Object based building footprint detection from high resolution multispectral satellite image usingK-means clustering algorithm and shape parameters

2018 ◽  
Vol 34 (6) ◽  
pp. 626-643 ◽  
Author(s):  
Nitin Laxmanrao Gavankar ◽  
Sanjay Kumar Ghosh
Keyword(s):  
High Resolution ◽  
Clustering Algorithm ◽  
Satellite Image ◽  
Shape Parameters ◽  
Object Based ◽  
Building Footprint ◽  
Multispectral Satellite Image

scholarly journals Graph – Based High Resolution Satellite Image Segmentation for Object Recognition

2014 ◽  
Vol XL-8 ◽  
pp. 913-917 ◽  
Author(s):  
K. Ravali ◽  
M. V. Ravi Kumar ◽  
K. Venugopala Rao
Keyword(s):  
Image Segmentation ◽  
High Resolution ◽  
Land Cover ◽  
Satellite Image ◽  
Spatial Relations ◽  
Visual Interpretation ◽  
Object Based ◽  
Higher Weights ◽  
Dual Core ◽  
Cover Graph

Object based image processing and analysis is challenging research in very high resolution satellite utilisation. Commonly ei ther pixel based classification or visual interpretation is used to recognize and delineate land cover categories. The pixel based classification techniques use rich spectral content of satellite images and fail to utilise spatial relations. To overcome th is drawback, traditional time consuming visual interpretation methods are being used operational ly for preparation of thematic maps. This paper addresses computational vision principles to object level image segmentation. In this study, computer vision algorithms are developed to define the boundary between two object regions and segmentation by representing image as graph. Image is represented as a graph G (V, E), where nodes belong to pixels and, edges (E) connect nodes belonging to neighbouring pixels. The transformed Mahalanobis distance has been used to define a weight function for partition of graph into components such that each component represents the region of land category. This implies that edges between two vertices in the same component have relatively low weights and edges between vertices in different components should have higher weights. The derived segments are categorised to different land cover using supervised classification. <br><br> The paper presents the experimental results on real world multi-spectral remote sensing images of different landscapes such as Urban, agriculture and mixed land cover. Graph construction done in C program and list the run time for both graph construction and segmentation calculation on dual core Intel i7 system with 16 GB RAM, running 64bit window 7.

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.

Development of Building Height Data in Peru from High-Resolution SAR Imagery

2014 ◽  
Vol 9 (6) ◽  
pp. 1042-1049 ◽  
Author(s):  
Wen Liu ◽  
◽  
Fumio Yamazaki ◽  
Bruno Adriano ◽  
Erick Mas ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Image ◽  
Ground Surface ◽  
Sar Images ◽  
Building Height ◽  
Radar Sensors ◽  
Gis Data ◽  
Building Footprint ◽  
Image Displays ◽  
Sar Imagery

Building data, such as footprint and height, are important information for pre- and post-event damage assessments when natural disasters occur. However, these data are not easily available in many countries. Because of the remarkable improvements in radar sensors, high-resolution (HR) Synthetic Aperture Radar (SAR) images can provide detailed ground surface information. Thus, it is possible to observe a single building using HR SAR images. In this study, a new method is developed to detect building heights automatically from two-dimensional (2D) geographic information system (GIS) data and a single HR TerraSAR-X (TSX) intensity image. A building in a TSX image displays a layover from the actual position to the direction of the sensor, because of the side-looking nature of the SAR. Since the length of the layover on a ground-range SAR image is proportional to the building height, it can be used to estimate this height. We shift the building footprint obtained from 2D GIS data toward the sensor direction. The proposed method was applied to a TSX image of Lima, Peru in the HighSpot mode with a resolution of about 1 m. The results were compared with field survey photos and an optical satellite image, and a reasonable level of accuracy was achieved.

scholarly journals Object-Based Classification of Grasslands from High Resolution Satellite Image Time Series Using Gaussian Mean Map Kernels

2017 ◽  
Vol 9 (7) ◽  
pp. 688 ◽  
Author(s):  
Mailys Lopes ◽  
Mathieu Fauvel ◽  
Stéphane Girard ◽  
David Sheeren
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Satellite Image ◽  
Object Based ◽  
High Resolution Satellite Image
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