scholarly journals Potentialities and Limitations of Research on VHRS Data: Alexander the Great’s Military Camp at Gaugamela on the Navkur Plain in Kurdish Iraq as a Test Case

2021 ◽  
Vol 13 (5) ◽  
pp. 904
Author(s):  
Tomasz Pirowski ◽  
Michał Marciak ◽  
Marcin Sobiech
Keyword(s):  
Image Processing ◽  
High Resolution ◽  
Vegetation Indices ◽  
Test Case ◽  
Linear Features ◽  
Negative Results ◽  
Research Perspectives ◽  
Military Camp ◽  
Different Color ◽  
Very High

This paper presents a selected aspect of research conducted within the Gaugamela Project, which seeks to finally identify the location of one of the most important ancient battles: the Battle of Gaugamela (331 BCE). The aim of this study was to discover material remains of the Macedonian military camp on the Navkur Plain in Kurdish Iraq. For this purpose, three very high resolution satellite (VHRS) datasets from Pleiades and WorldView-2 were acquired and subjected to multi-variant image processing (development of different color composites, integration of multispectral and panchromatic images, use of principle component analysis transformation, use of vegetation indices). Documentation of photointerpretation was carried out through the vectorization of features/areas. Due to the character of the sought-after artifacts (remnants of a large enclosure), features were categorized into two types: linear features and areal features. As a result, 19 linear features and 2 areal features were found in the study area of the Mahad hills. However, only a few features fulfilled the expected geometric criteria (layout and size) and were subjected to field groundtruthing, which ended in negative results. It is concluded that no traces have been found that could be interpreted as remnants of an earthen enclosure capable of accommodating around 47,000 soldiers. Further research perspectives are also suggested.

scholarly journals PRECISION VITICULTURE FROM MULTITEMPORAL, MULTISPECTRAL VERY HIGH RESOLUTION SATELLITE DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 919-925
Author(s):  
Z. Kandylakis ◽  
K. Karantzalos
Keyword(s):  
High Resolution ◽  
Satellite Data ◽  
Precision Agriculture ◽  
Vegetation Indices ◽  
Quality Parameters ◽  
Total Phenolic ◽  
Canopy Reflectance ◽  
Northern Greece ◽  
In Situ Data ◽  
Very High

In order to exploit efficiently very high resolution satellite multispectral data for precision agriculture applications, validated methodologies should be established which link the observed reflectance spectra with certain crop/plant/fruit biophysical and biochemical quality parameters. To this end, based on concurrent satellite and field campaigns during the veraison period, satellite and in-situ data were collected, along with several grape samples, at specific locations during the harvesting period. These data were collected for a period of three years in two viticultural areas in Northern Greece. After the required data pre-processing, canopy reflectance observations, through the combination of several vegetation indices were correlated with the quantitative results from the grape/must analysis of grape sampling. Results appear quite promising, indicating that certain key quality parameters (like brix levels, total phenolic content, brix to total acidity, anthocyanin levels) which describe the oenological potential, phenolic composition and chromatic characteristics can be efficiently estimated from the satellite data.

Real-Time Unwrapping and Rectification of High-Resolution Catadioptric Panoramic Video during Non-Stationary Moving

2014 ◽  
Vol 668-669 ◽  
pp. 1098-1101
Author(s):  
Jian Wang ◽  
Zhen Hai Zhang ◽  
Ke Jie Li ◽  
Hai Yan Shao ◽  
Tao Xu ◽  
...  
Keyword(s):  
Image Processing ◽  
High Resolution ◽  
Real Time ◽  
Stationary State ◽  
Vision System ◽  
Image Rectification ◽  
Panoramic Vision ◽  
Panoramic Video ◽  
Environment Perception ◽  
Very High

