Classification of Landsat-8 Imagery Based on Pca and Ndvi Methods
M Venkata Dasu1, Dr P V N Reddy2, Dr S Chandra Mohan Reddy3
1M Venkata Dasu, Research Scholar, Department of Engineering and Communication Engineering, JNTUA, Anantapuramu, Andhra Pradesh.
2Dr P V N Reddy, Principal, Sri Venkateswara College of Engineering, Balaji Nagar, Kadapa, Andhra Pradesh – 516003.
3Dr S Chandra Mohan Reddy, Associate Professor, Dept of ECE JNTUA, Anantapuramu, Andhra Pradesh. 

Manuscript received on 12 August 2019 | Revised Manuscript received on 20 August 2019 | Manuscript published on 30 August 2019 | PP: 4321-4325 | Volume-8 Issue-10, August 2019 | Retrieval Number: J98430881019/2019©BEIESP | DOI: 10.35940/ijitee.J9843.0881019
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: Remote sensing is an important issue in satellite image classification. In developing a significant sustainable system in agriculture farming, the major concern for remote sensing applications is the crop classification mechanism. The other important application in remote sensing is urban classification which gives the information about houses, roads, buildings, vegetation etc. A superior indicator for the presence of vegetation can be computed from the vegetation indices of a satellite image. This indicator supports in describing the health of vegetation through the image attributes like greenness and density. The other parameter in detecting objects or region of interest is an image is the texture. A satellite image contains spectral information and can be represented by more spectral bands and classification is very tough task. Generally, Classification of individual pixels in satellite images is based on the spectral information. In this research paper Principle component analysis and combination of PCA and NDVI classification methods are applied on Landsat-8 images. These images are acquired from USGS. The performance of these methods is compared in statistical parameters such as Kappa coefficient, overall accuracy, user’s accuracy, precision accuracy and F1 accuracy. In this work existing method is PCA and proposed method is PCA+NDVI. Experimental results shows that the proposed method has better statistical values compared to existing method.
Keywords: Classification, Kappa Coefficient, Multispectral Images, NDVI, PCA.

Scope of the Article: Classification