OBJECT-BASED CLASSIFICATION VS . PIXEL-BASED CLASSIFICATION : COMPARITIVE IMPORTANCE OF MULTI-RESOLUTION IMAGERY

Land Use/Land Cover (LULC) classifications have proven to be valuable assets for resource managers interested in landscape characteristics and the changes that occur over time. This study made a comparison of an object-based classification with supervised and unsupervised pixel-based classification. Two multi-temporal (leaf-on and leaf-off), medium-spatial resolution SPOT-5 satellite images and a high-spatial resolution color infrared digital orthophoto were used in the analysis. Combinations of these three images were merged to evaluate the relative importance of multi-temporal and multi-spatial imagery to classification accuracy. The objectbased classification using all three-image datasets produced the highest overall accuracy (82.0%), while the object-based classification using the high-spatial resolution image merged with the SPOT-5 leaf-off image had the second highest overall accuracy (78.2%). While not significantly different from each other, these two object-based classifications were statistically significantly different from the other classifications. The presence of the high-spatial resolution imagery had a greater impact on improving overall accuracy than the multi-temporal dataset, especially with the object-based classifications.

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