scholarly journals ENHANCEMENT OF SPATIAL RESOLUTION OF THE LROC WIDE ANGLE CAMERA IMAGES

2016 ◽  
Vol XLI-B7 ◽  
pp. 685-692
Author(s):  
P. Mahanti ◽  
M. S. Robinson ◽  
H. Sato ◽  
A. Awumah ◽  
M. Henriksen
Keyword(s):  
High Resolution ◽  
Image Fusion ◽  
Work Performance ◽  
Performance Metrics ◽  
Resolution Enhancement ◽  
Image Data ◽  
Band Ratio ◽  
New Science ◽  
And Performance ◽  
Wide Angle Camera

Image fusion, a popular method for resolution enhancement in Earth-based remote sensing studies involves the integration of geometric (sharpness) detail of a high-resolution panchromatic (Pan) image and the spectral information of a lower resolution multi-spectral (MS) image. Image fusion with planetary images is not as widespread as with terrestrial studies, although successful application of image fusion can lead to the generation of higher resolution MS image data. A comprehensive comparison of six image fusion algorithms in the context of lunar images is presented in this work. Performance of these algorithms is compared by visual inspection of the high-resolution multi-spectral products, derived products such as band-to-band ratio and composite images, and performance metrics with an emphasis on spectral content preservation. Enhanced MS images of the lunar surface can enable new science and maximize the science return for current and future missions.

scholarly journals ENHANCEMENT OF SPATIAL RESOLUTION OF THE LROC WIDE ANGLE CAMERA IMAGES

2016 ◽  
Vol XLI-B7 ◽  
pp. 685-692
Author(s):  
P. Mahanti ◽  
M. S. Robinson ◽  
H. Sato ◽  
A. Awumah ◽  
M. Henriksen
Keyword(s):  
High Resolution ◽  
Image Fusion ◽  
Work Performance ◽  
Performance Metrics ◽  
Resolution Enhancement ◽  
Image Data ◽  
Band Ratio ◽  
New Science ◽  
And Performance ◽  
Wide Angle Camera

Image fusion, a popular method for resolution enhancement in Earth-based remote sensing studies involves the integration of geometric (sharpness) detail of a high-resolution panchromatic (Pan) image and the spectral information of a lower resolution multi-spectral (MS) image. Image fusion with planetary images is not as widespread as with terrestrial studies, although successful application of image fusion can lead to the generation of higher resolution MS image data. A comprehensive comparison of six image fusion algorithms in the context of lunar images is presented in this work. Performance of these algorithms is compared by visual inspection of the high-resolution multi-spectral products, derived products such as band-to-band ratio and composite images, and performance metrics with an emphasis on spectral content preservation. Enhanced MS images of the lunar surface can enable new science and maximize the science return for current and future missions.

scholarly journals Pansformers: Transformer-Based Self-Attention Network for Pansharpening

2021 ◽  
Author(s):  
Nithin G R ◽  
Nitish Kumar M ◽  
Venkateswaran Narasimhan ◽  
Rajanikanth Kakani ◽  
Ujjwal Gupta ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Performance Metrics ◽  
Satellite Image ◽  
Image Data ◽  
Landsat 8 ◽  
Learning Models ◽  
Attention Network ◽  
Panchromatic Image ◽  
Recent Success

Pansharpening is the task of creating a High-Resolution Multi-Spectral Image (HRMS) by extracting and infusing pixel details from the High-Resolution Panchromatic Image into the Low-Resolution Multi-Spectral (LRMS). With the boom in the amount of satellite image data, researchers have replaced traditional approaches with deep learning models. However, existing deep learning models are not built to capture intricate pixel-level relationships. Motivated by the recent success of self-attention mechanisms in computer vision tasks, we propose Pansformers, a transformer-based self-attention architecture, that computes band-wise attention. A further improvement is proposed in the attention network by introducing a Multi-Patch Attention mechanism, which operates on non-overlapping, local patches of the image. Our model is successful in infusing relevant local details from the Panchromatic image while preserving the spectral integrity of the MS image. We show that our Pansformer model significantly improves the performance metrics and the output image quality on imagery from two satellite distributions IKONOS and LANDSAT-8.

