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Proceedings Paper • Open Access • new

Comparing land-cover maps accuracies generated from multispectral classification of Landsat-8 OLI dan Pleiades images using two different classification schemes

Paper Abstract

Multispectral classification is one of the main methods in the analysis and processing of digital remotely sensed imagery, which until now is still widely used to generate land-cover/ land-use information. Technically, pixel-based classification methods rely on conventional approaches, as compared to GeoBIA, and it can be implemented using either supervised or unsupervised methods. The classification methods are supported by the rapid development of various image processing software, which provide a wide variety of algorithm options, so that the classification process can be carried out easily. Although relatively simple, an appropriate selection of multispectral classification algorithm may provide highly accurate land-cover maps. However, the highly accurate land-cover/land-use maps may also be influenced by image types and classification schemes that are used in the study. This study aimed to compare the results of the multispectral classification using maximum likelihood algorithm, for generating land-cover maps based on Landsat-8 OLI images (30 meters) and Pleiades imagery (2 meters). The classification referred to two different classification schemes relating to spectral and spatial dimensions. The results showed that the multispectral classification with spectral-related classification scheme applied to Pleiades imagery gave higher overall accuracy as compared to that of Landsat-8 OLI. It was also found that the highest overall accuracy achieved in this study was 81.7%, obtained using Pleiades imagery and referring to spectral dimension classification scheme. On the other hand, the lowest overall accuracy was obtained by the same imagery applied using spatial-related dimension. The relatively similar values of low overall accuracy for spatial-related dimension was also gained by Landsat-8 OLI imagery, proving that multispectral classification does not work well for spatial-related land cover classification scheme.

Paper Details

Date Published: 21 November 2019
PDF: 7 pages
Proc. SPIE 11311, Sixth Geoinformation Science Symposium, 113110U (21 November 2019); doi: 10.1117/12.2548888
Show Author Affiliations
Erisa Ayu W. A. Putri, Gadjah Mada Univ. (Indonesia)
Projo Danoedoro, Gadjah Mada Univ. (Indonesia)
Nur M. Farda, Gadjah Mada Univ. (Indonesia)


Published in SPIE Proceedings Vol. 11311:
Sixth Geoinformation Science Symposium
Sandy Budi Wibowo; Andi B. Rimba; Stuart Phinn; Ammar A. Aziz; Josaphat Tetuko Sri Sumantyo; Hasti Widyasamratri; Sanjiwana Arjasakusuma, Editor(s)

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