scholarly journals Discussion of different Remote sensing satellite possibilities for scientifical Earth observations

2021 ◽  
Vol 264 ◽  
pp. 04007
Author(s):  
Aybek Arifjanov ◽  
Shamshodbek Akmalov ◽  
Shakhzod Shodiev ◽  
Abdukarim Haitov
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Water Discharge ◽  
Satellite Image ◽  
Research Area ◽  
Daily Monitoring ◽  
Medium Resolution ◽  
Very High Spatial Resolution ◽  
High Resolution Images ◽  
Very High

More than 1,000 satellites are launched into space, and they differ in their functions, rotation orbits, resolution, and other properties. Scientists divide the satellites into low-resolution, medium-resolution, high-resolution, and very high-resolution satellites by their properties. Now, the biggest challenge facing scientists is to use some of these different resolution images in their field. To get the expected result, it is very important to analyze the image that needs an which gives more accurate results. Therefore, the main attention of this article is aimed to find the answer to these problems. In this article 3 satellite images which have different resolution are analyzed. The possibility of middle-resolution images of MODIS, high-resolution images of Landsat, and very high-resolution images of WorldView-2 (WV-2) satellites using GIS are analyzed. A research area was the Syrdarya region, and downloaded different images of satellites of this area and compared with using e Cognition. According to the results, a more accurate satellite image for irrigation sets information is WorldView-2 images. In comparison analysis, it shows more accurate properties than other satellite images. As irrigation sets are small objects for the analysis, very high spatial resolution satellite images are important. Water discharge and surface change happen very fast; thus, it requires daily monitoring of the condition. And in this case, the temporal resolution of the MODIS and Landsat is 16 day, and it is a too long period.

scholarly journals Towards Sub-Pixel Automatic Geometric Corrections of Very-High Resolution Panchromatic Satellite Data of Urban Areas

2019 ◽  
Vol 11 (9) ◽  
pp. 1097 ◽  
Author(s):  
Aleš Marsetič ◽  
Peter Pehani
Keyword(s):  
Feature Extraction ◽  
High Resolution ◽  
Urban Areas ◽  
Satellite Images ◽  
Reference Data ◽  
Geometric Model ◽  
Control Point ◽  
Satellite Image ◽  
Extraction Algorithm ◽  
Very High

This paper presents an automatic procedure for the geometric corrections of very-high resolution (VHR) optical panchromatic satellite images. The procedure is composed of three steps: an automatic ground control point (GCP) extraction algorithm that matches the linear features that were extracted from the satellite image and reference data; a geometric model that applies a rational function model; and, the orthorectification procedure. Accurate geometric corrections can only be achieved if GCPs are employed to precisely correct the geometric biases of images. Due to the high resolution and the varied acquisition geometry of images, we propose a fast, segmentation based method for feature extraction. The research focuses on densely populated urban areas, which are very challenging in terms of feature extraction and matching. The proposed algorithm is capable of achieving results with a root mean square error of approximately one pixel or better, on a test set of 14 panchromatic Pléiades images. The procedure is robust and it performs well in urban areas, even for images with high off-nadir angles.

scholarly journals High Resolution Satellite Images for Instantaneous Shoreline Extraction Using New Enhancement Algorithms

Geosciences ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 123 ◽  
Author(s):  
Donatella Dominici ◽  
Sara Zollini ◽  
Maria Alicandro ◽  
Francesca Della Torre ◽  
Paolo Buscema ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Satellite Images ◽  
Human Life ◽  
Essential Element ◽  
Action Planning ◽  
High Resolution Satellite Images ◽  
High Resolution Images ◽  
Territorial Government ◽  
Very High

Knowledge of a territory is an essential element in any future planning action and in appropriate territorial and environmental requalification action planning. The current large-scale availability of satellite data, thanks to very high resolution images, provides professional users in the environmental, urban planning, engineering, and territorial government sectors, in general, with large amounts of useful data with which to monitor the territory and cultural heritage. Italy is experiencing environmental emergencies, and coastal erosion is one of the greatest threats, not only to the Italian heritage and economy, but also to human life. The aim of this paper is to find a rapid way of identifying the instantaneous shoreline. This possibility could help government institutions such as regions, civil protection, etc., to analyze large areas of land quickly. The focus is on instantaneous shoreline extraction in Ortona (CH, Italy), without considering tides, using WorldView-2 satellite images (50-cm resolution in panchromatic and 2 m in multispectral). In particular, the main purpose of this paper is to compare commercial software and ACM filters to test their effectiveness.

scholarly journals Assessment of Approaches for the Extraction of Building Footprints from Pléiades Images

2021 ◽  
Vol 15 (4) ◽  
pp. 101-116
Author(s):  
Lamyaa Gamal El-deen Taha ◽  
Rania Elsayed Ibrahim
Keyword(s):  
Support Vector Machines ◽  
Maximum Likelihood ◽  
High Resolution ◽  
Random Forest ◽  
Satellite Images ◽  
Support Vector ◽  
Morphological Operations ◽  
Vector Machines ◽  
High Resolution Images ◽  
Very High

