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

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.

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 3D CHANGE DETECTION IN URBAN AREAS BASED ON DCNN USING A SINGLE IMAGE

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 89-95
Author(s):  
H. Amini Amirkolaee ◽  
H. Arefi
Keyword(s):  
Change Detection ◽  
Urban Areas ◽  
Satellite Images ◽  
Satellite Image ◽  
Light Detection And Ranging ◽  
Surface Model ◽  
Lidar Data ◽  
Single Image ◽  
Convolutional Network ◽  
Novel Approach

Abstract. In this paper, a novel approach is proposed for 3D change detection in urban areas using only a single satellite images. To this purpose, a dense convolutional neural network (DCNN) is utilized in order to estimate a digital surface model (DSM) from a single image. In this regard, a densely connected convolutional network is employed for feature extraction and an upsampling method based on dilated convolution is employed for estimating the height values. The proposed DCNN is trained using satellite and Light Detection and Ranging (LiDAR) data which are provided in 2012 from Isfahan, Iran. Subsequently, the trained network is utilized in order to estimate DSM of a single satellite image that is provided in 2006. Finally, the changed areas are detected by subtracting the estimated DSMs. Evaluating the accuracy of the detected changed areas indicates 66.59, 72.90 and 67.90 for correctness, completeness, and kappa, respectively.

Novel approach to identify good tracer clouds from a sequence of satellite images

2005 ◽  
Vol 43 (4) ◽  
pp. 813-818 ◽  
Author(s):  
A.K. Mandal ◽  
S. Pal ◽  
A.K. De ◽  
S. Mitra
Keyword(s):  
Satellite Images ◽  
Novel Approach ◽  
Good Tracer

scholarly journals A Novel Approach for Selective Reconstruction of Cloud-Contaminated Satellite Images

2011 ◽  
Vol 28 (8) ◽  
pp. 1028-1035 ◽  
Author(s):  
Bipasha Paul Shukla ◽  
P. K. Pal ◽  
P. C. Joshi
Keyword(s):  
High Resolution ◽  
Brightness Temperature ◽  
Satellite Imagery ◽  
Satellite Images ◽  
Weather Prediction ◽  
Climate Research ◽  
Clear Sky ◽  
Novel Approach ◽  
Sky Brightness ◽  
Very High

Abstract The paper presents a robust technique for cloud clearing of satellite imagery. The proposed algorithm combines mathematical morphological techniques with a conventional cloud clearing scheme to restore clear sky values. The derived equivalent clear sky brightness temperature plays a very important role in numerical weather prediction, climate research, and monitoring. The developed methodology uses distinct approaches for reconstruction of partially clouded domains and overcast regions. It is found that the algorithm is especially suitable for pre- or postmonsoon months, where there is a high percentage of partially cloudy and small overcast cloudy regions. The algorithm is tested for the Kalpana Very High Resolution Radiometer (VHRR) thermal infrared (TIR) band data acquired over the oceanic region adjoining India throughout the month of May 2009. It is found that the algorithm is able to clear 25% of cloudy pixels with an RMSE of 1.2 K for brightness temperature.

scholarly journals A Novel Approach for Cloud Detection in Scenes with Snow/Ice Using High Resolution Sentinel-2 Images

Atmosphere ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 44 ◽  
Author(s):  
Ling Han ◽  
Tingting Wu ◽  
Qing Liu ◽  
Zhiheng Liu
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Matched Filter ◽  
Spectral Characteristics ◽  
Cloud Detection ◽  
Novel Approach ◽  
Visible Wavelength Range ◽  
Number Frequency ◽  
Detection Effect ◽  
Sentinel 2

The recognition of snow versus clouds causes difficulties in cloud detection because of the similarity between cloud and snow spectral characteristics in the visible wavelength range. This paper presents a novel approach to distinguish clouds from snow to improve the accuracy of cloud detection and allow an efficient use of satellite images. Firstly, we selected thick and thin clouds from high resolution Sentinel-2 images and applied a matched filter. Secondly, the fractal digital number-frequency (DN-N) algorithm was applied to detect clouds associated with anomalies. Thirdly, spatial analyses, particularly spatial overlaying and hotspot analyses, were conducted to eliminate false anomalies. The results indicate that the method is effective for detecting clouds with various cloud covers over different areas. The resulting cloud detection effect possesses specific advantages compared to classic methods, especially for satellite images of snow and brightly colored ground objects with spectral characteristics similar to those of clouds.

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