Evaluation of object-based image analysis techniques on very high-resolution satellite image for biomass estimation in a watershed of hilly forest of Nepal

2014 ◽  
Vol 6 (1) ◽  
pp. 59-68 ◽  
Author(s):  
Yousif Ali Hussin ◽  
Hammad Gilani ◽  
Louise van Leeuwen ◽  
M. S. R. Murthy ◽  
Rachna Shah ◽  
...  
Author(s):  
Aybek Arifjanov ◽  
Shamshodbek Akmalov ◽  
Tursunoy Apakhodjaeva ◽  
Dilmira Tojikhodjaeva

Currently, more than 300 satellites have been launched into space and providing us with information about the Earth and processes which happens in there. Those information is very useful in all branches. These satellites started to modify and modernize year by year. Especially after 2000, satellites of very high resolution were launched into space. These satellites are sending information with very high resolution. To improve the speed and accuracy of the analysis of these images, scientists have developed a number of methods and programs. As a result, users often find face to difficulties with knowing which method or program is most effective. In this article, analyzed many researches and scientific studies and analyzed WorldView-2 (WV2) images of the Syrdarya Province based on field experiments and outlined the advantages and disadvantages of the method and tool. WV2 images are very important and provide much relevant data for all image analysis. VHR of these images can increase the quality and possibilities of all analysis. But usage of these images globally has not developed because of their costs. Square of satellite image capturing is very little for global analysis. to do global analysis we need 100 s of this image. That is why scientists use this data more often for correlation or creating general methods. That is why it has not been used for regional and global analysis. In our research, we used GEOBIA’s eCognition software. The accuracy of this program is 95 %. In arid regions like Uzbekistan, we recommend optimal software, analyse steps and data.


2019 ◽  
Vol 1 ◽  
pp. 1-8
Author(s):  
Ankita Medhi ◽  
Ashis Kumar Saha

<p><strong>Abstract.</strong> Rural roads in India have been considered as significant component for overall rural development. In India, the status of rural road connectivity is not up to the mark in some of the states. For providing better connectivity in the rural areas the information on roads are considered important. Detailed mapping of the roads can be useful for planning further road connectivity and proving access to facilities in the rural areas. For detailed mapping of roads higher resolution satellite imageries are required. Object based Image Analysis (OBIA) has emerged as a promising map analysis approach using high and very high resolution imageries. Feature extraction is one of the important aspect in OBIA extracting features such as roads, buildings, water bodies and other important features of interest from the high resolution imageries. In the present study, an attempt has been made to extract rural roads of Titabor in Jorhat district of Assam (India). Various OBIA based extraction methods have been used for extracting roads using high &amp; very high resolution Resourcesat-II (5.8&amp;thinsp;m) and Kompsat imagery (2.8&amp;thinsp;m MS &amp;amp; 0.7&amp;thinsp;m PAN). The results have been compared and relative advantages were evaluated.</p>


10.29007/shdh ◽  
2018 ◽  
Author(s):  
Antonio Francipane ◽  
Francesca Mussomè ◽  
Giuseppe Cipolla ◽  
Leonardo Noto

An accurate mapping of gullies is important since they are still major contributors of sediment to streams. Mapping gullies in many areas is difficult because of the presence of dense canopy, which precludes the identification through aerial photogrammetry and other traditional remote sensing methods. Moreover, the wide spatial extent of some gullies makes their identification and characterization through field surveys a very large and expensive proposition.This work aims to develop an object-based image analysis (OBIA) to detect and map gullies based on a set of rules and morphological characteristics retrieved by very high resolution (VHR) imagery. A one-meter resolution LiDAR Digital Elevation Model (DEM) is used to derive different morphometric indexes, which are combined, by using different segmentation and classification rules, to identify gullies. The tool has been calibrated using, as reference, the perimeters of two relatively large gullies that have been measured during a field survey in the Calhoun Critical Zone Observatory (CCZO) area in the Southeastern United States.


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