1 research outputs found
Performance of Kriging Based Soft Classification on WiFS/IRS- 1D image using Ground Hyperspectral Signatures
Hard and soft classification techniques are the conventional ways of image
classification on satellite data. These classifiers have number of drawbacks.
Firstly, these approaches are inappropriate for mixed pixels. Secondly, these
approaches do not consider spatial variability. Kriging based soft classifier
(KBSC) is a non-parametric 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
(WiFS) from the Indian Remote Sensing Satellite -1D (IRS-1D). The ground
hyperspectral signatures acquired from the agricultural fields by a hand held
spectroradiometer are used to detect subpixel targets from the satellite
images. Two measures of closeness have been used for 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.Comment: 5 pages,3 figures 3 table