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 PANSHARPENING TECHNIQUES TO DETECT MASS MONUMENT DAMAGING IN IRAQ

2017 ◽  
Vol XLII-5/W1 ◽  
pp. 121-126 ◽  
Author(s):  
V. Baiocchi ◽  
A. Bianchi ◽  
C. Maddaluno ◽  
M. Vidale
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Coastal Area ◽  
Satellite Images ◽  
Multispectral Imagery ◽  
First Report ◽  
Aerial Photogrammetry ◽  
Definition Of ◽  
Survey Methodologies ◽  
Very High

The recent mass destructions of monuments in Iraq cannot be monitored with the terrestrial survey methodologies, for obvious reasons of safety. For the same reasons, it’s not advisable the use of classical aerial photogrammetry, so it was obvious to think to the use of multispectral Very High Resolution (VHR) satellite imagery. Nowadays VHR satellite images resolutions are very near airborne photogrammetrical images and usually they are acquired in multispectral mode. The combination of the various bands of the images is called pan-sharpening and it can be carried on using different algorithms and strategies. The correct pansharpening methodology, for a specific image, must be chosen considering the specific multispectral characteristics of the satellite used and the particular application. In this paper a first definition of guidelines for the use of VHR multispectral imagery to detect monument destruction in unsafe area, is reported. <br><br> The proposed methodology, agreed with UNESCO and soon to be used in Libya for the coastal area, has produced a first report delivered to the Iraqi authorities. Some of the most evident examples are reported to show the possible capabilities of identification of damages using VHR images.

scholarly journals shorelines of the Holocene Megalake Chad (Africa, Sahara) investigated with very high resolution satellite imagery (Pléiades) : example of the Goz Kerki paleo-spit

2014 ◽  
pp. 63-68 ◽  
Author(s):  
Mathieu Schuster ◽  
Claude Roquin ◽  
Abderamane Moussa ◽  
Jean-François Ghienne ◽  
Philippe Duringer ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Satellite Images ◽  
Field Surveys ◽  
Large Size ◽  
High Resolution Satellite Imagery ◽  
Sahel Region ◽  
Lake Deposits ◽  
Humid Period ◽  
Very High

Megalake Chad (350,000 km2), the largest paleo-lake of the Sahara-Sahel area, is one of the most emblematic marker of the hydroclimatic changes that occurred during the African Humid Period (AHP; ca. 11,500 — 5,000 years BP) in subtropical Africa. From field surveys, the existence of Megalake Chad is well demonstrated by widespread typical lake deposits. However, considering the very large size of this paleo-lake, it is best evidenced and understood from space. Conspicuous paleo-littoral features distributed along hundreds of kilometers are clearly visible on second generation satellite images. These features represent major archives of the Megalake Chad and of the climate during the AHP. This paper is the first attempt to investigate the paleo-littoral of Megalake Chad with very high resolution satellite imagery. A Pléiades scene (images and DEM) is used to characterize the fossil sand spit of the Goz Kerki, which is one of the most representative and best preserved littoral features of Megalake Chad. Thanks to Pléiades stereoscopic images the geomorphology and the lithology of this paleo-spit can now be detailed and the evolution of the paleo-bathymetry of Megalake Chad can be reconstructed. This brings new insights into the paleo-environments and paleo-climates of the Sahara-Sahel region.

scholarly journals Irrigation network extraction in arid regions with using worldview-2 satellite data

2021 ◽  
Vol 264 ◽  
pp. 03012
Author(s):  
Shamshod Akmalov ◽  
Luqmon Samiev ◽  
Tursunoy Apakhodjaeva ◽  
Dinislom Atakulov ◽  
Sarvar Melikuziyev
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Arid Region ◽  
Satellite Images ◽  
Extraction Methods ◽  
Water Extraction ◽  
Water Supply Systems ◽  
Irrigation Systems ◽  
Irrigation Network ◽  
Very High

