Performance of Kriging Based Soft Classification on WiFS/IRS- 1D image using Ground Hyperspectral Signatures

TL;DR

This paper evaluates a Kriging-based soft classification method for satellite imagery, demonstrating its superior accuracy over traditional methods by leveraging spatial variability and ground hyperspectral data.

Contribution

It introduces and compares a Kriging-based soft classifier with conventional techniques, showing improved accuracy in satellite image classification.

Findings

KBSC outperforms conventional classifiers statistically

Utilizes ground hyperspectral signatures for subpixel detection

Exploits spatial variability for enhanced classification accuracy

Abstract

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.