Classification of Hyperspectral Remote Sensing for Production Minerals Mapping Using Geological Map and Geomatics Techniques
Keywords:Band ratio, Classification, De-correlation, Hyperspectral Stacking.
The classification of hyperspectral images is an interesting job since the data dimension is huge for conventional classification procedures; normally several hundreds of spectral bands are attained for each image. These spectral bands can supported very rich spectral data of each pixel to find objects material .The objective of this research is to classify hyperspectral images for detection and production of detailed minerals mapping using geological map and Environment for Visualizing Images (ENVI) software. In this research, ASTER data and geological map have been used. Some techniques on these data are used such as enhancement, matching (linking), De-correlation, Band Ratio, stacking image and classification. The results showed that comparison of the two classification results showed the classification of stack image with the aspect and the slope provide more information than classification of ASTER image alone. Also, using ENVI software to generate 3D surface views.It concluded that capability of hyperspectral and its differentiation with multispectral data to extract detailed features from ASTER image.
 Lattin, J. M., Douglas, J., & Green, P. E. 2003. Analyzing multivariate data. china:Machine Press, pp. 38â€“40.
 Hardin, P, 2013, Hyperspectral Remote Sensing of Urban Areas.GeographyCompass,7:7â€“21.doi:10.1111/gec3.12017.
 Fang, X., Zhu, X., Wang, Z., Zhao, G., Jiang, Y., & Wang, Y. A. 2014. Hyperspectral Characteristics of Apple Leaves Based on Different Disease Stress. Remote Sensing Science.
 Graham, R.L., 1972. An efficient algorithm for determining the convex hull of a finite planar set," Information Processing Letters, 1, 132â€“133.
 Kennedy RE, Townsend PA, Gross JE, Cohen WB, Bolstad P, et al. 2009.Remote sensing change detection tools for natural resource managers: Understanding concepts and tradeoffs in the design of landscape monitoring. Projects. Remote sensing of environment, 113: 1382â€“1396.
 Delwiche, S.R., Kim, M.S., 2000. Hyperspectral imaging for detection of scab in wheat. In:Proceedings of SPIE 4203, Biological Quality and Precision Agriculture II, 13â€“20.
 Delwiche, S.R., 2003. Classification of scab and other mold-damaged wheat kernels by nearinfrared reflectance spectroscopy. Transactions of the ASAE, 46(3), 731â€“738.
 Bock, C.H., Poole, G.H., Parker, P.E., Gottwald, T.R., 2010. Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. Critical Reviews in Plant Sciences, 29(2), 59â€“107.
 Michael, A., Bhaskar, R. ASTER Users Handbook.Version 2. 2015.
 USGS, Japan ASTER Program, 2003, ASTER scene AST_L1B_003_06262000100635, 1B, USGS, Sioux Falls, 6/26/2001.
 Nutter, F.W., Gleason, M.L., Jenco, J.H., Christians, N.C., 1993. Assessing the accuracy, intrarater repeatability, and inter-rater reliability of disease assessment systems. Phytopathology, 83, 806â€“812.
 Naidu, R.A, Perry E.M, Pierce FJ, Mekuria, T., 2009. The potential of spectral reflectance technique for the detection of Grapevine leafroll-associated virus-3in two red-berried wine grape cultivars. Computers and Electronics in Agriculture,p: 38â€“45.
 Mahlein A.-K. Rumpf T., Welke, P., Dehne H.-W., Plumer L.,Steiner U., Oreke E.-C. 2013. Development of spectral indices for detecting and identifying plant diseases, Remote Sensing of Environment, 128 (21) pp. 21â€“30.
 Lu D, Mausel P, Brondizio E, Moran E, 2004. Change detection techniques.International journal of remote sensing, 25: 2365â€“2401.
 Lillesand, T.M. and Kiefer, R.W., 1994.Remote Sensing and Image Interpretation, 3rd Ed., John Wiley and Sons, Inc.: Toronto.
View Full Article:
How to Cite
LicenseAuthors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under aÂ Creative Commons Attribution Licensethat allows others to share the work with an acknowledgement of the work''s authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal''s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (SeeÂ The Effect of Open Access).