Hyperspectral Image Classification Using Unsupervised Algorithms

Sahar A.

doi:10.14569/ijacsa.2016.070425

Abstract

Hyperspectral Imaging (HSI) is a process that results in collected and processed information of the electromagnetic spectrum by a specific sensor device. It’s data provide a wealth of information. This data can be used to address a variety of problems in a number of applications. Hyperspectral Imaging classification assorts all pixels in a digital image into groups. In this paper, unsupervised hyperspectral image classification algorithms used to obtain a classified hyperspectral image. Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) algorithm and K-Means algorithm are used. Applying two algorithms on Washington DC hyperspectral image, USA, using ENVI tool. In this paper, the performance was evaluated on the base of the accuracy assessment of the process after applying Principle Component Analysis (PCA) and K-Means or ISODATA algorithm. It is found that, ISODATA algorithm is more accurate than K-Means algorithm. Since The overall accuracy of classification process using K-Means algorithm is 78.3398% and The overall accuracy of classification process using ISODATA algorithm is 81.7696%. Also the processing time increased when the number of iterations increased to get the classified image.

Highlights

Remote sensing is the art and science to obtain information about an object, area
The unsupervised classification was applied on a hyperspectral image using ENVI tool
The two steps that applied to the hyperspectral image are Principle Component Analysis (PCA) and K-Means or Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) algorithms

Summary

INTRODUCTION

Remote sensing is the art and science to obtain information about an object, area It is viewed as the measurement and analysis of electromagnetic radiation transmitted through, reflected from, or absorbed and dissipated by the ambiance, the hydrosphere and by material at or near the land surface, for the purpose of interpreting and managing the Earth's resources and surroundings. The targets can be differentiated by their spectral reflectance signatures in the remotely sensed images [1][2][3] Hyperspectral sensors such as the Airborne Imaging Spectro-radiometer for applications (AISA) enabled the construction of an effective, continuous reflectance spectrum for every pixel in the scene. These schemes can be applied to discriminate among earth surface features [1][2][4]. The size of the pixel where it is possible to be relatively large so that a pixel can contain a lot of properties and that is difficult to classify, or be very small in terms of not contains characteristics can be classified is one of the hyperspectral imaging limits [12]

CLASSIFICATION

RESULTS AND DISCUSSION

Cases Studies

Class Statistics

CONCLUSION

FUTURE WORK