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Vegetation Index Based on Genetic Programming for Bare Ground Detection in the Amazon

机译：基于遗传规划的亚马逊裸地植被植被指数

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摘要

Vegetation indices are algebraic combinations of spectral bands produced by satellite. The indices allow different vegetation covers to be identified by contrast evaluation. Vegetation indexes are used mainly in tasks of classification of satellite images, as well as chemical and physical land studies. An example is seen in the Normalized Difference Vegetation Index (NDVI) that shows up live green vegetation. This article describes the process of creating a new vegetation index that enables bare ground identification in the Amazon using genetic programming. It further shows how a threshold is automatically defined for the new index, a threshold that facilitates the task of photointer-pretation and is not normally provided for other vegetation indexes. The new index, called BGIGP (Bare Ground Index obtained using Genetic Programming) showed significant values of contrast between the different covers analyzed, being seen to compete well with traditional vegetation indexes such as SR. The performance of BGIGP was also evaluated using the characteristics of 10448-pixel images from the "2017 Kaggle Planet: Understanding the Amazon from Space" competition, to classify bare ground against water, cloudy, primary, cultivation, road, and artisanal mine, obtaining a 93.71% of accuracy.
机译：植被指数是卫星产生的光谱带的代数组合。该指数允许通过对比评估来识别不同的植被。植被指数主要用于卫星图像的分类任务以及化学和物理土地研究。在归一化植被指数（NDVI）中可以看到一个例子，该指数显示了活着的绿色植被。本文介绍了创建新的植被指数的过程，该过程可以使用基因编程在亚马逊地区进行裸地识别。它还进一步说明了如何为新指标自动定义一个阈值，该阈值有助于光解释任务，通常不会为其他植被指标提供该阈值。新的指数称为BGIGP（使用遗传编程获得的裸地指数），显示了所分析的不同覆盖层之间对比度的显着值，并且可以与SR等传统植被指数很好地竞争。还使用来自“ 2017 Kaggle Planet：从太空了解亚马逊”竞赛中的10448像素图像的特征对BGIGP的性能进行了评估，以对裸露的地面进行分类，以防止水，多云，主要，耕种，道路和手工采矿93.71％的准确性。

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