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Abstract

Land Use/Land Cover (LULC) classification data have proven to be valuable assets for various governmental agencies, park managers, and natural resource managers. Traditional pixel-based classification methods have difficulty with high resolution imagery, resulting in a “salt and pepper” appearance. Newer object-based methods may prove to be more accurate. This study compared an object based classification procedure utilizing Feature Analyst© software with a traditional pixel-based methodology (supervised classification) when applied to medium-spatial resolution satellite imagery merged with high-spatial resolution aerial imagery. This study utilized two multi-spectral SPOT-5 satellite images, leaf-on and leaf-off, merged with a color infrared aerial image. Because of correlation between some of the bands of the merged image, Principal Component Analysis (PCA) was used to reduce redundancy in the data. Field data was collected in the study area to serve as a reference for the accuracy assessment. A training set was produced by selecting and identifying specific LULC class-types using 1-foot high-spatial resolution aerial imagery. This training set was used by both of the classification methods (supervised and object-based) to identify the various cover types within the study area. An accuracy assessment was performed on each image utilizing error matrices, the Kappa coefficient, and a two-tailed Z-test. Results indicate that the overall accuracy of the object-based classification was 82.0%, while the pixel-based classification was 66.9%. A Kappa analysis and a two tailed Z test were calculated. These values indicated a significant difference in the overall accuracies of the classifications.

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