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MULTITEMPORAL CLASSIFICATION OF AGRICULTURAL CROPS USING THE SPECTRAL-TEMPORAL RESPONSE SURFACE

机译:使用光谱 - 颞响应面的农业作物多型分类

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摘要

A method for classifying agricultural crops using multitemporal, multi-spectral and multi-source remotely sensed data is described. The procedure characterizes all the pixels in a scene by considering their intensity values as a function of time of imaging and spectral waveband. An analytical surface is interpolated through these data points, which may be irregularly spaced. For purposes of comparison, two interpolation methods were used to generate and parameterize the analytical surfaces. The surface coefficients were then input to different supervised classifiers (Maximum Likelihood and Artificial Neural Network algorithms). Results show that classification accuracy is significantly improved in comparison with the use of any single-date image. Classification accuracies in excess of 87% were achieved. The advantages of the methodology described in this paper are that it takes account of the reflectance spectra at different points in the growing season, and that neither the time periods between images nor the wavebands used need be the same at each date. Thus, the procedure can handle data from sensors such as SPOT HRV and Landsat TM. In addition, the use of coefficients to represent the analytical surfaces significantly reduces the amount of data processing, whilst maintaining information reliability.
机译:描述了一种使用多模型,多光谱和多源远程感测数据进行分类农作物的方法。通过将它们的强度值视为成像和频谱波段的时间的函数,该过程表征了场景中的所有像素。通过这些数据点内插的分析表面,其可以是不规则间隔的。出于比较的目的,使用两个内插方法来产生和参数化分析表面。然后将表面系数输入到不同的监督分类器(最大可能性和人工神经网络算法)。结果表明,与使用任何单日图像的使用相比,分类准确性明显改善。达到超过87%的分类准确性。本文描述的方法的优点在于,它考虑了生长季节中不同点的反射光谱,并且在每个日期时,图像之间的时间段也不需要相同。因此,该过程可以处理来自传感器的数据,例如点HRV和Landsat TM。另外,使用系数来表示分析表面显着降低了数据处理的量,同时保持信息可靠性。

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