Identification and Spectral Evaluation of Agricultural Crops on Hyperspectral Airborne Data
Main Article Content
Abstract
Hyperspectral remote sensing combined with advanced image processing techniques is an efficient tool for the identification of agricultural crops. In our study we pursued spectral analysis on a relatively small sample area using low number of training points to examine the potential of high resolution imagery. Spectral separability measurements were applied to reveal spectral overlapping between 4 crop species and for the discrimination we also used statistical comparisons such as plotting the PC values and calculating standard deviation of single band reflectance values on our classes. These statistical results were proven to be good indicators of spectral similarity and potential confusion of data samples. The classification of Spectral Angle Mapper (SAM) had an overall accuracy of 72% for the four species where the poorest results were obtained from the test points of garlic and sugar beet. Comparing the statistical analyses we concluded that spectral homogeneity does not necessarily have influence on the accuracy of mapping, whereas separability scores strongly correlate with classification results, implying also that preliminary statistical assessments can improve the efficiency of training site selection and provide useful information to specify some technical requirements of airborne hyperspectral surveys.
Downloads
Article Details
x
References
Burai, P. 2006. Földhasználat-elemzés és növény-monitoring különböző adattartalmú és térbeli felbontású távérzékelt felvételek alapján (Land use analysis and vegetation monitoring using remote sensed images of different data content and spatial resolution). Agrártudományi Közlemények 2006/22 Különszám, 7–12. (In Hungarian)10.34101/actaagrar/22/3178
Burai, P., Tomor, T. 2011. Hiperspektrális felmérés eredményei az Ipoly Balassagyarmat és Ipolytarnóc közötti szakaszán (The results of the hyperspectral survey along river Ipoly between Balassagyarmat and Ipolytarnóc. In: A Bükki Nemzeti Park Igazgatóság természeti értékeinek kutatása I.: „Az Ipoly–vízgyűjtő vizes élőhelyeinek komplex felmérése, közösségi jegyzékeinek kidolgozása” Felsőtárkány, 2011 (Conference book, in Hungarian). Online at: https://bnpi.hu/file/45/.
Burai, P., Lövei, G. Zs., Lénárt, Cs., Nagy, I., Enyedi, P. 2010. Mapping aquatic vegetation of the Rakamaz-Tiszanagyfalui Nagy-morotva using hyperspectral imagery. Acta Geographica Debrecina Landscape & Environment, 4 (1), 1–10.
Burai, P., Deák, B., Orolya, V., Lénárt, Cs. 2014. Mapping of Grass Species Using Airborne Hyperspectral Data. In: Pfeifer, N., Zlinszky, A. (eds) Proceedings of the International Workshop on Remote Sensing and GIS for Monitoring of Habitat Quality, Vienna, 87–88.
Büttner, Gy., Csillag, F., Mather, P. M. 1988. Spectral And Spatial Information Content Of Spot Data. In: Geoscience and Remote Sensing Symposium, 1988. IGARSS '88. Remote Sensing: Moving Toward the 21st Century. International; 10/1988 DOI: 10.1109/IGARSS.1988.57017210.1109/IGARSS.1988.570172
Huang, C., Davis, L. S., Townshend, J. R. G. 2002. An assessment of support vector machines for land cover classification. International Journal of Remote Sensing 23, 725–749. DOI: 10.1080/0143116011004032310.1080/01431160110040323
Jensen, J. R. 1986. Introductory digital image processing: A remote sensing perspective. Englewood Cliffs, NJ: Prentice-Hall.
Kardeván, P., Vekerdy, Z., Róth, L., Sommer St.–Kemper, Th., Jordan, Gy., Tamás, J., Pechmann, I., Kovács, E., Hargitai, H., László, F. 2003. Outline of scientific aims and data processing status of the first Hungarian hyperspectral data acquisition flight campaign, 3rd EARSeL Workshop on Imaging Spectroscopy in Oberpfaffenhofen, 13-16 May 2003
Kertész, P., Király, G., Burai, P. 2014. Tree Species Mapping Using Airborne Hyperspectral Remote Sensing. In: Pfeifer, N., Zlinszky, A. (eds) Proceedings of the International Workshop on Remote Sensing and GIS for Monitoring of Habitat Quality, Vienna, 60–62.
