e-journal

Effects of atmospheric correction and pansharpening on LULC classification accuracy using WorldView-2 imagery

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Changes of Land Use and Land Cover (LULC) affect atmospheric, climatic, and biological spheres of the earth. Accurate LULC map offers detail information for resources management and intergovernmental cooperation to debate global warming and biodiversity reduction. This paper examined effects of pansharpening and atmospheric correction on LULC classification. Object-Based Support Vector Machine (OB-SVM) and Pixel-Based Maximum Likelihood Classifier (PB-MLC) were applied for LULC classification. Results showed that atmospheric correction is not necessary for LULC classification if it is conducted in the original multispectral image. Nevertheless, pansharpening plays much
more important roles on the classification accuracy than the atmospheric correction. It can help to increase classification accuracy by 12% on average compared to the ones without pansharpening. PB-MLC and OB-SVM achieved similar classification rate. This study indicated that the LULC classification accuracy using PB-MLC and OB-SVM is
82% and 89% respectively. A combination of atmospheric correction, pansharpening,and OB-SVM could offer promising LULC maps from WorldView-2 multispectral and panchromatic images.

Keywords:
LULC Remote sensing Object-based image analysis Pixel-based image analysis Maximum likelihood classifier (MLC)
Support vector machine (SVM)

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Informasi Detail

Judul Seri

INFORMATION PROCESSING IN AGRICULTURE

No. Panggil

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Penerbit

: China Agricultural University., 2015

Deskripsi Fisik

INFORMATION PROCESSING IN AGRICULTURE 2 (2015) 25–36

Bahasa

English

ISBN/ISSN

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Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

Food And Health

Info Detail Spesifik

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Pernyataan Tanggungjawab

Wati/Agus

Versi lain/terkait

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