04.04.2016 Uni Trier 5 th EnMAP School EnMAP-Box Andreas Rabe Matthias Held Sebastian van der Linden Benjamin Jakimow [email protected] [email protected] www.hu-geomatics.de
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04.04.2016 Uni Trier
5th EnMAP School EnMAP-Box
Andreas Rabe
Matthias Held
Sebastian van der Linden
Benjamin Jakimow
www.hu-geomatics.de
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• the EnMAP-Box provides users of EnMAP-data (or similar) with a set of tools and applications to achieve best results during image analysis
• for this purpose the EnMAP-Box offers basic functionaliy for image processing as well as state-of-the-art algorithms for hyperspectral image analysis
• it is developed by Humboldt-Universität zu Berlin under contract of GFZ
EnMAP-Box
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Main development goals
• cost-free license agreement
• user-friendliness
• state-of-the-art applications for data analysis
• open source code
• rich application programming interface (hubAPI) to make it an evolving toolbox
– allow for easy and standardized integration of external developments
– offer flexibility for integrating code from various languages
EnMAP-Box
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Main development goals
• summary recently published in Remote Sensing Special Issue on EnMAP
EnMAP-Box
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• current version is EnMAP-Box 2.2.1
• developed with IDL 8.5
• runs in cost-free IDL Virtual Machine Mode
• IDL developers need a license
• supported platforms: Windows, Linux, Mac
• interfaces for code in C, C#, R, Python, JAVA
• can be integrated into ENVI 5.3
EnMAP-Box
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EnMAP-Box GUI
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EnMAP-Box GUI: Filelist and File Type
(hyperspectral) images
regression images
classification images
mask images
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EnMAP-Box GUI: Speclibs
spectral library
spectral library as pseudo-image
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• labeling a pixel or profile means: adding this pixel/profile to a region of interest (ROI) / spectra of interest (SOI)
• ROIs/SOIs are managed inside an attribute table
• specific attributes can later be used for supervised classification/regression
• labeled images can be converted to labeled speclibs
EnMAP-Box GUI: Labeling Tool
attribute table
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EnMAP-Box GUI: Image Labeling Tool
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EnMAP-Box GUI: Spectral Labeling Tool
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• wrapper to Mort Canty’s routine (http://mcanty.homepage.t-online.de/software.html)
EnMAP-Box Tools: linear and kernel PCA
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EnMAP-Box Tools: imageMath
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EnMAP-Box Applications: Supervised Methods
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• fully automated parameter tuning via grid search and cross-validation
• uses Java version of LIBSVM for optimization (IDL-Java Bridge)
EnMAP-Box Applications: Support Vector Machines
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• Random Forests for Classification and Regression (provided by Uni Bonn and HU Berlin)
EnMAP-Box Applications: Random Forests
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• groups redundant features
• useful for identifying hyperspectral or hypertemporal segments
EnMAP-Box Applications: Feature Clustering
hyperspectral data hypertemporal data
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• hyperspectral/hypertemporal segments could for example be ranked in terms of relevance using "SVM-based Feature Selection"
EnMAP-Box Applications: Feature Selection
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• Automatic Detection and Delineation of Surface Water Bodies (provided by GFZ Potsdam)
EnMAP-Box Applications: EnWaterMAP
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• SynthMix SVR(provided by HU Berlin)
EnMAP-Box Applications: LibMix and SynthMixSVR
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• Spectral Unmixing using SVRegression on synthetic mixures (LibMix) of pure endmembers (provided by HU Berlin)
EnMAP-Box Applications: synthMix-SVR
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• Spectral Index Data Mining Tool (provided by Uni Trier)
EnMAP-Box Applications: SpInMine
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• Agricultural Vegetation Indices (AVI) (provided by LMU München)
EnMAP-Box Applications: AVI
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1.application is started from EnMAP-Box menu
2.user input is collected via graphical dialogs (widget program)
3.image/data processing
4.results are presented via a report
Beside pure IDL, external R, Python or Matlab script, as well as stand-alone programs (e.g. C, Java, Fortran) can be integrated.
