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首页> 外文会议>2016 Al-Sadiq International Conference on Multidisciplinary in IT and Communication Techniques Science and Applications >Landsat-8 (OLI) classification method based on tasseled cap transformation features
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Landsat-8 (OLI) classification method based on tasseled cap transformation features

机译：基于tasseled cap变换特征的Landsat-8（OLI）分类方法

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摘要

The expected increase in world population requires a redoubling of efforts to increase agricultural yields including rice crop which is the main food of many countries of the world and in particular the countries of the Middle East. Therefore, estimating the size of the agricultural yield should be one of the first tasks of governments in the promotion of research to achieve food security and to study the obstacles resulting from environmental degradation. The Vegetation Indices (VIs) and the Tasseled Cap Transformation (TCT) features are particularly used to evaluate the presence and condition of vegetation, using satellite images. In this paper, new classification will introduced to harness these features to calculate the acreage of rice and compare them with the declared areas by the Ministry of Agriculture to make sure of their precisions. Typically, rice cultivation begins in the study area (Najaf Province) in mid-June and up to the top of the vegetative phase after 3 to 3.5 months (i.e. between September and October), then up to the harvest stage in thirty days (i.e. November). So, the present research will use Landsat-8 (Operational Land Imager OLI) images captured in September for the years 2013 and 2014 to ensure the validity of measurements. Among the many algorithms which have been developed for monitoring the biophysical characteristics of vegetation, only two of the six features of TCT (i.e. Brightness and Greenness have been adopted to use because they spectrally behave similarly like the Red and Infrared bands of the OLI images.
机译：预期的世界人口增长需要加倍努力，以增加包括水稻在内的农业单产，水稻是世界许多国家，特别是中东国家的主要粮食。因此，估计农业产量的大小应该是政府在促进研究以实现粮食安全和研究环境退化所造成的障碍方面的首要任务之一。植被指数（VIs）和流苏帽转换（TCT）功能特别用于通过卫星图像评估植被的存在和状况。在本文中，将引入新的分类方法，以利用这些功能来计算水稻的种植面积，并将其与农业部宣布的面积进行比较，以确保其精度。通常，水稻种植于6月中旬在研究区（纳贾夫省）开始，并在3至3.5个月（即9月至10月）之后直至营养期的顶部，然后直到30天（即收获期）开始。十一月）。因此，本研究将使用2013年和2014年9月捕获的Landsat-8（Operational Land Imager OLI）图像来确保测量的有效性。在已开发的用于监测植被生物物理特征的许多算法中，仅采用了TCT的六个特征中的两个（即亮度和绿色），因为它们的光谱行为类似于OLI图像的红色和红外波段。

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