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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Crop Classification Based on Feature Band Set Construction and Object-Oriented Approach Using Hyperspectral Images
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Crop Classification Based on Feature Band Set Construction and Object-Oriented Approach Using Hyperspectral Images

机译：基于特征带集构建和高光谱图像面向对象方法的作物分类

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摘要

Remote sensing plays a significant role for crop classification. Accurate crop classification is a common requirement to precision agriculture, including crop area estimation, crop yield estimation, precision crop management, etc. This paper developed a new crop classification method involving the construction and optimization of the vegetation feature band set (FBS) and combination of FBS and object-oriented classification (OOC) approach. In addition to the spectral and textural features of the original image, 20 spectral indices sensitive to the vegetation's biological parameters are added to the FBS to distinguish specific vegetation. A spectral dimension optimization algorithm of FBS based on class-pair separability (CPS) is also proposed to improve the separability between class pairs while reducing data redundancy. OOC approach is conducted on the optimized FBS based on CPS to reduce the salt-and-pepper noise. The proposed classification method was validated by two airborne hyperspectral images. The first image acquired in an agricultural area of Japan includes seven crop types, and the second image acquired in a rice breeding area consists of six varieties of rice. For the first image, the proposed method distinguished different vegetation with an overall accuracy of 97.84% and kappa coefficient of 0.96. For the second image, the proposed method distinguished the rice varieties accurately, achieving the highest overall accuracy (98.65%) and kappa coefficient (0.98). Results demonstrate that the proposed method can significantly improve crop classification accuracy and reduce edge effects, and that textural features combined with spectral indices sensitive to the chlorophyll, carotenoid, and Anthocyanin indicators contribute significantly to crop classification. Therefore, it is an effective approach for classifying crop species, monitoring invasive species, as well as precision agriculture related applications.
机译：遥感在作物分类中起着重要作用。精确的农作物分类是精准农业的普遍要求，包括作物面积估算，农作物产量估算，精准农作物管理等。本文开发了一种新的农作物分类方法，涉及植被特征带集（FBS）的构建和优化及其组合。 FBS和面向对象分类（OOC）方法的概述。除了原始图像的光谱和纹理特征外，还将对植被的生物学参数敏感的20个光谱指数添加到FBS中，以区分特定的植被。提出了基于类对可分离性（CPS）的FBS频谱尺寸优化算法，以提高类对之间的可分离性，同时减少数据冗余。在基于CPS的优化FBS上执行OOC方法，以减少盐和胡椒的噪声。通过两个航空高光谱图像验证了所提出的分类方法。在日本的农业地区中获取的第一张图像包含七种作物类型，在水稻育种地区中获取的第二张图像由六种水稻组成。对于第一个图像，所提出的方法以97.84％的总准确度和0.96的kappa系数区分了不同的植被。对于第二张图像，所提出的方法可以准确地区分水稻品种，获得最高的总体准确度（98.65％）和kappa系数（0.98）。结果表明，该方法可以显着提高农作物分类的准确性并减少边缘效应，并且纹理特征与对叶绿素，类胡萝卜素和花色苷指示剂敏感的光谱指数相结合，对农作物的分类有显着贡献。因此，这是一种用于对作物种类进行分类，监控入侵种以及与精确农业相关的应用的有效方法。

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