{
  "id": "1608.00501",
  "version": "v1",
  "published": "2016-08-01T17:24:30.000Z",
  "updated": "2016-08-01T17:24:30.000Z",
  "title": "Supervised Classification of RADARSAT-2 Polarimetric Data for Different Land Features",
  "authors": [
    "Abhishek Maity"
  ],
  "comment": "3 pages, 3 figures, 2 tables",
  "categories": [
    "cs.CV"
  ],
  "abstract": "The pixel percentage belonging to the user defined area that are assigned to cluster in a confusion matrix for RADARSAT-2 over Vancouver area has been analysed for classification. In this study, supervised Wishart and Support Vector Machine (SVM) classifiers over RADARSAT-2 (RS2) fine quadpol mode Single Look Complex (SLC) product data is computed and compared. In comparison with conventional single channel or dual channel polarization, RADARSAT-2 is fully polarimetric, making it to offer better land feature contrast for classification operation.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2016-08-01T17:24:30.000Z"
    }
  ],
  "analyses": {
    "subjects": [
      "I.4",
      "I.5.3"
    ],
    "keywords": [
      "polarimetric data",
      "supervised classification",
      "offer better land feature contrast",
      "quadpol mode single look complex",
      "fine quadpol mode single look"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 3,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}