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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.