Catadioptric panoramic vision system has been widely used in many fields, and also plays a very important role in environment perception of unmanned platform especially. However, the resolution of system is not very high, usually less than 5 million pixels at present. Even if the resolution is high, but the unwrapping and rectification of panoramic video is carried out off-line. Further, the system is also applied in stationary state or low stationary moving. This paper proposes an unwrapping and rectification method based on high-resolution catadioptric panoramic vision system used during non-stationary moving. It can segment dynamic circular mark region accurately and get the coordinates of center of circular image real-timely, shorten the time of image processing, meanwhile the coordinates of center and radius of the circular mark region would be obtained, so the image distortion caused by inaccurate center coordinates can be reduced. During image rectification, after achieving radial distortions parameters (K1, K2, K3), decentering distortions parameters (P1, P2), and the correction factor that has no physical meanings, we can used those for fitting the rectification polynomial, so the panoramic video can be rectified without distortion.

scholarly journals DETECTION OF ORTHOIMAGE MOSAICKING SEAMLINES BY MEANS OF WAVELET TRANSFORM

2016 ◽  
Vol XLI-B4 ◽  
pp. 45-50 ◽  
Author(s):  
K. Pyka
Keyword(s):  
Wavelet Transform ◽  
High Resolution ◽  
Edge Detection ◽  
Test Case ◽  
Alignment Method ◽  
Multiresolution Edge Detection ◽  
Very High ◽  
Redundant Wavelet Transform

The detection of orthoimage mosaicking seamlines by means of wavelet transform was examined. Radiometric alignment was omitted, giving priority to the issue of seamlines which bypass locations where there is a parallax between orthoimages. The importance of this issue is particularly relevant for images with very high resolution. In order to create a barrier image between orthoimages, the redundant wavelet transform variant known as MODWT-MRA was used. While more computationally complex than the frequently used DWT, it enables very good multiresolution edge detection. An IT prototype was developed on the basis of the described concept, and several cases of seamline detection were tested on the basis of data with a resolution of 10 cm to 1 m. The correct seamline location was obtained for each test case. This result opens the door to future expansion of the radiometric alignment method, which is also based on wavelets.

scholarly journals An Integrated GIS and Remote Sensing Approach for Monitoring Harvested Areas from Very High-Resolution, Low-Cost Satellite Images

2019 ◽  
Vol 11 (21) ◽  
pp. 2539
Author(s):  
Azadeh Abdollahnejad ◽  
Dimitrios Panagiotidis ◽  
Lukáš Bílek
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Satellite Images ◽  
Sustainable Forest Management ◽  
Low Cost ◽  
Vegetation Indices ◽  
Cost Effective ◽  
Monitoring Method ◽  
Gis And Remote Sensing ◽  
Very High

Advanced monitoring and mapping of forest areas using the latest technological advances in satellite imagery is an alternative solution for sustainable forest management compared to conventional ground measurements. Remote sensing products have been a key source of information and cost-effective options for monitoring changes in harvested areas. Despite recent advances in satellite technology with a broad variety of spectral and temporal resolutions, monitoring the areal extent of harvested forest areas in managed forests is still a challenge, primarily due to the highly dynamic spatiotemporal patterns of logging activities. Our goal was to introduce a plot-based method for monitoring harvested forest areas from very high-resolution (VHR), low-cost satellite images. Our method encompassed two data categories, which included vegetation indices (VIs) and texture analysis (TA). Each group of data was used to model the amount of harvested volume both independently and in combination. Our results indicated that the composition of all spectral bands can improve the accuracy of all models of average volume by 23.52 RMSE reduction and total volume by 33.57 RMSE reduction. This method demonstrated that monitoring and extrapolation of the calculated relation and results from smaller forested areas could be applied as an automatic remote-based supervised monitoring method over larger forest areas.