scholarly journals Pansformers: Transformer-Based Self-Attention Network for Pansharpening

2021 ◽  
Author(s):  
Nithin G R ◽  
Nitish Kumar M ◽  
Venkateswaran Narasimhan ◽  
Rajanikanth Kakani ◽  
Ujjwal Gupta ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Performance Metrics ◽  
Satellite Image ◽  
Image Data ◽  
Landsat 8 ◽  
Learning Models ◽  
Attention Network ◽  
Panchromatic Image ◽  
Recent Success

Pansharpening is the task of creating a High-Resolution Multi-Spectral Image (HRMS) by extracting and infusing pixel details from the High-Resolution Panchromatic Image into the Low-Resolution Multi-Spectral (LRMS). With the boom in the amount of satellite image data, researchers have replaced traditional approaches with deep learning models. However, existing deep learning models are not built to capture intricate pixel-level relationships. Motivated by the recent success of self-attention mechanisms in computer vision tasks, we propose Pansformers, a transformer-based self-attention architecture, that computes band-wise attention. A further improvement is proposed in the attention network by introducing a Multi-Patch Attention mechanism, which operates on non-overlapping, local patches of the image. Our model is successful in infusing relevant local details from the Panchromatic image while preserving the spectral integrity of the MS image. We show that our Pansformer model significantly improves the performance metrics and the output image quality on imagery from two satellite distributions IKONOS and LANDSAT-8.

Results of a high-resolution airborne TEM system demonstration for unexploded ordnance detection

Geophysics ◽  
2010 ◽  
Vol 75 (6) ◽  
pp. B211-B220 ◽  
Author(s):  
William E. Doll ◽  
T. Jeffrey Gamey ◽  
J. Scott Holladay ◽  
Jacob R. Sheehan ◽  
Jeannemarie Norton ◽  
...  
Keyword(s):  
New Mexico ◽  
High Resolution ◽  
Time Domain ◽  
Performance Metrics ◽  
Unexploded Ordnance ◽  
Sensor Systems ◽  
And Performance ◽  
Unexploded Ordnance Detection ◽  
Recent Evaluation

Airborne geophysical sensor systems using boom-mounted configurations now play an important role in characterizing ordnance-contaminated defense sites. Most of the systems developed to date have been magnetometer systems. These have proven ineffective at sites where basalt or other magnetic geologic units or soils have caused unacceptable noise in the data. Electromagnetic (EM) systems have been developed as an alternative to magnetometer systems for such sites. Recent evaluation of New Mexico field results from the new TEM-8 time-domain EM system has shown successful detection of emplaced blind-seeded ordnance items. Overall, 109 of 110 items were detected, some as small as [Formula: see text] mortars at an area with moderately magnetic geology. The TEM-8 system was also effective in mapping ordnance at a bombing target with severe geologic interference due to basalt, where a previous airborne magnetometer survey proved ineffective. Data and performance metrics for both survey areas are presented and evaluated.

Analysis of hybrid statistical textural and intensity features to discriminate retinal abnormalities through classifiers

2019 ◽  
Vol 233 (5) ◽  
pp. 506-514 ◽  
Author(s):  
Kishore Balasubramanian ◽  
N P Ananthamoorthy
Keyword(s):  
Support Vector Machine ◽  
High Resolution ◽  
Random Forest ◽  
Extreme Learning Machine ◽  
Work Performance ◽  
Performance Metrics ◽  
Support Vector ◽  
Computational Techniques ◽  
Hybrid Features ◽  
Learning Machine

