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.

scholarly journals SAR Image Segmentation Using Morphological Attribute Profiles

2014 ◽  
Vol XL-3 ◽  
pp. 39-44 ◽  
Author(s):  
M. Boldt ◽  
A. Thiele ◽  
K. Schulz ◽  
S. Hinz
Keyword(s):  
Image Segmentation ◽  
High Resolution ◽  
Region Growing ◽  
Object Based Image Analysis ◽  
Sar Image Segmentation ◽  
High Resolution Imagery ◽  
Object Based ◽  
High Resolution Images ◽  
Novel Method ◽  
Image Set

In the last years, the spatial resolution of remote sensing sensors and imagery has continuously improved. Focusing on spaceborne Synthetic Aperture Radar (SAR) sensors, the satellites of the current generation (TerraSAR-X, COSMO-SykMed) are able to acquire images with sub-meter resolution. Indeed, high resolution imagery is visually much better interpretable, but most of the established pixel-based analysis methods have become more or less impracticable since, in high resolution images, self-sufficient objects (vehicle, building) are represented by a large number of pixels. Methods dealing with Object-Based Image Analysis (OBIA) provide help. Objects (segments) are groupings of pixels resulting from image segmentation algorithms based on homogeneity criteria. The image set is represented by image segments, which allows the development of rule-based analysis schemes. For example, segments can be described or categorized by their local neighborhood in a context-based manner. <br><br> In this paper, a novel method for the segmentation of high resolution SAR images is presented. It is based on the calculation of morphological differential attribute profiles (DAP) which are analyzed pixel-wise in a region growing procedure. The method distinguishes between heterogeneous and homogeneous image content and delivers a precise segmentation result.

scholarly journals Using very high resolution (VHR) imagery within a GEOBIA framework for gully mapping: an application to the Calhoun Critical Zone Observatory

2019 ◽  
Vol 22 (1) ◽  
pp. 219-234 ◽  
Author(s):  
A. Francipane ◽  
G. Cipolla ◽  
A. Maltese ◽  
G. La Loggia ◽  
L. V. Noto
Keyword(s):  
High Resolution ◽  
Critical Zone ◽  
Soil Productivity ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Critical Zone Observatory ◽  
Geographic Object ◽  
Widespread Availability ◽  
New Methodologies ◽  
Very High

Abstract Gully erosion is a form of accelerated erosion that may affect soil productivity, restrict land use, and lead to an increase of risk to infrastructure. An accurate mapping of these landforms can be difficult because of the presence of dense canopy and/or the wide spatial extent of some gullies. Even where possible, mapping of gullies through conventional field surveying can be an intensive and expensive activity. The recent widespread availability of very high resolution (VHR) imagery has led to a remarkable growth in the availability of terrain information, thus providing a basis for the development of new methodologies for analyzing Earth's surfaces. This work aims to develop a geographic object-based image analysis to detect and map gullies based on VHR imagery. A 1-meter resolution LIDAR DEM is used to identify gullies. The tool has been calibrated for two relatively large gullies surveyed in the Calhoun Critical Zone Observatory (CCZO) area in the southeastern United States. The developed procedure has been applied and tested on a greater area, corresponding to the Holcombe's Branch watershed within the CCZO. Results have been compared to previous works conducted over the same area, demonstrating the consistency of the developed procedure.

scholarly journals Rural Road Extraction using Object Based Image Analysis (OBIA): A case study from Assam, India

2019 ◽  
Vol 1 ◽  
pp. 1-8
Author(s):  
Ankita Medhi ◽  
Ashis Kumar Saha
Keyword(s):  
Image Analysis ◽  
High Resolution ◽  
Rural Areas ◽  
Extraction Methods ◽  
Rural Roads ◽  
Object Based Image Analysis ◽  
Rural Road ◽  
Object Based ◽  
The Status ◽  
Very High

<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>

scholarly journals Comparison of pixel to pixel and object-based image analysis with using WorldView-2 satellite images of Yangiobod village of Syrdarya province

2020 ◽  
Vol 26 (2) ◽  
pp. 313-321
Author(s):  
Aybek Arifjanov ◽  
Shamshodbek Akmalov ◽  
Tursunoy Apakhodjaeva ◽  
Dilmira Tojikhodjaeva
Keyword(s):  
Image Analysis ◽  
High Resolution ◽  
Field Experiments ◽  
Global Analysis ◽  
Satellite Image ◽  
Advantages And Disadvantages ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Image Capturing ◽  
Very High

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.

Sign in / Sign up

Export Citation Format

Share Document