首页> 外文会议>Asian Conference on Remote Sensing(ACRS2005); Asian Space Conference; 20051107-11; 20051107-11; Ha Noi(VN); Ha Noi(VN) >Land Cover Land Use Classification Using Multi-sensor,Multi-temporal Satellite Data; Mekong Delta, South Vietnam
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Land Cover Land Use Classification Using Multi-sensor,Multi-temporal Satellite Data; Mekong Delta, South Vietnam

机译:利用多传感器,多时间卫星数据进行土地覆被土地利用分类;越南南部湄公河三角洲

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摘要

Remote sensing data, Landsat-ETM, JERS-OPS/SAR data in Mekong River delta (Lat 10°30'-10°45', Lon 106°45'-107° 00') were processed for Land-cover Land-use classification. The delta, a flood plane of Mekong River, comprises of a river network formed due to huge freshwater supply from the Mekong basin for millions years. On the other hand during dry period, low river discharge tends to back flow of ocean water due to tides (3-4 m) along the drainage system, causing salt water intrusion into upstream results salt water contamination into the environment. Therefore, the land-cover consists of salt marshes with dense Mangrove forest including other kind of brackish water flora. JERS-OPS images were processed to discriminate the Land-cover classes. Not like in the other areas, wetlands consist of both freshwater and brackish water where, Agriculture, aquaculture, salt mashes. Capability of discriminating those classes are difficult using optical sensor data, hence multi sensor and multi-temporal approaches have been made to overcome the situation to improve the results. The processing technique adopted in this study is to identify major classes which contain similar spectral characteristics and mask out to segregate major classes. This will enhance the image seperability to perform secondary classification to each segregated major classes and avoid mixing among classes. Near Infra Red (NIR) band of optical sensors allowed to separate three major classes such as water, wetlands and vegetation. However, vegetation represents Mangrove forests, Plantations, paddy and other agricultural crops while the wetlands consist of salt marshes with sparse mangroves and other brackish water flora, mud flats, and aquaculture practices a challenge for image processing. Multi-temporal and multi sensor data allows to overcome the challenge by means of changing nature of land use pattern. Methodology and the results of this work are discussed in this paper in detail.
机译:处理了湄公河三角洲(10°30'-10°45',Lon 106°45'-107°00')的遥感数据,Landsat-ETM,JERS-OPS / SAR数据,用于土地覆盖土地利用分类。三角洲是湄公河的洪水平面,由数百万年来来自湄公河流域的大量淡水供应而形成的河网组成。另一方面,在干旱时期,由于潮汐(3-4 m)沿着排水系统,低河流量倾向于使海水倒流,导致盐水侵入上游,导致盐水污染环境。因此,土地覆盖层由盐沼和茂密的红树林组成,其中包括其他类型的淡水植物。对JERS-OPS图像进行处理以区分土地覆盖类别。与其他地区不同,湿地包括淡水和微咸水,其中包括农业,水产养殖业,盐浆。使用光学传感器数据来区分那些类别是困难的,因此已经提出了多传感器和多时间方法来克服这种情况以改善结果。本研究采用的处理技术是识别包含相似光谱特征的主要类别,并掩盖以分离主要类别。这将增强图像可分离性,以便对每个分离的主要类别进行二级分类,并避免类别之间的混淆。光学传感器的近红外(NIR)波段可将水,湿地和植被等三大类分开。但是,植被代表着红树林,人工林,稻田和其他农作物,而湿地则是盐沼,稀疏的红树林和其他咸淡的水生植物,泥滩和水产养殖,这对图像处理构成了挑战。多时相和多传感器数据可以通过改变土地利用方式的性质来克服挑战。本文详细讨论了方法和这项工作的结果。

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