{
  "title": "A Supervised Learning Data Mining Approach for Object Recognition and Classification in High Resolution Satellite Data",
  "abstract": "Advances in spatial and spectral resolution of satellite\r\nimages have led to tremendous growth in large image databases. The\r\ndata we acquire through satellites, radars, and sensors consists of\r\nimportant geographical information that can be used for remote\r\nsensing applications such as region planning, disaster management.\r\nSpatial data classification and object recognition are important tasks\r\nfor many applications. However, classifying objects and identifying\r\nthem manually from images is a difficult task. Object recognition is\r\noften considered as a classification problem, this task can be\r\nperformed using machine-learning techniques. Despite of many\r\nmachine-learning algorithms, the classification is done using\r\nsupervised classifiers such as Support Vector Machines (SVM) as the\r\narea of interest is known. We proposed a classification method,\r\nwhich considers neighboring pixels in a region for feature extraction\r\nand it evaluates classifications precisely according to neighboring\r\nclasses for semantic interpretation of region of interest (ROI). A\r\ndataset has been created for training and testing purpose; we\r\ngenerated the attributes by considering pixel intensity values and\r\nmean values of reflectance. We demonstrated the benefits of using\r\nknowledge discovery and data-mining techniques, which can be on\r\nimage data for accurate information extraction and classification from\r\nhigh spatial resolution remote sensing imagery.",
  "keywords": [
    "Remote sensing",
    "object recognition",
    "classification",
    "data mining",
    "waterbody identification",
    "feature extraction."
  ],
  "authors": [
    "Mais Nijim",
    "Rama Devi Chennuboyina",
    "Waseem Al Aqqad"
  ],
  "values": 9,
  "issue": 12,
  "issn": null,
  "page_start": 2516,
  "page_end": 5,
  "year": "2015",
  "doi": "https://doi.org/10.5281/zenodo.1110770",
  "journal": "International Journal of Information, Control and Computer Sciences",
  "categories": [
    "Computer and Information Engineering"
  ],
  "files": [
    "http://scholarly.org/pdf/display/a-supervised-learning-data-mining-approach-for-object-recognition-and-classification-in-high-resolution-satellite-data"
  ]
}