The EnMAP-box-A toolbox and application programming interface for EnMAP data processing

Remote Sensing

Volumn 7, Issue 9, 2015, Pages 11249-11266

  van der Linden, Sebastian ^a   Rabe, Andreas ^a   Held, Matthias ^a   Jakimow, Benjamin ^a   Leitão, Pedro J ^a   Okujeni, Akpona ^a   Schwieder, Marcel ^a   Suess, Stefan ^a   Hostert, Patrick ^a  

^a HUMBOLDT UNIVERSITY   (Germany)

Author keywords

EnMAP; HubAPI; Hyperspectral; ImageSVM; Imaging spectroscopy; Machine learning; Toolbox

Indexed keywords

APPLICATION PROGRAMMING INTERFACES (API); APPLICATION PROGRAMS; ARTIFICIAL INTELLIGENCE; IMAGE PROCESSING; LEARNING SYSTEMS; OPEN SOURCE SOFTWARE; OPEN SYSTEMS; SPECTROMETERS; SPECTROSCOPY; SPECTRUM ANALYSIS;

ENMAP; HUBAPI; HYPERSPECTRAL; IMAGESVM; IMAGING SPECTROSCOPY; TOOLBOX;

DATA HANDLING;

EID: 84937858907     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs70911249     Document Type: Article

References (69)