EnMAP-Box Application Development
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EnMAP-Box External R Applications
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Future EnMAP-Box in QGIS
- EnMAP-Box as a QGIS Plug-In
- Inroduce hyperspectral processing and viewer functionality to QGIS
- Programming in Python
- Tools and Apps implemented using the QGIS Processing Framework, allowing the usage inside the QGIS Model Builder
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Hands-On Exercise: The dataset
Okujeni, Akpona; van der Linden, Sebastian; Hostert, Patrick (2016): Berlin-Urban-Gradient
dataset 2009 - An EnMAP Preparatory Flight Campaign (Datasets). GFZ Data Services.
http://doi.org/10.5880/enmap.2016.002
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Hands-On Exercise: Open and explore the data
Open & explore Subsetting Random Sample SVR Accuracy
Topic: Imperviousness in Berlin
Open ‘EnMAP01_Berlin_Urban_Gradient_2009.bsq’ (image products)
-> “colored infrared”
‘LandCov_Layer_Level1_Berlin_Urban_Gradient_2009.bsq’ (add. data)
-> Impervious
Link images
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Hands-On Exercise: Subset the land cover stack
Open & explore Subsetting Random Sample SVR Accuracy
Start Tools > Spatial/Spectral Subset
Choose the land cover file and create a “spectral subset” to have the impervious fraction in a single band file (choose band 1).
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Hands-On Exercise: Random Sample
Open & explore Subsetting Random Sample SVR Accuracy
Draw a random sample from the impervious fraction reference pixels
Tools > Random Sampling
-> Absolute Sampling, 100 Pixels, Output with Complement
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Hands-On Exercise: Parameterize SVM
Open & explore Subsetting Random Sample SVR Accuracy
Start Applications > Regression> imageSVM > Parameterize SVR
-> The (feature) Image is the simulated EnMAP scene
-> The reference areas is the random sample from the imperviousness reference dataset (100 pixels)
An HTML report opens, click ‘Yes’ to apply the SVR model to the EnMAP scene
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Hands-On Exercise: Application of SVR Model
Open & explore Subsetting Random Sample SVR Accuracy
In the Apply SVR to Image window, everything from before is defined already, simply Apply.
Explore the result.
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Hands-On Exercise: Accuracy Assessment
Open & explore Subsetting Random Sample SVR Accuracy
Perform an accuracy assessment of the result (svrEstimation) with the sample complement
Applications > Accuracy Assessment > Regression
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Held, M., Rabe, A., Senf, C., van der Linden, S., & Hostert, P. (2015).
Analyzing hyperspectral and hypertemporal data by decoupling feature redundancy and feature relevance. Geoscience and Remote Sensing Letters, IEEE, 12(5), 983-987.
Mielke, C., Rogass, C., Boesche, N., Segl, K., & Altenberger, U. (2016).
EnGeoMAP 2.0—Automated Hyperspectral Mineral Identification for the German EnMAP Space Mission. Remote Sensing, 8(2), 127.
Okujeni, A., van der Linden, S., Tits, L., Somers, B., & Hostert, P. (2013).
Support vector regression and synthetically mixed training data for quantifying urban land cover. Remote Sensing of Environment, 137, 184-197.
Suess, S., van der Linden, S., Okujeni, A., Leitão, P. J., Schwieder, M., & Hostert, P.(2015).
Using class probabilities to map gradual transitions in shrub vegetation from simulated EnMAP data. Remote Sensing, 7(8), 10668-10688.
Waske, B., van der Linden, S., Oldenburg, C., Jakimow, B., Rabe, A., & Hostert, P. (2012).
ImageRF–a user-oriented implementation for remote sensing image analysis with Random Forests. Environmental Modelling & Software, 35, 192-193.
References
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Andreas Rabe
(andreas.rabe@
geo.hu-berlin.de)
Benjamin Jakimow (benjamin.jakimow@
geo.hu-berlin.de)
Matthias Held
(matthias.held@
geo.hu-berlin.de)
Sebastian van der Linden (sebastian.linden@ geo.hu-berlin.de)
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