Novel Approach for Resolution Enhancement of Satellite Images Using Wavelet Techniques

2020 ◽  
pp. 13-23
Author(s):  
Mansing Rathod ◽  
Jayashree Khanapuri ◽  
Dilendra Hiran
Keyword(s):  
Satellite Images ◽  
Resolution Enhancement ◽  
Novel Approach ◽  
Wavelet Techniques

scholarly journals A GENERATIVE ADVERSARIAL NETWORK APPROACH FOR SUPER-RESOLUTION OF SENTINEL-2 SATELLITE IMAGES

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 9-14
Author(s):  
F. Pineda ◽  
V. Ayma ◽  
C. Beltran
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Resolution Enhancement ◽  
Super Resolution ◽  
Structural Similarity ◽  
Generative Adversarial Network ◽  
Acceptable Result ◽  
Adversarial Network ◽  
High Resolution Satellite Images ◽  
Sentinel 2

Abstract. High-resolution satellite images have always been in high demand due to the greater detail and precision they offer, as well as the wide scope of the fields in which they could be applied; however, satellites in operation offering very high-resolution (VHR) images has experienced an important increase, but they remain as a smaller proportion against existing lower resolution (HR) satellites. Recent models of convolutional neural networks (CNN) are very suitable for applications with image processing, like resolution enhancement of images; but in order to obtain an acceptable result, it is important, not only to define the kind of CNN architecture but the reference set of images to train the model. Our work proposes an alternative to improve the spatial resolution of HR images obtained by Sentinel-2 satellite by using the VHR images from PeruSat1, a peruvian satellite, which serve as the reference for the super-resolution approach implementation based on a Generative Adversarial Network (GAN) model, as an alternative for obtaining VHR images. The VHR PeruSat-1 image dataset is used for the training process of the network. The results obtained were analyzed considering the Peak Signal to Noise Ratios (PSNR) and the Structural Similarity (SSIM). Finally, some visual outcomes, over a given testing dataset, are presented so the performance of the model could be analyzed as well.

scholarly journals Improvement of Clay and Sand Quantification Based on a Novel Approach with a Focus on Multispectral Satellite Images

2018 ◽  
Vol 10 (10) ◽  
pp. 1555 ◽  
Author(s):  
Caio Fongaro ◽  
José Demattê ◽  
Rodnei Rizzo ◽  
José Lucas Safanelli ◽  
Wanderson Mendes ◽  
...  
Keyword(s):  
Model Calibration ◽  
Large Scale ◽  
Satellite Images ◽  
Satellite Image ◽  
Soil Mapping ◽  
Spectral Signatures ◽  
Novel Approach ◽  
Multispectral Satellite Images ◽  
Reduced Costs ◽  
Multispectral Satellite Image

Soil mapping demands large-scale surveys that are costly and time consuming. It is necessary to identify strategies with reduced costs to obtain detailed information for soil mapping. We aimed to compare multispectral satellite image and relief parameters for the quantification and mapping of clay and sand contents. The Temporal Synthetic Spectral (TESS) reflectance and Synthetic Soil Image (SYSI) approaches were used to identify and characterize texture spectral signatures at the image level. Soil samples were collected (0–20 cm depth, 919 points) from an area of 14,614 km2 in Brazil for reference and model calibration. We compared different prediction approaches: (a) TESS and SYSI; (b) Relief-Derived Covariates (RDC); and (c) SYSI plus RDC. The TESS method produced highly similar behavior to the laboratory convolved data. The sandy textural class showed a greater increase in average spectral reflectance from Band 1 to 7 compared with the clayey class. The prediction using SYSI produced a better result for clay (R2 = 0.83; RMSE = 65.0 g kg−1) and sand (R2 = 0.86; RMSE = 79.9 g kg−1). Multispectral satellite images were more stable for the identification of soil properties than relief parameters.

A Novel Approach to Compression of Satellite Images Using Butterworth Filtering

2019 ◽  
pp. 179-183
Author(s):  
Anirban Patra ◽  
Swagata Bandyopadhyay ◽  
Debasish Chakraborty ◽  
Arijit Saha
Keyword(s):  
Satellite Images ◽  
Novel Approach

CONTOUR INTERPOLATION USING LEVEL-SET ANALYSIS

2012 ◽  
Vol 12 (01) ◽  
pp. 1250004 ◽  
Author(s):  
DIPTI PRASAD MUKHERJEE ◽  
NILANJAN RAY
Keyword(s):  
Level Set ◽  
Satellite Images ◽  
Flow Analysis ◽  
Shape Features ◽  
Contour Interpolation ◽  
Curvature Analysis ◽  
Novel Approach ◽  
Contour Deformation ◽  
Level Set Framework ◽  
Contour Curvature