After the 2000s, the launch of very high-resolution satellites provided great water and irrigation network management personnel opportunities. Now, the water management staff have the opportunity to study and monitor water supply systems and exploitation conditions of irrigation systems remotely via satellite imagery. By using those satellite images, specialists can search for water bodies, detect defected place of irrigation systems, and monitor their technical condition. Another advantage of satellite imagery is that they capture large areas of the Earth, keeping water systems under control in large areas. Therefore, the use of very high-resolution images has greatly developed in the water branch since the 2000s. The creation of different water extraction methods, models, indexes, and using different layers in the analysis for different regions using different satellites with very high resolution is developed. These indexes and layers are so numerous that they are now over 100. The user has difficulty getting any of them in the analyzes. Therefore, in this article, we have studied more than 50 water extraction methods, which gave positive and accurate results in an arid region. From those 50 methods, separated 10 the most effective methods and tested with WorldView-2 image analysis in the arid region and the water-rich region of Syrdarya region. According to the results of the analysis recommend the highest accuracy method for arid areas. Results show that water extraction using NIR2 layer of the WorldView-2 satellite images is the most accurate method than other methods. The accuracy of the results was 94 %. The analysis found the irrigation systems filled with sand and vegetation.

An Approach for Building Rooftop Border Extraction from Very High-Resolution Satellite Images

2021 ◽  
pp. 1-11
Author(s):  
Yasser Mostafa ◽  
Mahmoud Nokrashy O. Ali ◽  
Faten Mostafa ◽  
Mohamed Yousef
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
High Resolution Satellite Images ◽  
Very High

scholarly journals Using U-Net-Like Deep Convolutional Neural Networks for Precise Tree Recognition in Very High Resolution RGB (Red, Green, Blue) Satellite Images

Forests ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 66
Author(s):  
Kirill A. Korznikov ◽  
Dmitry E. Kislov ◽  
Jan Altman ◽  
Jiří Doležal ◽  
Anna S. Vozmishcheva ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Satellite Images ◽  
Russian Far East ◽  
Pinus Koraiensis ◽  
Accuracy Score ◽  
Learning Approaches ◽  
Individual Tree ◽  
Populus Suaveolens ◽  
Very High

Very high resolution satellite imageries provide an excellent foundation for precise mapping of plant communities and even single plants. We aim to perform individual tree recognition on the basis of very high resolution RGB (red, green, blue) satellite images using deep learning approaches for northern temperate mixed forests in the Primorsky Region of the Russian Far East. We used a pansharpened satellite RGB image by GeoEye-1 with a spatial resolution of 0.46 m/pixel, obtained in late April 2019. We parametrized the standard U-Net convolutional neural network (CNN) and trained it in manually delineated satellite images to solve the satellite image segmentation problem. For comparison purposes, we also applied standard pixel-based classification algorithms, such as random forest, k-nearest neighbor classifier, naive Bayes classifier, and quadratic discrimination. Pattern-specific features based on grey level co-occurrence matrices (GLCM) were computed to improve the recognition ability of standard machine learning methods. The U-Net-like CNN allowed us to obtain precise recognition of Mongolian poplar (Populus suaveolens Fisch. ex Loudon s.l.) and evergreen coniferous trees (Abies holophylla Maxim., Pinus koraiensis Siebold & Zucc.). We were able to distinguish species belonging to either poplar or coniferous groups but were unable to separate species within the same group (i.e. A. holophylla and P. koraiensis were not distinguishable). The accuracy of recognition was estimated by several metrics and exceeded values obtained for standard machine learning approaches. In contrast to pixel-based recognition algorithms, the U-Net-like CNN does not lead to an increase in false-positive decisions when facing green-colored objects that are similar to trees. By means of U-Net-like CNN, we obtained a mean accuracy score of up to 0.96 in our computational experiments. The U-Net-like CNN recognizes tree crowns not as a set of pixels with known RGB intensities but as spatial objects with a specific geometry and pattern. This CNN’s specific feature excludes misclassifications related to objects of similar colors as objects of interest. We highlight that utilization of satellite images obtained within the suitable phenological season is of high importance for successful tree recognition. The suitability of the phenological season is conceptualized as a group of conditions providing highlighting objects of interest over other components of vegetation cover. In our case, the use of satellite images captured in mid-spring allowed us to recognize evergreen fir and pine trees as the first class of objects (“conifers”) and poplars as the second class, which were in a leafless state among other deciduous tree species.

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