Earticle

초록

영어

Obtaining thematic maps using image classification techniques from hyperspectral datasets is a very difficult image processing task. In hyperspectral image analysis dimensionality reduction is one of the challenging pre-processing tasks, which is achieved using feature extraction techniques. The beauty of these techniques is that they drastically reduces the dimensionality of image and at the same time preserves the majority of the essential information. In this paper few most frequently used dimensionality reduction methods are being investigated, which helps to get accurateness. This research work presents a relative performance investigation of few mostly frequently used feature extraction techniques like Decision Boundary Feature Extraction (DBFE), Non-Parametric Weighted Feature Extraction (NWFE), Discriminative analysis feature extraction (DAFE) and Principal Component Analysis (PCA). The classification is carried out using two most widely used classification techniques including Gaussian Maximum Likelihood (GML) and neural network (NNs). The results obtained after performing experiments indicates that Decision Boundary Feature Extraction (DBFE) technique has provided the best accuracy among various investigated feature extraction techniques. The application areas of this research include areas like identification of exact location in battle field, drought affected areas, flooded areas and weather forecasting etc.

목차

Abstract
 1. Introduction
 2. Feature Extraction Techniques
 3. Classification Methods
 4. Experimental Results
 5. Conclusion
 References

키워드

저자

• Diwaker [ Department of CSE, Uttarakhand Technical University, Dehradun ]
• Manoj Kumar Chaudhary [ Department of CSE, Uttarakhand Technical University, Dehradun ]
• Praveen Tripathi [ Department of Computer Sc, SGRRITS Dehradun ]
• Ashutosh Bhatt [ Department of Computer Sc, Birla Institute of applied Sciences, Bhimtal, Nainital ]
• Abhay Saxena [ Department of Computer Sc, Dev Sanskriti University, Haridwar ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605