Kruse, F. A., Lefkoff, A. B., Boardman, J.W., Heidebrecht, K.B., Shapiro, A. T., Barloon, J. P., Goetz, A. F. H. 1993. The spectral image processing system (SIPS) - Interactive visualization and analysis of imaging spectrometer data. Remote Sensing of Environment 44, 145–163. DOI: 10.1016/0034-4257(93)90013-n10.1016/0034-4257(93)90013-N
Landgrebe, D. A. 2003. Signal Theory Methods in Multispectral Remote Sensing. John Wiley & Sons, Inc.10.1002/0471723800
Lary, D. J., Alavi, A. H., Gandomi, A. H., Walker, A. L. 2015. Machine learning in geosciences and remote sensing. Geoscience Frontiers 7, 3–10. DOI: 10.1016/j.gsf.2015.07.00310.1016/j.gsf.2015.07.003
Lausch, A., Salbach, C., Doktor, D., Schmidt, A., Merbach, I., Pause, M. 2015. Deriving phenology of barley with imaging hyperspectral remote sensing. Ecological Modelling 295, 123–135. DOI: 10.1016/j.ecolmodel.2014.10.00110.1016/j.ecolmodel.2014.10.001
Lausch, A., Bannehr, L., Beckmann, M., Boehm, C., Feilhauer, H., Hacker, J. M., Heurich, M., Jung, A., Klenke, R., Neumann, C., Pause, M., Rocchini, D., Schaepman, M. E., Schmidtlein, S., Schulz, K., Selsam, P., Settele, J., Skidmore, A. K., Cord, A. F. 2016. Linking Earth Observation and taxonomic, structural and functional biodiversity: Local to ecosystem perspectives. Ecological indicators. DOI: 10.1016/j.ecolind.2016.06.22 (in press)
Lillesand, T. M., Kiefer, R. W., Chipman, J. W. 2004. Remote Sensing and Image Interpretation (Fifth Edition). John Wiley & Sons, Inc.
Liu, Zh-Y., Wu, H-F., Huang, J-F. 2010. Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis. Computers and Electronics in Agriculture 72, 99–106. DOI: 10.1016/j.compag.2010.03.00310.1016/j.compag.2010.03.003
van der Meer, F. D. 2004 Pixel-Based, Stratified and Contextual Analysis of Hyperspectral Imagery. In: de Jong, S. M., van der Meer, F. D. (eds) Remote Sensing Image Analysis: Including the Spatial Domain, Dordrecht, Springer Academic Publishers, 153–180.
Metternicht, G. I., Zinck, J. A. 1998. Evaluating the information content of JERS-1 SAR and Landsat TM data for discrimination of soil erosion features. ISPRS Journal of Photogrammetry & Remote Sensing, 53, 143–153. DOI: 10.1016/s0924-2716(98)00004-510.1016/S0924-2716(98)00004-5
Moshou, D., Vrindts, E., De Ketelaere, B., De Baerdemaeker, J., Ramon, H. 2001. A neural network based plant classifier. Computers and Electronics in Agriculture 31, 5–16. DOI: 10.1016/s0168-1699(00)00170-810.1016/S0168-1699(00)00170-8
Mucsi, L. Henits, L. 2011. Belvízelöntési térképek készítése közepes felbontású űrfelvételek szubpixel alapú osztályozásával (Mapping inland excess water using subpixel classification of middle resolution satellite images) Földrajzi közlemények 135(4), 365–378. (In Hungarian).
Tobak, Z. 2013. Urban surface analyses using high temporal and spectral resolution aerial imagery. PhD theses, University of Szeged, Hungary.
Tobak, Z., Csendes, B., Henits, L., van Leeuwen, B., Szatmári, J., Mucsi, L. 2012. Városi felszínek spektrális tulajdonságainak vizsgálata légifelvételek alapján (Spectral analysis of urban surfaces using aerial photography) In: Lóki, J. (ed) Az elmélet és gyakorlat találkozása a térinformatikában III, Debrecen, 413-420. ISBN:978-963-318-218-5 (In Hungarian:).
Ustin, L. S., Valko, P. G., Kefauver, S. C., Santos, M. J., Zimpfer, J. F., Smith, S. D. 2009. Remote sensing of biological soil crust under simulated climate change manipulations in the Mojave Desert. Remote Sensing of Environment 113, 317–328. DOI: 10.1016/j.rse.2008.09.01310.1016/j.rse.2008.09.013
Visi-Rajczi, E., Burai, P., Király, G., Albert, L. 2012. Ecological Characterization of the green areas in Sopron by Plant Chemical Analysis and Hyperspectral Recording. In: The Impact of Urbanization, Industrial and Agricultural Technologies on the Natural Environment: International Scientific Conference on Sustainable Development and Ecological Footprint. University of West Hungary, Sopron. ISBN 978-963-334-047-9