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Comparison between several feature extraction/classification methods for mapping complicated agricultural land use patches using airborne hyperspectral data

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Airborne hyperspectral remote sensing was applied to agricultural land in the Miura Peninsula, near the metropolis of Tokyo in Japan. The study area is characterized by complicated land use patches, which is the general characteristic of most agricultural lands in Japan. Several feature extraction/classification methods were examined in classifying the land use and plant species. The results showed that decision boundary feature extraction (DBFE) was better than principal component analysis (PCA) as the feature extraction method. Moreover, the pre-classification process using NDVI that separates the whole study area into vegetated area and non-vegetated areas also improved the classification accuracy. After the pre-procedures, the land use and plant species were finally mapped by maximum likelihood classification (MLC) or extraction and classification of homogeneous objects (ECHO). The best kappa (overall accuracy) of classification was 0.914 (92.4%) and 0.924 (93.3%) for MLC and ECHO, respectively. The best accuracies of each category for the image were 79.5% to 100% for plant species (watermelon, pumpkin, marigold, grass and tree), 88.7% to 100% for soil types, 97.8% for concrete, and 99.4% for vinyl-mulches. Although, built-up area has low estimation accuracy, this did not affect the overall classification accuracy because it covers only a very small area.

Document Type: Research Article


Affiliations: Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan

Publication date: January 1, 2007

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