Detecting and Enumerating New Building Structures Utilizing Very‐High Resolution Imaged Data and Image Processing

2001 ◽  
Vol 16 (1) ◽  
pp. 71-84 ◽  
Author(s):  
DongMei Chen ◽  
Douglas Stow ◽  
Scott Daeschner ◽  
Linda Tucker
Keyword(s):  
Image Processing ◽  
High Resolution ◽  
Building Structures ◽  
Very High

scholarly journals Detection of Archaeological Surface Ceramics Using Deep Learning Image-Based Methods and Very High-Resolution UAV Imageries

Land ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1365
Author(s):  
Athos Agapiou ◽  
Athanasios Vionis ◽  
Giorgos Papantoniou
Keyword(s):  
Image Processing ◽  
High Resolution ◽  
Near Infrared ◽  
Google Earth ◽  
Single Shot ◽  
Archaeological Survey ◽  
Cultural Biography ◽  
First Case ◽  
Surface Survey ◽  
Very High

Mapping surface ceramics through systematic pedestrian archaeological survey is considered a consistent method to recover the cultural biography of sites within a micro-region. Archaeologists nowadays conduct surface survey equipped with navigation devices counting, documenting, and collecting surface archaeological potsherds within a set of plotted grids. Recent advancements in unmanned aerial vehicles (UAVs) and image processing analysis can be utilised to support such surface archaeological investigations. In this study, we have implemented two different artificial intelligence image processing methods over two areas of interest near the present-day village of Kophinou in Cyprus, in the Xeros River valley. We have applied a random forest classifier through the Google Earth Engine big data cloud platform and a Single Shot Detector neural network in the ArcGIS Pro environment. For the first case study, the detection was based on red–green–blue (RGB) high-resolution orthophotos. In contrast, a multispectral camera covering both the visible and the near-infrared parts of the spectrum was used in the second area of investigation. The overall results indicate that such an approach can be used in the future as part of ongoing archaeological pedestrian surveys to detect scattered potsherds in areas of archaeological interest, even if pottery shares a very high spectral similarity with the surface.

scholarly journals Automatic Near-Real-Time Image Processing Chain for Very High Resolution Optical Satellite Data

2015 ◽  
Vol XL-7/W3 ◽  
pp. 669-676 ◽  
Author(s):  
K. Oštir ◽  
K. Čotar ◽  
A. Marsetič ◽  
P. Pehani ◽  
M. Perše ◽  
...  
Keyword(s):  
Image Processing ◽  
High Resolution ◽  
Satellite Data ◽  
Satellite Image ◽  
Atmospheric Correction ◽  
Automatic Detection ◽  
Initial Development ◽  
Processing Chain ◽  
Optical Satellite Data ◽  
Very High

In response to the increasing need for automatic and fast satellite image processing SPACE-SI has developed and implemented a fully automatic image processing chain STORM that performs all processing steps from sensor-corrected optical images (level 1) to web-delivered map-ready images and products without operator's intervention. <br><br> Initial development was tailored to high resolution RapidEye images, and all crucial and most challenging parts of the planned full processing chain were developed: module for automatic image orthorectification based on a physical sensor model and supported by the algorithm for automatic detection of ground control points (GCPs); atmospheric correction module, topographic corrections module that combines physical approach with Minnaert method and utilizing anisotropic illumination model; and modules for high level products generation. Various parts of the chain were implemented also for WorldView-2, THEOS, Pleiades, SPOT 6, Landsat 5-8, and PROBA-V. Support of full-frame sensor currently in development by SPACE-SI is in plan. <br><br> The proposed paper focuses on the adaptation of the STORM processing chain to very high resolution multispectral images. The development concentrated on the sub-module for automatic detection of GCPs. The initially implemented two-step algorithm that worked only with rasterized vector roads and delivered GCPs with sub-pixel accuracy for the RapidEye images, was improved with the introduction of a third step: super-fine positioning of each GCP based on a reference raster chip. The added step exploits the high spatial resolution of the reference raster to improve the final matching results and to achieve pixel accuracy also on very high resolution optical satellite data.

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