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Multispectral Classification of Remote Sensing Data for Geospatial Analysis

  • Duong Dang Khoi
  • Kondwani Godwin Munthali
Chapter

Abstract

Remote sensing data are one of the primary data sources for many geospatial analyses. The nature of remote sensing data acquisition ranges from ground-based to airborne to space-borne. There are two types of remote sensing: active and passive. Passive remote sensing sensors detect the natural radiation that is emitted from, or reflected by, the object or surrounding area being observed. Reflected sunlight is the most common source of radiation measured by passive sensors. Some examples of passive remote sensing satellites are Landsat MSS/TM/ETM+, SPOT, IKONOS, QuickBird, etc. Active remote sensing emits energy in order to scan objects, and then detects and measures the radiation that is reflected or back-scattered from the target. Radio detection and ranging (RADAR), light detection and ranging (LiDAR) and sound navigation and ranging (SONAR) are examples of active remote sensing where the time delay between emission and return is measured, thus establishing the location, height, speed and direction of an object.

Keywords

Cellular Automaton Accuracy Assessment Digital Number Cellular Automaton Model Unsupervised Classification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Japan 2012

Authors and Affiliations

  1. 1.Faculty of Land AdministrationHanoi University of Natural Resources and EnvironmentHanoiVietnam
  2. 2.Division of Spatial Information Science, Graduate School of Life and Environmental SciencesUniversity of TsukubaTsukubaJapan

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