Accuracy Assessment of Supervised and Unsupervised Classification using NOAA Data in Andhra Pradesh Region

Abstract

The objective of this study is to differentiate NOAA satellite data using NDVI thresholds. Normalized Different Vegetation Index image (NDVI), initially derived from visible and near infrared bands of NOAA satellite. The different areas like vegetation, non-vegetation and water bodies are keenly observed and the thresholds for classifying them are formulated carefully with the help of ground truth information of the study area. The separation of images into different land covers is performed using density slicing. The classification process is completed by color mapping and class labelling. Confusion matrix is used to determine the accuracy of classified image by calculating overall classification accuracy and Kappa coefficient. NDVI based classification is one of best method to classify the NOAA satellite data with a high accuracy. Keyword-AccuracyAssessment, Normalised different vegetation index (NDVI), Near infrared(NIR), Threshold, Vegetation, Kappa coefficeint. Ⅰ. INTRODUCTION Remote sensing data have a wide range of applications, among them land cover mapping has its own significance. The physical condition of the ground surface can be detected using land covers. In the study of land cover dynamics, remote sensing is majorly done by satellites. From last few decades, due to the advancement in technology in remote sensing, there is enhancement in obtaining a large geographical data. The accurate and timely information about location and spatial configuration as well as growth rates of land covers is provided by sensors. Image classification is process used for land cover mapping from remote sensing data. The major breakthrough in satellite remote sensing is better spatial and spectral resolution and it's been possible by employing advanced sensors. For mapping and monitoring forest/vegetation, both visual and digital analysis techniques have been used. Interpreting various land use classes such as forest, vegetation is done by adapting proper methodology. The analysis of remotely sensed imagery through interpretation is distinguished by following three factors. They are 1. Panoramic overview of remotely sensed imagery. 2. They fall in the region of visible and infrared region of the electromagnetic spectrum and 3. portraying the Earth's surface at different scales and resolutions. The necessary spectral and spatial features of the various objects can be obtained through multispectral remote sensing. Technique used for classification of objects is spectral analysis of the radiant energy reflected or emitted by the target. In this paper, the Normalized Difference Vegetation Index (NDVI) values and classifying different land cover types over the selected study area is done by multispectral images. The difference between the sensor spectral radiance of the red band (band4) and the near-infrared band (band5) of satellite image, gives NVDI. Generally, NVDI values are positive for soil and vegetation and theoretically the values of the NDVI vary between-1.0 and +1.0. This paper is divided into 5 sections, section II describes the detailed of Study Area and Data, Methodology used is given in section III. Section IV deals with the obtained experimental results for the proposing approach. Section V contains concluding remarks.