1. Goetz, A.F.H. Three decades of hyperspectral remote sensing of the Earth: A personal view. Remote Sens. Environ. 2009, 113, S5-S16.
2. Goetz, A.F.H.; Vane, G.; Solomon, J.E.; Rock, B.N. Imaging spectrometry for Earth remote-sensing. Science 1985, 228, 1147-1153.
3. Schaepman, M.E.; Ustin, S.L.; Plaza, A.J.; Painter, T.H.; Verrelst, J.; Liang, S. Earth system science related imaging spectroscopy-An assessment. Remote Sens. Environ. 2009, 113, S123-S137.
4. Kokaly, R.F.; Asner, G.P.; Ollinger, S.V.; Martin, M.E.; Wessman, C.A. Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies. Remote Sens. Environ. 2009, 113, S78-S91.
5. Schlerf, M.; Atzberger, C.; Hill, J. Remote sensing of forest biophysical variables using HyMap imaging spectrometer data. Remote Sens. Environ. 2005, 95, 177-194.
6. Kötz, B.; Schaepman, M.; Morsdorf, F.; Bowyer, P.; Itten, K.; Allgower, B. Radiative transfer modeling within a heterogeneous canopy for estimation of forest fire fuel properties. Remote Sens. Environ. 2004, 92, 332-344.
7. Thenkabail, P.S.; Smith, R.B.; De Pauw, E. Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sens. Environ. 2000, 71, 158-182.
8. Haboudane, D.; Miller, J.R.; Tremblay, N.; Zarco-Tejada, P.J.; Dextraze, L. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sens. Environ. 2002, 81, 416-426.
9. Zarco-Tejada, P.J.; Berjon, A.; Lopez-Lozano, R.; Miller, J.R.; Martin, P.; Cachorro, V.; Gonzalez, M.R.; de Frutos, A. Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy. Remote Sens. Environ. 2005, 99, 271-287.
10. G. Zhang, B.; Wu, D.; Zhang, L.; Jiao, Q.; Li, Q. Application of hyperspectral remote sensing for environment monitoring in mining areas. Environ. Earth Sci. 2012, 65, 649-658.
11. Riaza, A.; Buzzi, J.; García-Meléndez, E.; Carrère, V.; Müller, A. Monitoring the extent of contamination from acid mine drainage in the Iberian Pyrite Belt (SW Spain) using hyperspectral imagery. Remote Sens. 2011, S, 2166-2186.
12. Heiden, U.; Segl, K.; Roessner, S.; Kaufmann, H. Determination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data. Remote Sens. Environ. 2007, 111, 537-552.
13. Okujeni, A.; van der Linden, S.; Tits, L.; Somers, B.; Hostert, P. Support vector regression and synthetically mixed training data for quantifying urban land cover. Remote Sens. Environ. 2013, 1S7, 184-197.
14. Van der Linden, S.; Hostert, P. The influence of urban structures on impervious surface maps from airborne hyperspectral data. Remote Sens. Environ. 2009, 11S, 2298-2395.
15. Asner, G.P.; Martin, R.E. Spectral and chemical analysis of tropical forests: Scaling from leaf to canopy levels. Remote Sens. Environ. 2008, 112, 3958-3978.
16. Somers, B.; Asner, G.P. Hyperspectral time series analysis of native and invasive species in Hawaiian rainforests. Remote Sens. 2012, 4, 2518-2529.
17. Roberts, D.A.; Gardner, M.; Church, R.; Ustin, S.; Scheer, G.; Green, R. Mapping chaparral in the Santa Monica mountains using multiple endmember spectral mixture models. Remote Sens. Environ. 1998, 65, 267-279.
18. Kooistra, L.; Wamelink, W.; Schaepman-Strub, G.; Schaepman, M.; van Dobben, H.; Aduaka, U.; Batelaan, O. Assessing and predicting biodiversity in a floodplain ecosystem: Assimilation of net primary production derived from imaging spectrometer data into a dynamic vegetation model. Remote Sens. Environ. 2008, 112, 2118-2138.
19. Suess, S.; van der Linden, S.; Okujeni, A.; Leitäo, P.; Schwieder, M.; Hostert, P. Using class probabilities to map gradual transitions in shrub vegetation from simulated EnMAP data. Remote Sens. 2015, 7, 18668-18688.
20. Okin, G.S.; Roberts, D.A.; Murray, B.; Okin, W.J. Practical limits on hyperspectral vegetation discrimination in arid and semiarid environments. Remote Sens. Environ. 2001, 77, 212-225.
21. Ustin, S.L.; Valko, P.G.; Kefauver, S.C.; Santos, M.J.; Zimpfer, J.F.; Smith, S.D. Remote sensing of biological soil crust under simulated climate change manipulations in the Mojave desert. Remote Sens. Environ. 2009, 113, 317-328.
22. Dozier, J.; Green, R.O.; Nolin, A.W.; Painter, T.H. Interpretation of snow properties from imaging spectrometry. Remote Sens. Environ. 2009, 113, S25-S37.
23. Zhao, S.; Jiang, T.; Wang, Z. Snow grain-size estimation using Hyperion imagery in a typical area of the Heihe River Basin, China. Remote Sens. 2013, 5, 238-253.