Retinal image analysis relies on the effectiveness of computational techniques to discriminate various abnormalities in the eye like diabetic retinopathy, macular degeneration and glaucoma. The onset of the disease is often unnoticed in case of glaucoma, the effect of which is felt only at a later stage. Diagnosis of such degenerative diseases warrants early diagnosis and treatment. In this work, performance of statistical and textural features in retinal vessel segmentation is evaluated through classifiers like extreme learning machine, support vector machine and Random Forest. The fundus images are initially preprocessed for any noise reduction, image enhancement and contrast adjustment. The two-dimensional Gabor Wavelets and Partition Clustering is employed on the preprocessed image to extract the blood vessels. Finally, the combined hybrid features comprising statistical textural, intensity and vessel morphological features, extracted from the image, are used to detect glaucomatous abnormality through the classifiers. A crisp decision can be taken depending on the classifying rates of the classifiers. Public databases RIM-ONE and high-resolution fundus and local datasets are used for evaluation with threefold cross validation. The evaluation is based on performance metrics through accuracy, sensitivity and specificity. The evaluation of hybrid features obtained an overall accuracy of 97% when tested using classifiers. The support vector machine classifier is able to achieve an accuracy of 93.33% on high-resolution fundus, 93.8% on RIM-ONE dataset and 95.3% on local dataset. For extreme learning machine classifier, the accuracy is 95.1% on high-resolution fundus, 97.8% on RIM-ONE and 96.8% on local dataset. An accuracy of 94.5% on high-resolution fundus 92.5% on RIM-ONE and 94.2% on local dataset is obtained for the random forest classifier. Validation of the experiment results indicate that the hybrid features can be deployed in supervised classifiers to discriminate retinal abnormalities effectively.

scholarly journals REMOTE SENSING IMAGE FUSION USING ICA AND OPTIMIZED WAVELET TRANSFORM

2016 ◽  
Vol XLI-B7 ◽  
pp. 653-659
Author(s):  
V. V. Hnatushenko ◽  
V. V. Vasyliev
Keyword(s):  
Remote Sensing ◽  
Wavelet Transform ◽  
High Resolution ◽  
Image Fusion ◽  
Wavelet Decomposition ◽  
Performance Metrics ◽  
Spectral Characteristics ◽  
Remote Sensing Image ◽  
Remote Sensing Image Fusion ◽  
Spectral Channels

In remote-sensing image processing, fusion (pan-sharpening) is a process of merging high-resolution panchromatic and lower resolution multispectral (MS) imagery to create a single high-resolution color image. Many methods exist to produce data fusion results with the best possible spatial and spectral characteristics, and a number have been commercially implemented. However, the pan-sharpening image produced by these methods gets the high color distortion of spectral information. In this paper, to minimize the spectral distortion we propose a remote sensing image fusion method which combines the Independent Component Analysis (ICA) and optimization wavelet transform. The proposed method is based on selection of multiscale components obtained after the ICA of images on the base of their wavelet decomposition and formation of linear forms detailing coefficients of the wavelet decomposition of images brightness distributions by spectral channels with iteratively adjusted weights. These coefficients are determined as a result of solving an optimization problem for the criterion of maximization of information entropy of the synthesized images formed by means of wavelet reconstruction. Further, reconstruction of the images of spectral channels is done by the reverse wavelet transform and formation of the resulting image by superposition of the obtained images. To verify the validity, the new proposed method is compared with several techniques using WorldView-2 satellite data in subjective and objective aspects. In experiments we demonstrated that our scheme provides good spectral quality and efficiency. Spectral and spatial quality metrics in terms of RASE, RMSE, CC, ERGAS and SSIM are used in our experiments. These synthesized MS images differ by showing a better contrast and clarity on the boundaries of the "object of interest - the background". The results show that the proposed approach performs better than some compared methods according to the performance metrics.

scholarly journals REMOTE SENSING IMAGE FUSION USING ICA AND OPTIMIZED WAVELET TRANSFORM

2016 ◽  
Vol XLI-B7 ◽  
pp. 653-659 ◽  
Author(s):  
V. V. Hnatushenko ◽  
V. V. Vasyliev
Keyword(s):  
Remote Sensing ◽  
Wavelet Transform ◽  
High Resolution ◽  
Image Fusion ◽  
Wavelet Decomposition ◽  
Performance Metrics ◽  
Spectral Characteristics ◽  
Remote Sensing Image ◽  
Remote Sensing Image Fusion ◽  
Spectral Channels

