Comparative Study ◽

Principal Components Analysis ◽

Principal Components ◽

High Performance ◽

Cutting Tool ◽

Low Loss ◽

Compression Process ◽

Lifting Technique ◽

Components Analysis

This work accomplishes a comparative study between two distinct image compression techniques, namely the Lifting technique and the Principal Components Analysis (PCA), in order to determine what of these two approaches is more appropriate for cutting tool wear images analysis. Lifting and Principal Components Analysis were applied in original images of a cutting tool for producing a low resolution version, while keeping the more important details of the image. The low-loss image compression quality provided by these techniques was expressed in terms of the compression factor (ρ), the Mean Square Error (MSE) and the Peak Signal-to-Noise Rate (PSNR) provided by the image compression process. The tests were accomplished using the high-performance language for technical computing MATLAB®, and the results shown that the PCA technique presented the best values of PSNR with low compression rates. However, with high values of compression rates the lifting technique gave the highest PSNR.

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