The Marina area represents an official new gateway of entry to Egypt and the development of infrastructure is proceeding rapidly in this region. The objective of this research is to obtain building data by means of automated extraction from Pléiades satellite images. This is due to the need for efficient mapping and updating of geodatabases for urban planning and touristic development. It compares the performance of random forest algorithm to other classifiers like maximum likelihood, support vector machines, and backpropagation neural networks over the well-organized buildings which appeared in the satellite images. Images were subsequently classified into two classes: buildings and non-buildings. In addition, basic morphological operations such as opening and closing were used to enhance the smoothness and connectedness of the classified imagery.The overall accuracy for random forest, maximum likelihood, support vector machines, and backpropagation were 97%, 95%, 93% and 92% respectively. It was found that random forest was the best option, followed by maximum likelihood, while the least effective was the backpropagation neural network. The completeness and correctness of the detected buildings were evaluated. Experiments confirmed that the four classification methods can effectively and accurately detect 100% of buildings from very high-resolution images. It is encouraged to use machine learning algorithms for object detection and extraction from very high-resolution images.

scholarly journals MSLp: Deep Superresolution for Meteorological Satellite Image

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Liling Zhao ◽  
Hao Yu ◽  
Yan Wang
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Weather Forecasting ◽  
Satellite Image ◽  
Image Resolution ◽  
Complex Data ◽  
Opinion Score ◽  
Phase Loss ◽  
High Resolution Images ◽  
Meteorological Satellite

High-resolution meteorological satellite image is the basic data for weather forecasting, climate prediction, and early warning of various meteorological disasters. However, the poor image resolution is limited for both subjective and automated analyses. Through our investigation and study, we found that the meteorological satellite image is a kind of complex data with multimodal and multitemporal characteristics. Fortunately, based on zero-shot learning theory, the complexity of the meteorological satellite image can be used to enhance its own image resolution. In this work, we propose a novel framework called MSLp (Meteorological Satellite Loss phase). Specifically, we choose a zero-shot network as a backbone and propose a phase loss function. A mapping from low- to high-resolution meteorological satellite images was trained for improving the resolution by up to a factor of 4×. Our quantitative study demonstrates the superiority of the proposed approach over ZSSR and bicubic interpolation. For qualitative analysis, visual tests were performed by 7 meteorologists to confirm the utility of the proposed algorithm. The mean opinion score is 9.32 (the full score is 10). These meteorologists think that weather forecasters need higher-resolution meteorological satellite images and the high-resolution images obtained by our method have the potential to be a great help for weather analysis and forecasting.

scholarly journals Advances on coastal erosion assessment from satellite earth observations: exploring the use of Sentinel products along with very high resolution sensors

2020 ◽  
pp. 412-421
Author(s):  
Paula Gomes da Silva ◽  
Anne-Laure Beck ◽  
Jara Martinez Sanchez ◽  
Raúl Medina Santanmaria ◽  
Martin Jones ◽  
...  
Keyword(s):  
Time Series ◽  
High Resolution ◽  
High Frequency ◽  
Coastal Erosion ◽  
Satellite Images ◽  
Horizontal Shift ◽  
Earth Observations ◽  
High Resolution Images ◽  
Very High ◽  
Sentinel 2

This work proposes the use of automatic co-registered satellite images to obtain large, high frequency and highly accurate shorelines time series. High resolution images are used to co-register Landsat and Sentinel-2 images. 90% of the co-registered images presented vertical and horizontal shift lower than 3 m. Satellite derived shorelines presented errors lower than mission’s precision. A discussion is presented on the applicability of those shorelines through an application to Tordera Delta (Spain).

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.

scholarly journals Mask R-CNN and OBIA Fusion Improves the Segmentation of Scattered Vegetation in Very High-Resolution Optical Sensors

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 320
Author(s):  
Emilio Guirado ◽  
Javier Blanco-Sacristán ◽  
Emilio Rodríguez-Caballero ◽  
Siham Tabik ◽  
Domingo Alcaraz-Segura ◽  
...  
Keyword(s):  
High Resolution ◽  
Optical Sensors ◽  
Biotic Interactions ◽  
Relevant Information ◽  
Vegetation Monitoring ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Segmentation Methods ◽  
High Resolution Images ◽  
Very High

Vegetation generally appears scattered in drylands. Its structure, composition and spatial patterns are key controls of biotic interactions, water, and nutrient cycles. Applying segmentation methods to very high-resolution images for monitoring changes in vegetation cover can provide relevant information for dryland conservation ecology. For this reason, improving segmentation methods and understanding the effect of spatial resolution on segmentation results is key to improve dryland vegetation monitoring. We explored and analyzed the accuracy of Object-Based Image Analysis (OBIA) and Mask Region-based Convolutional Neural Networks (Mask R-CNN) and the fusion of both methods in the segmentation of scattered vegetation in a dryland ecosystem. As a case study, we mapped Ziziphus lotus, the dominant shrub of a habitat of conservation priority in one of the driest areas of Europe. Our results show for the first time that the fusion of the results from OBIA and Mask R-CNN increases the accuracy of the segmentation of scattered shrubs up to 25% compared to both methods separately. Hence, by fusing OBIA and Mask R-CNNs on very high-resolution images, the improved segmentation accuracy of vegetation mapping would lead to more precise and sensitive monitoring of changes in biodiversity and ecosystem services in drylands.

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