Progress in Geospatial Analysis
This book examines current trends and developments in the methods and applications of geospatial analysis and highlights future development prospects. It provides a comprehensive discussion of remote sensing- and geographical information system (GIS)-base
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Multispectral Classification of Remote Sensing Data for Geospatial Analysis Duong Dang Khoi and Kondwani Godwin Munthali
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Introduction
Remote sensing data are one of the primary data sources for many geospatial analyzes. 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. In the 1860s, the observation of the earth using a balloon became the starting point of what would later be called remote sensing (Lillesand et al. 2008). Observation of the earth from airborne platforms has a history of 150 years, but most of the technical
D.D. Khoi (*) Faculty of Land Administration, Hanoi University of Natural Resources and Environment, Vietnam Formerly in Division of Spatial Information Science, Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan e-mail: [email protected] K.G. Munthali Division of Spatial Information Science, Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan e-mail: [email protected] Y. Murayama (ed.), Progress in Geospatial Analysis, DOI 10.1007/978-4-431-54000-7_2, © Springer Japan 2012
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D.D. Khoi and K.G. Munthali
innovation and development has taken place in the last three decades. The launch of the first remote sensing satellite by the United States in 1972 paved the way for applications of remote sensing in studies of the earth’s resource management, including the management of forests. Satellite imagery is increasingly used in various fields such as agriculture, forestry, geology, hydrology, land use/cover change, oceans and coastal monitoring. Remote sensing images are processed and combined with other ancillary datasets in geographical information science (GIS) via spatial analysis techniques to provide significant information for environmental monitoring and management. This chapter presents a multispectral classification process for space-borne remote sensing data, which is the most important part of information extraction from satellite images. Here, we explain the complete procedures of multispectral classification with image preprocessing, image enhancement, supervised and unsupervised classification, training
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