- Volume 24 Issue 1
DOI QR Code
Accuracy Assessment of Supervised Classification using Training Samples Acquired by a Field Spectroradiometer: A Case Study for Kumnam-myun, Sejong City
지상 분광반사자료를 훈련샘플로 이용한 감독분류의 정확도 평가: 세종시 금남면을 사례로
- Received : 2016.03.15
- Accepted : 2016.03.28
- Published : 2016.03.31
Many studies are focused on image data and classifier for comparison or improvement of classification accuracy. Therefore studies are needed aspect of the training samples on supervised classification which depend on reference data or skill of analyst. This study tries to assess usability of field spectra as training samples on supervised classification. Classification accuracies of hyperspectral and multispectral images were assessed using training samples from image itself and field spectra, respectively. The results shown about 90% accuracy with training sample collected from image. Using field spectra as training sample, accuracy was decreased 10%p for hyperspectral image, and 20%p for multispectral image. Especially, some classes shown very low accuracies due to similar spectral characteristics on multispectral image. Therefore, field spectra might be used as training samples on classification of hyperspectral image, although it has limitation for multispectral image.
Supervised Classification;Accuracy;Training Sample;Field Spectra
- Burai, P., Deak, B., Valko, O. and Tomor, T., 2015, Classification of herbaceous vegetation using airborne hyperspectral imagery, Remote Sensing, Vol. 7, No. 2, pp. 2046-2066. https://doi.org/10.3390/rs70202046
- Byun, Y. G., Eo, Y. D. and Yu, K. Y., 2007, Classification of hyperspectral image using spectral mutual information, Journal of the Korean Society for Geospatial Information System, Vol. 15, No. 3, pp. 33-39.
- Cerra, D., Mueller, R. and Reinartz, P., 2012, A classification algorithm for hyperspectral data based on synergetics theory, Proc. of ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, International Society for Photogrammetry and Remote Sensing, Melbourne, Australia, Vol. I-7, pp. 71-76.
- Chang, C. I., 2003, Hyperspectral imaging: Techniques for spectral detection and classification, Kluwer Academic/Plenum Publishers, New York, pp. 2-35.
- Cho, H. G., Kim, D. W. and Shin, J. I., 2014, Study of comparison of classification accuracy of airborne hyperspectral image land cover classification though resolution change, Journal of the Korean Society for Geospatial Information System, Vol. 22, No. 3, pp. 155-160. https://doi.org/10.7319/kogsis.2014.22.3.155
- Cho, H. G. and Lee, K. S., 2014, Comparison between hyperspectral and multispectral images for the classification of coniferous species, Korean Journal of Remote Sensing, Vol. 30, No. 1, pp. 25-36. https://doi.org/10.7780/kjrs.2014.30.1.3
- Choi, B. G., Na, Y. W., Kim, S. H. and Lee, J. I., 2014, A study on the improvement classification accuracy of land cover using the aerial hyperspectral image with PCA, Journal of the Korean Society for Geospatial Information System, Vol. 22, No. 1, pp. 81-88.
- Dalponte, M., Bruzzone, L., Vescovo, L. and Gianelle, D., 2009, The role of spectral resolution and classifier complexity in the analysis of hyperspectral images of forest areas, Remote Sensing of Environment, Vol. 113, No. 11, pp. 2345-2355. https://doi.org/10.1016/j.rse.2009.06.013
- Han, D. Y., Kim, H. J., Kim, D. S., Cho, Y. W. and Kim, Y. I., 2003, Feature selection for image classification of hyperion data, Korean Journal of Remote Sensing, Vol. 19, No. 2, pp. 171-179.
- Heiden, U., Roessner, S. S. and Kaufmann, H., 2007, Determination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data, Remote Sensing of Environment, Vol. 111, No. 4, pp. 537-552. https://doi.org/10.1016/j.rse.2007.04.008
- Herold, M., Margaret, E. and Roberts, D. A., 2003, Spectral resolution requirements for mapping urban areas, IEEE Transactions on Geoscience and Remote Sensing, Vol. 41, No. 9, pp. 1907-1919. https://doi.org/10.1109/TGRS.2003.815238
- Hochberg, E. J. and Atkinson, M. J., 2003, Capabilities of remote sensors to classify coral, algae and sand as pure and mixed spectra, Remote Sensing of Environment, Vol. 85, No. 2, pp. 174-189. https://doi.org/10.1016/S0034-4257(02)00202-X
- Jensen, J. R., 2005, Introductory digital image processing: A remote sensing perspective, 3rd Edition, Upper Saddle River, NJ: Pearson Prentice Hall, pp. 431-465.
- Jia, X. and Richards, J. A., 1993, Binary coding of imaging spectrometer data for fast spectral matching and classification, Remote Sensing of Environment, Vol. 43, No. 1, pp. 47-53. https://doi.org/10.1016/0034-4257(93)90063-4
- Lu, Q., Huang, X. and Zhang, L., 2014, A Novel clustering-based feature representation for the classification of hyperspectral imagery, Remote Sensing, Vol. 6, No. 6, pp. 5732-5753. https://doi.org/10.3390/rs6065732
- Manolakis, D., Marden, D. and Shaw, G. A., 2003, Hyperspectral image processing for automatic target detection applications, Lincoln Laboratory Journal, Vol. 14, No. 1, pp. 79-116.
- Mockel, T., Dalmayne, J., Prentice, H. C., Eklundh, L., Purschke, O., Schmidtlein, S. and Hall, K., 2014, Classification of grassland successional stages using airborne hyperspectral imagery, Remote Sensing, Vol. 6, No. 8, pp. 7732-7761. https://doi.org/10.3390/rs6087732
- Nasarudin, N. E. M. and Shafri, H. Z. M., 2011, Development and utilization of urban spectral library for remote sensing of urban environment, Journal of Urban and Environmental Engineering, Vol. 5, No. 1, pp. 44-56. https://doi.org/10.4090/juee.2011.v5n1.044056
- Richards, J. A. and Jia, X., 2006, Remote sensing digital image analysis: An introduction, Fourth Edition, Springer, Germany, pp. 296-297.
- Shin, J. I., 2012, Comparative analysis and improvement of target detection algorithms in hyperspectral image, Doctoral thesis, Inha University, pp. 7-13.
- Stavrakoudis, D. G., Dragozi, E., Gitas, I. Z. and Karydas, C. G., 2014, Decision fusion based on hyperspectral and multispectral satellite imagery for accurate forest species mapping, Remote Sensing, Vol. 6, No. 8, pp. 6897-6928. https://doi.org/10.3390/rs6086897
- Tamir, C. and Arnon, K., 2015, Ground-level classification of a coral reef using a hyperspectral camera, Remote Sensing, Vol. 7, No. 6, pp. 7521-7544. https://doi.org/10.3390/rs70607521
- Van der Meer, F. D. and De Jong, S. M., 2003, Imaging spectrometry: Basic principles and prospective applications, Kluwer Academic Publishers, Netherlands, pp. 44-61.
- Zain, R. M., Ismail, M. H. and Zaki, P. H., 2013, Classifying forest species using hyperspectral data in balah forest reserve, Kelantan, Peninsular Malaysia, Journal of Forest Science, Vol. 29, No. 2, pp. 131-137.
Supported by : 국토교통부