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首页> 外文期刊>Geoscience and Remote Sensing Letters, IEEE >Performance of Kriging-Based Soft Classification on WiFS/IRS-1D Image Using Ground Hyperspectral Signatures
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Performance of Kriging-Based Soft Classification on WiFS/IRS-1D Image Using Ground Hyperspectral Signatures

机译：使用地面高光谱特征对WiFS / IRS-1D图像进行基于Kriging的软分类的性能

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摘要

Hard/soft classification techniques are the conventional ways of image classification on satellite data. These classifiers have a number of drawbacks. First, these approaches are inappropriate for mixed pixels. Second, these approaches do not consider spatial variability. Kriging-based soft classification (KBSC) is a nonparametric geostatistical method. It exploits the spatial variability of the classes within the image. This letter compares the performance of KBSC with other conventional hard/soft classification techniques. The satellite data used in this study is the Wide Field Sensor from the Indian Remote Sensing Satellite-1D (IRS-1D). The ground hyperspectral signatures acquired from the agricultural fields by a handheld spectroradiometer are used to detect subpixel targets from the satellite images. Two measures of closeness have been used for the accuracy assessment of the KBSC to that of the conventional classifications. The results prove that the KBSC is statistically more accurate than the other conventional techniques.
机译：硬/软分类技术是对卫星数据进行图像分类的常规方法。这些分类器具有许多缺点。首先，这些方法不适用于混合像素。其次，这些方法没有考虑空间可变性。基于克里格的软分类（KBSC）是一种非参数地统计方法。它利用图像中类的空间变异性。这封信将KBSC的性能与其他常规的硬/软分类技术进行了比较。这项研究中使用的卫星数据是来自印度遥感卫星1D（IRS-1D）的广域传感器。通过手持式光谱辐射仪从农田获得的地面高光谱特征用于检测卫星图像中的亚像素目标。与传统分类相比，KBSC的准确性评估使用了两种接近度度量。结果证明，KBSC在统计上比其他常规技术更准确。

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