A Supervised Learning Data Mining Approach for Object Recognition and Classification in High Resolution Satellite Data

Autor: Nijim, Mais, Chennuboyina, Rama Devi, Aqqad, Waseem Al
Jazyk: angličtina
Rok vydání: 2015
Předmět:
ISSN: 0738-4602
DOI: 10.5281/zenodo.1110770
Popis: Advances in spatial and spectral resolution of satellite images have led to tremendous growth in large image databases. The data we acquire through satellites, radars, and sensors consists of important geographical information that can be used for remote sensing applications such as region planning, disaster management. Spatial data classification and object recognition are important tasks for many applications. However, classifying objects and identifying them manually from images is a difficult task. Object recognition is often considered as a classification problem, this task can be performed using machine-learning techniques. Despite of many machine-learning algorithms, the classification is done using supervised classifiers such as Support Vector Machines (SVM) as the area of interest is known. We proposed a classification method, which considers neighboring pixels in a region for feature extraction and it evaluates classifications precisely according to neighboring classes for semantic interpretation of region of interest (ROI). A dataset has been created for training and testing purpose; we generated the attributes by considering pixel intensity values and mean values of reflectance. We demonstrated the benefits of using knowledge discovery and data-mining techniques, which can be on image data for accurate information extraction and classification from high spatial resolution remote sensing imagery.
{"references":["A Adnan A. Y. Mustafa, Linda G. Shapiro and Mark A. Ganter,\"3D\nObject Recognition from Color Intensity Images\", 13th Int. Conf. on\nPattern Recognition, Vienna, Austria, pp. 25-30, /August, 1996.","Wang Xiang-yang, Sun Wei-wei a, Wu Zhi-fang, Yang Hong-ying,\nWang Qin-yan \"Color image segmentation using PDTDFB domain\nhidden Markov tree model\" Applied Soft Computing 29 (2015) 138–\n152.","Yang Haibo, Wang Zongmin, Zhao Hongling, Guo Yu \"Water body\nExtraction Methods Study Based on RS and GIS \" 2011 3rd\nInternational Conference on Environmental Science and Information\nApplication Technology (ESIAT 2011).","Yuqiang Wang, Renzong Ruan, Yuanjian SHE, Meichun YAN\n\"Extraction of Water Information based on Radarsat Sar and Landsat\nETM+ \" 2011 3rd International Conference on Environmental Science\nand Information Application Technology (ESIAT 2011).","U. S. Geological Survey (USGS)\" Landsat Orthorectified ETM+ Pan\nSharpened\" Sioux Falls, SD USA, USGS Earth Resources Observation\nand Science Center (EROS), https://lta.cr.usgs.gov/Tri_Dec_GLOO.","Xiaoxiao Lia, B., Soe W. Myintb, Yujia Zhangb, Chritopher Gallettib,\nXiaoxiang Zhangc,Billie L. Turner II \"Object-based land-cover\nclassification for metropolitan Phoenix, Arizona, using aerial\nphotography\" International Journal of Applied Earth Observation and\nGeoinformation 33 (2014) 321–330.","Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth \"From\nData Mining to Knowledge Discovery in Databases \"American\nAssociation for Artificial Intelligence. All rights reserved. 0738-4602-\n1996.","I. Witten, E. Frank \"Data Mining: practical Machine Learning Tools and\nTechniques\".","J. Zahang, W. Hsu and M. L. Lee. \"An information-Driven Framework\nfor image Mining\", in proceedings of 12th International Conference on\nDatabase and Expert Systems Applications."]}
Databáze: OpenAIRE
Externí odkaz: