Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30458
A Supervised Learning Data Mining Approach for Object Recognition and Classification in High Resolution Satellite Data

Authors: Mais Nijim, Rama Devi Chennuboyina, Waseem Al Aqqad

Abstract:

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.

Keywords: Data Mining, Remote Sensing, Object recognition, classification, Feature Extraction, waterbody identification

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1110770

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1633

References:


[1] A Adnan A. Y. Mustafa, Linda G. Shapiro and Mark A. Ganter,"3D Object Recognition from Color Intensity Images", 13th Int. Conf. on Pattern Recognition, Vienna, Austria, pp. 25-30, /August, 1996.
[2] Wang Xiang-yang, Sun Wei-wei a, Wu Zhi-fang, Yang Hong-ying, Wang Qin-yan “Color image segmentation using PDTDFB domain hidden Markov tree model” Applied Soft Computing 29 (2015) 138– 152.
[3] Yang Haibo, Wang Zongmin, Zhao Hongling, Guo Yu “Water body Extraction Methods Study Based on RS and GIS ” 2011 3rd International Conference on Environmental Science and Information Application Technology (ESIAT 2011).
[4] Yuqiang Wang, Renzong Ruan, Yuanjian SHE, Meichun YAN “Extraction of Water Information based on Radarsat Sar and Landsat ETM+ ” 2011 3rd International Conference on Environmental Science and Information Application Technology (ESIAT 2011).
[5] U. S. Geological Survey (USGS)” Landsat Orthorectified ETM+ Pan Sharpened” Sioux Falls, SD USA, USGS Earth Resources Observation and Science Center (EROS), https://lta.cr.usgs.gov/Tri_Dec_GLOO.
[6] Xiaoxiao Lia, B., Soe W. Myintb, Yujia Zhangb, Chritopher Gallettib, Xiaoxiang Zhangc,Billie L. Turner II “Object-based land-cover classification for metropolitan Phoenix, Arizona, using aerial photography” International Journal of Applied Earth Observation and Geoinformation 33 (2014) 321–330.
[7] Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth “From Data Mining to Knowledge Discovery in Databases “American Association for Artificial Intelligence. All rights reserved. 0738-4602- 1996.
[8] I. Witten, E. Frank “Data Mining: practical Machine Learning Tools and Techniques”.
[9] J. Zahang, W. Hsu and M. L. Lee. “An information-Driven Framework for image Mining”, in proceedings of 12th International Conference on Database and Expert Systems Applications.