International Journal of Engineering Research and Development e-ISSN: 2278-067X, p-ISSN: 2278-800X, www.ijerd.com Volume 4, Issue 3 (October 2012), PP. 35-39 35 Image Analysis and Processing of Remote Sensing Data with the help of NDVI values Jitender Kumar 1 , Deepak Chaudhary 2 , Vijendra Rai 3 1 M.Tech CSE Department, IET Alwar, Rajasthan, India 2 Asstt. Prof. CSE Department, IET Alwar, Rajasthan, India 3 Asstt. Prof. CSE Department, CERT Meerut, UP, India Abstract:- This paper focuses on the Image Analysis of Remote Sensing Data Integrating Spectral, and Spatial Features of Objects in the area of satellite image processing. Spatial distributions of land cover types such as roads; urban area, agriculture land, and water resources can easily be interpreted by using the NDVI values for different areas like greenery area, road, residential area, agricultural area etc in MATLAB. We compare these NVDI values with the values provided by the national remote sensing agency. The long-term objective of the thesis is to optimize the land use pattern for economically and environmentally sustainable urban development. Keywords:- Remote Sensing Material; Geometric Correction; Radiometric Correction; Area Index; Ray Tracing Simulations. I. INTRODUCTION Basic aim of our paper to analyze the Remote Sensing Data that we have received from the national remote sensing agency (space department, government of India); Integrating Spectral, Temporal and Spatial Features of the Objects in the area of satellite image processing. Here the multi-spectral remote sensing data is used to find the spectral signature of different objects of the Meerut city for the land cover classification, how the use of land changes according to time and also performed the temporal analysis to analyze the impact of climate over the surface. In this paper we used the NDVI technique. We calculate and compare the NDVI values for particular city using MAT Lab 7.1 version simulation tool. During the study following objectives were achieved: 1 General analysis of the different bands data of the multi spectral image. 2 Determination of NDVI values of different images from the ground survey data. 3 Creation of the False Color Composite image for the classified objects such as (vegetation, structures, roads, free land and water). 4 Compare the result values with the reference values for the confirmation of objects vegetation, structures, roads, free land and water. II. NDVI (NORMALIZED DIFFERENCE VEGETATION INDEX) NDVI pixel very rarely covers a single homogeneous agricultural region. Instead it may cover roads, buildings, bare soil, small water bodies, natural vegetation and agriculture, all within one pixel. An NDVI pixel is the sum of the radiation reflected from all the land cover types within the area covered by the pixel. NDVI is an indicator of the condition of the overall vegetation in an area, including natural vegetation and agriculture. In rain-fed agriculture, natural vegetation may follow similar patterns to the agriculture. More often however, agriculture is more susceptible to adverse conditions and follows different growth cycles. When looking at NDVI, always remember that you are looking at general conditions and not necessarily the condition of a specific crop. The formula for NDVI is given by: NIR= Near infrared radiation ARP=Active radiation of pixel conclusion. III. MOTIVATION Focus of our study is on Meerut city, which belongs to the Meerut district as shown in the map of Meerut district. Meerut is a large and prosperous city of Uttar Pradesh. It has a population of almost 1.2 million people. The city is historically important because it was the birthplace of the Indian revolution against British rule, but it is also becoming an economic center for the surrounding area.