Comparison of Pixel and Object Oriented Based Classification of Hyperspectral Pansharpened Images
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RESEARCH ARTICLE
Comparison of Pixel and Object Oriented Based Classification of Hyperspectral Pansharpened Images R. Zoleikani 1 & M. J. Valadan Zoej 1 & M. Mokhtarzadeh 1
Received: 11 September 2013 / Accepted: 19 February 2016 / Published online: 4 April 2016 # Indian Society of Remote Sensing 2016
Abstract In this paper pixel-based and object-oriented classifications were investigated for land-cover mapping in an urban area. Since the image fusion methods are playing a useful role in supplying classification different fusion approaches such as Gram-Schmidt Transform (GS), Principal Component Transform (PC), Haar wavelet, and À Trous Wavelet Transform (ATWT) algorithms have been used and the fused image with the best quality has been assessed on its respected classification. A Hyperion image and IRS-PAN image covering a region near Tehran, Iran have been used to demonstrate the enhancement and accuracy assessment of fused image over the initial images. The evaluation results of fused images showed that the Haar wavelet approach has good quality in preserving spectral information as well as spatial information. Classification results were compared to evaluate the effectiveness of the two classification approaches. Result of the pan-sharpened image classifications displayed that the object-oriented procedure presented more accurate outcomes (90.47 %) than those obtained by pixel-based classification method (77.33 %).
Keywords Image fusion techniques . Object-oriented image analysis . Pixel-based classification . Hyperspectral image
* R. Zoleikani [email protected]
1
Remote Sensing, Department of Remote Sensing Engineering, K. N. Toosi University of Technology, Tehran, Iran
Introduction The accuracy and reliability of urban image analysis from panchromatic satellite imagery could be enhanced using extra spectral bands of the high spectral image. A Pan image has high spatial resolution however weak spectral resolution. On the contrarily, a multi-band image has high spectral resolution but low spatial resolution. Since, a few numbers of spectral bands that defines multispectral sensors can be adequate to separate among various landcover classes; their discrimination ability is restricted when different types of the same varieties are to be identified. Hyperspectral sensors can be utilized to handle this issue. These sensors are known by a very high spectral resolution that usually results in many spectral bands. Hence, it is achievable to deal with different applications in need of high discrimination abilities in the spectral domain. It is worth noting that image fusion approach is able to compose a high spatial resolution panchromatic image and a high spectral resolution hyperspectral image into a new image with high spatial/ spectral resolution. Many researches in the particular field of image fusion goal expanding new algorithms for visual improvement or higher quantitative evaluation value while not much research on the effect of fusion on effective applications, i.e. image classification (Li and Li 201
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