24. Isada, T.; Hirawake, T.; Kobayashi, T.; Nosaka, Y.; Natsuike, M.; Imai, I.; Suzuki, K.; Saitoh, S.-I. Hyperspectral optical discrimination of phytoplankton community structure in Funka Bay and its implications for ocean color remote sensing of diatoms. Remote Sens. Environ. 2015, 159, 134-151.
25. Ryan, J.; Davis, C.; Tufillaro, N.; Kudela, R.; Gao, B.C. Application of the hyperspectral imager for the coastal ocean to phytoplankton ecology studies in Monterey Bay, CA, USA. Remote Sens. 2014, 6, 1007-1025.
26. Plaza, A.; Benediktsson, J.A.; Boardman, J.W.; Brazile, J.; Bruzzone, L.; Camps-Valls, G.; Chanussot, J.; Fauvel, M.; Gamba, P.; Gualtieri, A.; et al. Recent advances in techniques for hyperspectral image processing. Remote Sens. Environ. 2009, 113, S110-S122.
27. Koetz, B.; Morsdorf, F.; Van der Linden, S.; Curt, T.; Allgöwer, B. Multi-source land cover classification for forest fire management based on imaging spectrometry and LiDAR data. For. Ecol. Manag. 2008, 256, 263-271.
28. Okujeni, A.; van der Linden, S.; Jakimow, B.; Rabe, A.; Verrelst, J.; Hostert, P. A comparison of advanced regression algorithms for quantifying urban land cover. Remote Sens. 2014, 6, 6324-6346.
29. Leitão, P.J.; Schwieder, M.; Suess, S.; Catry, I.; Milton, E.J.; Moreira, F.; Osborne, P.E.; Pinto, M.J.; van der Linden, S.; Hostert, P. Mapping beta diversity from space: Sparse generalised dissimilarity modelling (SGDM) for analysing high-dimensional data. Methods Ecol. Evol. 2015, 6, 764-771.
30. Jacquemoud, S.; Verhoef, W.; Baret, F.; Bacour, C.; Zarco-Tejada, P.J.; Asner, G.P.; François, C.; Ustin, S.L. PROSPECT+SAIL models: A review of use for vegetation characterization. Remote Sens. Environ. 2009, 113, S56-S66.
31. Laurent, V.C.E.; Verhoef, W.; Clevers, J.G.P.W.; Schaepman, M.E. Inversion of a coupled canopy-atmosphere model using multi-angular top-of-atmosphere radiance data: A forest case study. Remote Sens. Environ. 2011, 115, 2603-2612.
32. Herold, M.; Gardner, M.E.; Roberts, D.A. Spectral resolution requirements for mapping urban areas. IEEE Trans. Geosci. Remote Sens. 2003, 41, 1907-1919.
33. Okujeni, A.; van der Linden, S.; Hostert, P. Extending the vegetation-impervious-soil model using simulated EnMAP data and machine learning. Remote Sens. Environ. 2015, 158, 69-80.
34. Mariotto, I.; Thenkabail, P.S.; Huete, A.; Slonecker, E.T.; Platonov, A. Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission. Remote Sens. Environ. 2013, 139, 291-305.
35. Guanter, L.; Kaufmann, H.; Segl, K.; Foerster, S.; Rogass, C.; Chabrillat, S.; Kuester, T.; Hollstein, A.; Rossner, G.; Chlebek, C.; et al. The EnMAP spaceborne imaging spectroscopy mission for Earth observation. Remote Sens. 2015, 7, 8830.
36. Vane, G.; Goetz, A.F.H.; Wellman, J.B. Airborne imaging spectrometer: A new tool for remote sensing. IEEE Trans. Geosci. Remote Sens 1984, GE-22, 546-549.
37. Vane, G.; Green, R.O.; Chrien, T.G.; Enmark, H.T.; Hansen, E.G.; Porter, W.M. The airborne visible infrared imaging spectrometer (AVIRIS). Remote Sens. Environ. 1993, 44, 127-143.
38. Mark, H. Chemometrics in near-infrared spectroscopy. Anal. Chim. Acta 1989, 223, 75-93.
39. Clark, R.N.; King, T.V.V.; Klejwa, M.; Swayze, G.A.; Vergo, N. High spectral resolution reflectance spectroscopy of minerals. J. Geophys. Res. Solid Earth 1990, 95, 12653-12680.
40. Adams, J.B.; Smith, M.O.; Johnson, P.E. Spectral mixture modeling-A new analysis of rock and soil types at the Viking Lander-1 site. J. Geophys. Res. Solid Earth Planets 1986, 91, 8098-8112.
41. Kruse, F.A.; Lefkoff, A.B.; Boardman, J.W.; Heidebrecht, K.B.; Shapiro, A.T.; Barloon, P.J.; Goetz, A.F.H. The spectral image-processing system (sips)-Interactive visualization and analysis of imaging spectrometer data. Remote Sens. Environ. 1993, 44, 145-163.
42. Roberts, D.A.; Smith, M.O.; Adams, J.B. Green vegetation, nonphotosynthetic vegetation, and soils in AVIRIS data. Remote Sens. Environ. 1993, 44, 255-269.
43. Kruse, F.A.; Lefkoff, A.B.; Dietz, J.B. Expert system-based mineral mapping in northern Death-Valley, California Nevada, using the airborne visible infrared imaging spectrometer (AVIRIS). Remote Sens. Environ. 1993, 44, 309-336.
