5 th EnMAP School. EnMAP-Box. Andreas Rabe Matthias Held Sebastian van der Linden Benjamin Jakimow.

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1 5 th EnMAP School EnMAP-Box Andreas Rabe Matthias Held Sebastian van der Linden Benjamin Jakimow Uni Trier

2 EnMAP-Box 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 2

3 EnMAP-Box 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 3

4 EnMAP-Box Main development goals summary recently published in Remote Sensing Special Issue on EnMAP 4

5 EnMAP-Box current version is EnMAP-Box 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 5

6 EnMAP-Box Web-Portal 6

7 EnMAP-Box GUI 7

8 EnMAP-Box GUI: Filelist and File Type (hyperspectral) images regression images mask images classification images 8

9 EnMAP-Box GUI: Speclibs spectral library spectral library as pseudo-image 9

10 EnMAP-Box GUI: Labeling Tool 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 attribute table 10

11 EnMAP-Box GUI: Image Labeling Tool 11

12 EnMAP-Box GUI: Spectral Labeling Tool 12

13 EnMAP-Box Tools: linear and kernel PCA wrapper to Mort Canty s routine ( 13

14 EnMAP-Box Tools: imagemath 14

15 EnMAP-Box Applications: Supervised Methods 15

16 EnMAP-Box Applications: Support Vector Machines fully automated parameter tuning via grid search and cross-validation uses Java version of LIBSVM for optimization (IDL-Java Bridge) 16

17 EnMAP-Box Applications: Random Forests Random Forests for Classification and Regression (provided by Uni Bonn and HU Berlin) 17

18 EnMAP-Box Applications: Feature Clustering groups redundant features useful for identifying hyperspectral or hypertemporal segments hyperspectral data hypertemporal data 18

19 EnMAP-Box Applications: Feature Selection hyperspectral/hypertemporal segments could for example be ranked in terms of relevance using "SVM-based Feature Selection" 19

20 EnMAP-Box Applications: EnWaterMAP Automatic Detection and Delineation of Surface Water Bodies (provided by GFZ Potsdam) 20

21 EnMAP-Box Applications: LibMix and SynthMixSVR SynthMix SVR(provided by HU Berlin) 21

22 EnMAP-Box Applications: synthmix-svr Spectral Unmixing using SVRegression on synthetic mixures (LibMix) of pure endmembers (provided by HU Berlin) 22

23 EnMAP-Box Applications: SpInMine Spectral Index Data Mining Tool (provided by Uni Trier) 23

24 EnMAP-Box Applications: AVI Agricultural Vegetation Indices (AVI) (provided by LMU München) 24

25 EnMAP-Box Application Development 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. 25

26 EnMAP-Box External R Applications 26

27 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 27

28 Hands-On Exercise: The dataset Okujeni, Akpona; van der Linden, Sebastian; Hostert, Patrick (2016): Berlin-Urban-Gradient dataset An EnMAP Preparatory Flight Campaign (Datasets). GFZ Data Services. 28

29 Hands-On Exercise: Open and explore the data 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 Open & explore Subsetting Random Sample SVR Accuracy 29

30 Hands-On Exercise: Subset the land cover stack 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). Open & explore Subsetting Random Sample SVR Accuracy 30

31 Hands-On Exercise: Random Sample Draw a random sample from the impervious fraction reference pixels Tools > Random Sampling -> Absolute Sampling, 100 Pixels, Output with Complement Open & explore Subsetting Random Sample SVR Accuracy 31

32 Hands-On Exercise: Parameterize SVM 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 Open & explore Subsetting Random Sample SVR Accuracy 32

33 Hands-On Exercise: Application of SVR Model In the Apply SVR to Image window, everything from before is defined already, simply Apply. Explore the result. Open & explore Subsetting Random Sample SVR Accuracy 33

34 Hands-On Exercise: Accuracy Assessment Perform an accuracy assessment of the result (svrestimation) with the sample complement Applications > Accuracy Assessment > Regression Open & explore Subsetting Random Sample SVR Accuracy 34

35 References 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), 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, 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), 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,

36 Andreas Rabe geo.hu-berlin.de) Benjamin Jakimow geo.hu-berlin.de) Matthias Held geo.hu-berlin.de) Sebastian van der Linden geo.hu-berlin.de) 36