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

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.

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Characterizing the Spatial and Temporal Availability of Very High Resolution Satellite Imagery in Google Earth and Microsoft Bing Maps as a Source of Reference Data

Land ◽

10.3390/land7040118 ◽

2018 ◽

Vol 7 (4) ◽

pp. 118 ◽

Cited By ~ 18

Author(s):

Myroslava Lesiv ◽

Linda See ◽

Juan Laso Bayas ◽

Tobias Sturn ◽

Dmitry Schepaschenko ◽

...

Keyword(s):

High Resolution ◽

Satellite Imagery ◽

Urban Areas ◽

Reference Data ◽

Temporal Distribution ◽

Google Earth ◽

Training Data ◽

Visual Interpretation ◽

The Usa ◽

Very High

Very high resolution (VHR) satellite imagery from Google Earth and Microsoft Bing Maps is increasingly being used in a variety of applications from computer sciences to arts and humanities. In the field of remote sensing, one use of this imagery is to create reference data sets through visual interpretation, e.g., to complement existing training data or to aid in the validation of land-cover products. Through new applications such as Collect Earth, this imagery is also being used for monitoring purposes in the form of statistical surveys obtained through visual interpretation. However, little is known about where VHR satellite imagery exists globally or the dates of the imagery. Here we present a global overview of the spatial and temporal distribution of VHR satellite imagery in Google Earth and Microsoft Bing Maps. The results show an uneven availability globally, with biases in certain areas such as the USA, Europe and India, and with clear discontinuities at political borders. We also show that the availability of VHR imagery is currently not adequate for monitoring protected areas and deforestation, but is better suited for monitoring changes in cropland or urban areas using visual interpretation.

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Co-seismic surface effects from very high resolution panchromatic images: the case of the 2005 Kashmir (Pakistan) earthquake

Natural Hazards and Earth System Science ◽

10.5194/nhess-11-931-2011 ◽

2011 ◽

Vol 11 (3) ◽

pp. 931-943 ◽

Cited By ~ 26

Author(s):

M. Chini ◽

F. R. Cinti ◽

S. Stramondo

Keyword(s):

High Resolution ◽

Urban Areas ◽

Visual Inspection ◽

Satellite Images ◽

Surface Effects ◽

Automatic Classification ◽

The Other ◽

Inspection Method ◽

Panchromatic Image ◽

Very High

Abstract. The use of Very High Resolution (VHR) satellite panchromatic image is nowadays an effective tool to detect and investigate surface effects of natural disasters. We specifically examined the capabilities of VHR images to analyse earthquake features and detect changes based on the combination of visual inspection and automatic classification tools. In particular, we have used Quickbird (0.6 m spatial resolution) images for detecting the three main co-seismic surface features: damages, ruptures and landslides. The present approach has been applied to the 8 October 2005, Mw7.6 Kashmir, Pakistan, earthquake. We have focused our study in and around the main urban areas hit by the above earthquake specifically at Muzaffarabad and Balakot towns. The automatic classification techniques provided the best results wherever dealing with the damage to man-made structures and landslides. On the other hand, the visual inspection method demonstrated in addressing the identification of rupture traces and associated features. The synoptic view (concerning landslide, more than 190 millions of pixels have been automatically classified), the spatiotemporal sampling and the fast automatic damage detection using satellite images provided a reliable contribution to the prompt response during natural disaster and for the evaluation of seismic hazard as well.

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Discussion of different Remote sensing satellite possibilities for scientifical Earth observations

E3S Web of Conferences ◽

10.1051/e3sconf/202126404007 ◽

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.

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Urban areas analysis using satellite image segmentation and deep neural network

E3S Web of Conferences ◽

10.1051/e3sconf/201913501064 ◽

2019 ◽

Vol 135 ◽

pp. 01064

Author(s):

Vladimir Khryaschev ◽

Leonid Ivanovsky

Keyword(s):

Neural Networks ◽

High Resolution ◽

Convolutional Neural Networks ◽

Urban Areas ◽

High Performance ◽

Satellite Images ◽

Satellite Image ◽

Aerial Images ◽

Building Detection ◽

High Resolution Satellite Images

The goal of our research was to develop methods based on convolutional neural networks for automatically extracting the locations of buildings from high-resolution aerial images. To analyze the quality of developed deep learning algorithms, there was used Sorensen-Dice coefficient of similarity which compares results of algorithms with real masks. These masks were generated automatically from json files and sliced on smaller parts together with respective aerial photos before the training of developed convolutional neural networks. This approach allows us to cope with the problem of segmentation for high-resolution satellite images. All in all we show how deep neural networks implemented and launched on modern GPUs of high-performance supercomputer NVIDIA DGX-1 can be used to efficiently learn and detect needed objects. The problem of building detection on satellite images can be put into practice for urban planning, building control of some municipal objects, search of the best locations for future outlets etc.

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

Geocarto International ◽

10.1080/10106049.2021.1892207 ◽

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

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

Forests ◽

10.3390/f12010066 ◽

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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