JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Classification of Radish and Chinese Cabbage in Autumn Using Hyperspectral Image
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Classification of Radish and Chinese Cabbage in Autumn Using Hyperspectral Image
Park, Jin Ki; Park, Jong Hwa;
  PDF(new window)
 Abstract
The objective of this study was to classify between radish and Chinese cabbage in autumn using hyperspectral images. The hyperspectral images were acquired by Compact Airborne Spectrographic Imager (CASI) with 1m spatial resolution and 48 bands covering the visible and near infrared portions of the solar spectrum from 370 to 1044 nm with a bandwidth of 14 nm. An object-based technique is used for classification of radish and Chinese cabbage. It was found that the optimum parameter values for image segmentation were scale 400, shape 0.1, color 0.9, compactness 0.5 and smoothness 0.5. As a result, the overall accuracy of classification was 90.7 % and the kappa coefficient was 0.71. The hyperspectral images can be used to classify other crops with higher accuracy than radish and Chines cabbage because of their similar characteristic and growth time.
 Keywords
hyperspectral image;crop classification;object-based;radish;chines cabbage;
 Language
Korean
 Cited by
 References
1.
Aboelghar, M., S. Arafat, and E. Farag, 2013. Hyper spectral measurements as a method for potato crop characterization. International Journal of Advanced Remote Sensing and GIS 2(1): 122-129.

2.
Baatz, M., and A. Schape, 2000. Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation, Angewandte Geographische Informationsverarbeitung XII, 12-23. Heidellberg: Wichmann-Verlag.

3.
Bajwa, S. G., P. Bajcsy, P. Groves, and L. F. Tian, 2004. Hyperspectral image data mining for band selection in agricultural applications. Transactions-American Society of Agricultural Engineers 47(3): 895-908. crossref(new window)

4.
Boschetti, L., S. P. Flasse, and P. A. Brivio, 2004. Analysis of conflict between omission and commision in low spatial resolution dichotomic thematic products: the Pareto boundary. Remote Sensing of Environment 91(3-4): 280-292. crossref(new window)

5.
Dopido, I., A. Villa, A. Plaza, and P. Gamba, 2012. A quantitative and comparative assessment of unmixing-based feature extraction techniques for hyperspectral image classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5(2): 421-435. crossref(new window)

6.
Guo, B., S. R. Gunn, R. I. Damper, and J. D. B. Nelson, 2006. Band selection for hyperspectral image classification using mutual information. Geoscience and Remote Sensing Letters, IEEE 3(4): 522-526. crossref(new window)

7.
Huang, H. Y., and B. C. Kuo, 2010. Double nearest proportion feature extraction for hyperspectral-image classification. Geoscience and Remote Sensing, IEEE Transactions on 48(11): 4034-4046.

8.
Jensen, J. R., 2009. Remote sensing of the environment: an Earth resource perspective, 2nd edition, Pearson Education India.

9.
Kim, H. O., and J. M. Yeom, 2012. A study on object based image analysis methods for land cover classification in agricultural areas, Journal of the korean association of geographic information studies 15 (4): 26-41 (in Korean). crossref(new window)

10.
Kim, S. H., K. S. Lee, J. R. Ma, and M. J. Kook, 2005. Current status of hyperspectral remote sensing: principle data processing techniques, and applications. Korean Journal of Remote Sensing 21(4): 341-369.

11.
Kim, T. W., 2014. An optimized band selection of airborne hyperspectral imagery for vegetation indices. Ph.D. Diss., Pukyong National University.

12.
Kruse, F. A., S. L. Perry and A. Caballero, 2002. Integrated multispectral and hyperspectral mineral mapping, Los Menucos, Rio Negro, Argentina, Part II. EO-1 Hyperion/AVIRIS comparisons and Landsat TM/ASTER extensions. Proceedings of the 11th JPL Airborne Geoscience Workshop.

13.
Lee, M. S., S. J. Kim, H. S. Shin, J. K. Park, and J. H. Park, 2009. Extraction of agricultural land use and crop growth information using KOMPSAT-3 resolution satellite image. Korean Journal of Remote Sensing 25(5): 411-421.

14.
Lee, S. H., Y. H. Oh, N. Y. Park, S. H. Lee, and J. Y. Choi, 2014. Extraction of paddy field in Jaeryeong, North Korea by objectoriented classification with RapidEye NDVI imagery, Journal of the Korean society of agricultural engineers 56(3): 55-64 (in Korean).

15.
Park J. K., and J. H. Park, 2015. Crops classification using imagery of unmanned aerial vehicle (UAV), Journal of the Korean society of agricultural engineers 57(6): 91-97 (in Korean). crossref(new window)

16.
Underwood, E., S. Ustin, and D. DiPietro, 2003. Mapping nonnative plants using hyperspectral imagery. Remote Sensing of Environment 86(2): 150-161. crossref(new window)