In remote-sensing image processing, fusion (pan-sharpening) is a process of merging high-resolution panchromatic and lower resolution multispectral (MS) imagery to create a single high-resolution color image. Many methods exist to produce data fusion results with the best possible spatial and spectral characteristics, and a number have been commercially implemented. However, the pan-sharpening image produced by these methods gets the high color distortion of spectral information. In this paper, to minimize the spectral distortion we propose a remote sensing image fusion method which combines the Independent Component Analysis (ICA) and optimization wavelet transform. The proposed method is based on selection of multiscale components obtained after the ICA of images on the base of their wavelet decomposition and formation of linear forms detailing coefficients of the wavelet decomposition of images brightness distributions by spectral channels with iteratively adjusted weights. These coefficients are determined as a result of solving an optimization problem for the criterion of maximization of information entropy of the synthesized images formed by means of wavelet reconstruction. Further, reconstruction of the images of spectral channels is done by the reverse wavelet transform and formation of the resulting image by superposition of the obtained images. To verify the validity, the new proposed method is compared with several techniques using WorldView-2 satellite data in subjective and objective aspects. In experiments we demonstrated that our scheme provides good spectral quality and efficiency. Spectral and spatial quality metrics in terms of RASE, RMSE, CC, ERGAS and SSIM are used in our experiments. These synthesized MS images differ by showing a better contrast and clarity on the boundaries of the "object of interest - the background". The results show that the proposed approach performs better than some compared methods according to the performance metrics.

The Relation Between Financial Incentives, Motivation, and Performance

2017 ◽  
Vol 16 (2) ◽  
pp. 61-76 ◽  
Author(s):  
Anaïs Thibault Landry ◽  
Marylène Gagné ◽  
Jacques Forest ◽  
Sylvie Guerrero ◽  
Michel Séguin ◽  
...  
Keyword(s):  
Financial Incentives ◽  
Work Performance ◽  
Autonomous Motivation ◽  
Compensation Plans ◽  
Work Settings ◽  
Path Analyses ◽  
Three Samples ◽  
And Performance ◽  
Performance Results

Abstract. To this day, researchers are debating the adequacy of using financial incentives to bolster performance in work settings. Our goal was to contribute to current understanding by considering the moderating role of distributive justice in the relation between financial incentives, motivation, and performance. Based on self-determination theory, we hypothesized that when bonuses are fairly distributed, using financial incentives makes employees feel more competent and autonomous, which in turn fosters greater autonomous motivation and lower controlled motivation, and better work performance. Results from path analyses in three samples supported our hypotheses, suggesting that the effect of financial incentives is contextual, and that compensation plans using financial incentives and bonuses can be effective when properly managed.

MODEL KUANTITATIF MANAJEMEN STRES KERJA DAN MOTIVASI UNTUK MENINGKATKAN KINERJA PETUGAS REKAM MEDIS DI RS ORTOPEDI PROF. DR. R. SOEHARSO SURAKARTA

2016 ◽  
Vol 5 (2) ◽  
pp. 150-156
Author(s):  
Laili Rahmatul Ilmi
Keyword(s):  
Stress Management ◽  
Work Stress ◽  
Work Performance ◽  
Work Motivation ◽  
Cross Sectional ◽  
Staff Performance ◽  
Bivariate Correlation ◽  
And Performance ◽  
The Relationship ◽  
Management Work

Background: Workload may indirectly cause stress. The ability to manage work stress may affect staff’s motivation and performance. The staff performance will affect decision-making in improving the service quality. Objective: This study aimed to analyze the relationship between stress management, work motivation and work performance. Method: This was an analytic observational study with a cross sectional approach. A sample of 19 medical record staff, working at Prof. Dr. R Soeharso orthopedic hospital Surakarta, were selected for this study. A set of questionnaires were developed and administered to measure stress management, work motivation and work performance. Data were then analyzed with a bivariate correlation analysis. Results: There were statistically significant correlations between work stress management, work motivation and work performance. The ability to manage stress positively increased the motivation (r= 0,56; p= 0,013), as well as the work performance (r= 0,49; p= 0,036). Moreover, a higher motivation will lead to a higher performance (r= 0,42; p= 0,071). Conclusion: There were positive relationships between work stress management, work motivation and work performance. Key words: work stress management, motivation, performance.

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