Comparing Land-cover Maps Accuracies Generated from Multispectral Classification of Landsat-8 OLI dan Pleiades Images using Two Different Classification Schemes

摘要

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 classificationmethods rely on conventional approaches, as compared to GeoBIA, and it can be implemented using either supervised orunsupervised methods. The classification methods are supported by the rapid development of various image processingsoftware, 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 accurateland-cover maps. However, the highly accurate land-cover/land-use maps may also be influenced by image types andclassification schemes that are used in the study. This study aimed to compare the results of the multispectral classificationusing maximum likelihood algorithm, for generating land-cover maps based on Landsat-8 OLI images (30 meters) andPleiades imagery (2 meters). The classification referred to two different classification schemes relating to spectral andspatial dimensions. The results showed that the multispectral classification with spectral-related classification schemeapplied to Pleiades imagery gave higher overall accuracy as compared to that of Landsat-8 OLI. It was also found that thehighest overall accuracy achieved in this study was 81.7%, obtained using Pleiades imagery and referring to spectraldimension classification scheme. On the other hand, the lowest overall accuracy was obtained by the same imagery appliedusing spatial-related dimension. The relatively similar values of low overall accuracy for spatial-related dimension wasalso gained by Landsat-8 OLI imagery, proving that multispectral classification does not work well for spatial-related landcover classification scheme.