44. EXELIS. ENVI 5.2-Environment for Visualizing Images. Available online: http://www.exelisvis.com/ProductsServices/ENVIProducts.aspx (accessed on 27 August 2015).
45. Kuo, B.-C.; Landgrebe, D.A. Nonparametric weighted feature extraction for classification. IEEE Trans. Geosci. Remote Sens. 2004, 42, 1096-1105.
46. Breiman, L. Random forests. Mach. Learn. 2001, 45, 5-32.
47. Richards, J.A. Analysis of remotely sensed data: The formative decades and the future. IEEE Trans. Geosci. Remote Sens. 2005, 43, 422-432.
48. Verrelst, J.; Muñoz, J.; Alonso, L.; Delegido, J.; Rivera, J.P.; Camps-Valls, G.; Moreno, J. Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3. Remote Sens. Environ. 2012, 118, 127-139.
49. Chang, C.-C.; Lin, C.-J. LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2011, 2, 1-27.
50. Li, S.Z. Markov Random Field Modeling in Image Analysis; Springer-Verlag: London, UK, 2009.
51. Rasmussen, C.E.; Williams, C.K.I. Gaussian Processes for Machine Learning; The MIT Press: Cambridge, MA, USA, 2006.
52. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2014.
53. Python Software Foundation. Python (TM). Available online: http://www.python.org (accessed on 27 August 2015).
54. Aho, A.V. Software and the future of programming languages. Science 2004, 303, 1331-1333.
55. Perkel, J.M. Programming: Pick up Python. A powerful programming language with huge community support. Nature 2015, 518, 125-126.
56. CNES. Orfeo Toolbox-Orfeo Toolbox is not a Black Box. Available online: http://www.orfeo-toolbox.org (accessed on 27 August 2015).
57. Bochow, M.; Heim, B.; Küster, T.; Rogaß, C.; Bartsch, I.; Segl, K.; Reigber, S.; Kaufmann, H. On the use of airborne imaging spectroscopy data for the automatic detection and delineation of surface water bodies. In Remote Sensing of Planet Earth; Chemin, Y., Ed.; InTech: Rijeka, Croatia, 2012; pp. 3-22.
58. Waske, B.; van der Linden, S.; Oldenburg, C.; Jakimow, B.; Rabe, A.; Hostert, P. ImageRF-A user-oriented implementation for remote sensing image analysis with random forests. Environ. Model. Softw. 2012, 35, 192-193.
59. Janz, A.; van der Linden, S.; Waske, B.; Hostert, P. ImageSVM-A user-oriented tool for advanced classification of hyperspectral data using support vector machines. In Proceedings of the 5th EARSeL SIG IS Workshop-"Imaging Spectroscopy: Innovation in Environmental Research", Bruges, Belgium, 23-25 April 2007.
60. Roscher, R.; Waske, B.; Foerstner, W. Incremental import vector machines for classifying hyperspectral data. IEEE Trans. Geosci. Remote Sens. 2012, 50, 3463-3473.
61. Wold, S.; Sjostrom, M.; Eriksson, L. PLS-regression: A basic tool of chemometrics. Chemom. Intell. Lab. Syst. 2001, 58, 109-130.
62. Held, M.; Rabe, A.; Senf, C.; van der Linden, S.; Hostert, P. Analyzing hyperspectral and hypertemporal data by decoupling feature redundancy and feature relevance. IEEE Geosci. Remote Sens. Lett. 2015, 12, 983-987.
63. Rogge, D.M.; Rivard, B.; Zhang, J.; Feng, J. Iterative spectral unmixing for optimizing per-pixel endmember sets. IEEE Trans. Geosci. Remote Sens. 2006, 44, 3725-3736.
64. Olofsson, P.; Foody, G.M.; Stehman, S.V.; Woodcock, C.E. Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation. Remote Sens. Environ. 2013, 129, 122-131.
65. Alcantara, C.; Kuemmerle, T.; Prishchepov, A.V.; Radeloff, V.C. Mapping abandoned agriculture with multi-temporal MODIS satellite data. Remote Sens. Environ. 2012, 124, 334-347.
66. Schwieder, M.; Leitao, P.J.; Suess, S.; Senf, C.; Hostert, P. Estimating fractional shrub cover using simulated EnMAP data: A comparison of three machine learning regression techniques. Remote Sens. 2014, 6, 3427-3445.
67. EXELIS. IDLdoc. Available online: http://www.exelisvis.com/docs/Using_IDLdoc_to_Generate.html (accessed on 27 August 2015).
68. Dalton, J.B.; Bove, D.J.; Mladinich, C.S.; Rockwell, B.W. Identification of spectrally similar materials using the USGS Tetracorder algorithm: The calcite-epidote-chlorite problem. Remote Sens. Environ. 2004, 89, 455-466.
69. Segl, K.; Guanter, L.; Rogass, C.; Kuester, T.; Roessner, S.; Kaufmann, H.; Sang, B.; Mogulsky, V.; Hofer, S. EeteS-The EnMAP end-to-end simulation tool. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2012, 5, 522-530.