We propose a novel approach to generate intermediate contours given a sequence of object contours. The proposal unifies shape features through contour curvature analysis and motion between the contours through optic flow analysis. The major contribution of this work is in integrating this shape and image intensity-based contour interpolation scheme in a level-set framework. The interpolated contours between an initial and a target contour act as missing link and establish a path along which contour deformation has taken place. We have shown that for different application domains such as 3D organ visualization (the generation of contours between two spatially apart contours of 2D slice images of a 3D organ), the meteorological applications of tracing, and the path of a developing cyclone (when satellite images are taken at distant time points and the shape of cyclone in between two consecutive satellite images are of interest), the proposal has outperformed the competing approaches.

scholarly journals A Novel Approach to Estimate Diffuse Attenuation Coefficients for QuickBird Satellite Images: A Case Study at Kish Island, the Persian Gulf

2013 ◽  
Vol 41 (4) ◽  
pp. 797-806 ◽  
Author(s):  
Keivan Kabiri ◽  
Biswajeet Pradhan ◽  
Helmi Zulhaidi Mohd Shafri ◽  
Shattri Bin Mansor ◽  
Kaveh Samimi-Namin
Keyword(s):  
Persian Gulf ◽  
Satellite Images ◽  
Attenuation Coefficients ◽  
Novel Approach ◽  
The Persian Gulf

scholarly journals Reducing error and increasing reliability of wildlife counts from citizen science surveys: counting Weddell Seals in the Ross Sea from satellite images

2020 ◽  
Author(s):  
Leo A. Salas ◽  
Michelle LaRue ◽  
Nadav Nur ◽  
David G. Ainley ◽  
Sharon E. Stammerjohn ◽  
...  
Keyword(s):  
Citizen Science ◽  
Satellite Images ◽  
Ross Sea ◽  
Spatial Scales ◽  
Sampling Bias ◽  
Weddell Seal ◽  
Time Of Day ◽  
Weddell Seals ◽  
Novel Approach ◽  
High Resolution Satellite Images

ABSTRACTCitizen science programs can be effective at collecting information at large temporal and spatial scales. However, sampling bias is a concern in citizen science datasets and can lead to unreliable estimates. We address this issue with a novel approach in a first-of-its-kind citizen science survey of Weddell seals for the entire coast of Antarctica. Our citizen scientists inspected very high-resolution satellite images to tag any presumptive seals hauled out on the fast ice during the pupping period. To assess and reduce the error in counts in term of bias and other factors, we ranked surveyors on how well they agreed with each other in tagging a particular feature (presumptive seal), and then ranked these features based on the ranking of surveyors placing tags on them. We assumed that features with higher rankings, as determined by “the crowd wisdom,” were likely to be seals. By comparing citizen science feature ranks with an expert’s determination, we found that non-seal features were often highly ranked. Conversely, some seals were ranked low or not tagged at all. Ranking surveyors relative to their peers was not a reliable means to filter out erroneous or missed tags; therefore, we developed an effective correction factor for both sources of error by comparing surveyors’ tags to those by the expert. Furthermore, counts may underestimate true abundance due to seals not being present on the ice when the image was taken. Based on available on-the-ground haul-out location counts in Erebus Bay, the Ross Sea, we were able to correct for the proportion of seals not detected through satellite images after accounting for year, time-of-day, location (islet vs. mainland locations), and satellite sensor effects. We show that a prospective model performed well at estimating seal abundances at appropriate spatial scales, providing a suitable methodology for continent-wide Weddell Seal population estimates.

scholarly journals IMAGE RESOLUTION ENHANCEMENT OF SATELLITE IMAGES USING DYADIC INTEGER COEFFICIENT WAVELET FILTER FOR WAVELET TRANSFORM

2021 ◽  
Vol 9 (1) ◽  
pp. 971-975
Author(s):  
P.B. Chopade, P. N. Kota
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Satellite Images ◽  
Resolution Enhancement ◽  
Image Resolution ◽  
Integer Coefficient ◽  
Discrete Wavelet ◽  
Wavelet Filter ◽  
Hardware Complexity ◽  
Integer Coefficients

In this paper, image resolution enhancements for satellite images are proposed using dyadic integer coefficients based wavelet filter (DICWF). We proposes a technique in which discrete wavelet transform and stationary wavelet transform using DICWF which is used to obtain a high resolution image and this image is derived from frequency subbands. The satellite images play a very vital role now days in the development of technical aspects which needs to be enhanced. These satellite images are superresolved with the help of dyadic integer coefficient-based wavelet filters, which reduces the hardware complexity and computational difficulties due to the rational and integer coefficients of these filter banks. The value of the peak signal-to-noise ratio (PSNR) of the proposed method and the resultant visual images of the proposed method show the effectiveness of this algorithm over other existing algorithms using discrete wavelet transform. Noise can be minimized by applying thresholding on different frequency subbands which obtained by the application of DICWF to the noisy, blurred input images.

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