IQPC'15

IQPC 2015 TRACK: Water detection and classification on multi-source remote sensingand terrain data

A. Olasz, D. Kristóf, M. Belényesi, K. Bakos (FÖMI)

Z. Kovács, B. Balázs, Sz. Szabó (University of Debrecen)

ISPRS Geospatial Week - GeoBigData workshop

Montpellier / La Grande Motte, France, 1-2 October 2015

Institute of Geodesy, Cartography and Remote Sensing

Budapest, Hungary

University of Debrecen,Department of Physical Geography and Geoinformation Systems

Debrecen, Hungary

Introduction

ISPRS Geospatial Week - GeoBigData workshopMontpellier / La Grande Motte, France, 1-2 October 2015

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• Main objective: thematic mapping of water-related categories– Water surfaces, water-affected soil and

vegetation

• Numerous remotely sensed data sources• „Optimal” solution needed to possibly

support operational tasks– Efficiency in terms of data and resource needs

Challenge


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• Provide thematic maps with a set of pre-defined categories

• Create the best possible classification using the simplest set/combination of input sources

• Try to reduce the number of input datasets needed for accurate processing

• Develop algorithms that are fast to run• Find the best balance of complexity and accuracy

(maximize efficiency) during data processing

Study area


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Data provided


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• Medium-resolution (Landsat 8) satellite imagery (A/B/C)

• High-resolution aerial hyperspectral imagery (B, C)• High-resolution visible (RGB) orthophotos (B, C)• Terrain (DTM) and surface models (DSM) derived

from airborne LiDAR point clouds (B, C)• Reference data for classification, verification and

benchmarking

Landsat 8 data (A)


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Bands Wavelength(micrometers)

Resolution(meters)

Band 1 - Coastal aerosol 0.43 - 0.45 30

Band 2 - Blue 0.45 - 0.51 30

Band 3 - Green 0.53 - 0.59 30

Band 4 - Red 0.64 - 0.67 30

Band 5 - Near Infrared (NIR) 0.85 - 0.88 30

Band 6 - SWIR 1 1.57 - 1.65 30

Band 7 - SWIR 2 2.11 - 2.29 30

Band 8 - Panchromatic 0.50 - 0.68 15

Band 9 - Cirrus 1.36 - 1.38 30

Band 10 - Thermal Infrared (TIRS) 1 10.60 - 11.19 100 ( resampled to 30)

Band 11 - Thermal Infrared (TIRS) 2 11.50 - 12.51 100 (resampled to 30)

Landsat 8OperationalLand Imager(OLI)andThermalInfraredSensor(TIRS)

Hyperspectral data (B,C)


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• Georeferenced (UTM34N) radiance data in .dat (ENVI) format containing 128 bands.

• Spectral and spatial resolution and accuracy:– Instrumentation / camera: aerial hyperspectral instrument (AISA

Eagle)– Spectral range: approx. 400-1000 nm VNIR– Number of channels: 128– Spectral resolution: 5 nm– Spatial resolution: 1.5 m / pixel– Spatial accuracy: 2.5 m (RMSE)– Data content: spectral radiance– Data Type / Format: Georeferenced ENVI DAT file; 16 bit BSQ

Digital Terrain & Surface Models (B, C)


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• Generated from aerial laser scanner (ALS) data, with gap filling by appropriate interpolation.– Original density: 4 points / m2– Grid resolution: 1 meter / pixel– Spatial accuracy: 30 cm– Data content: height values in cm (over Baltic)– Data Type / Format: raster (unsigned long integer)

(.tif)– Projection: UTM/WGS84/34N

Digital orthophotos (B, C)


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• Spectral range: Visible (RGB)• Number of Bands: 3• Spatial resolution: 15 cm / pixel• Spatial accuracy of 30 cm (RMSE)• Data Type / Format: Radiometrically and geometrically

corrected images in TIFF• Projection: UTM/WGS84, zone 34 North

Reference data & classes


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• 2 sets per study area provided to participants

• 1 independent set kept for validation and benchmarking

Evaluation


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• Submissions:– Georeferenced thematic rasters in GeoTIFF format,

containing the codes of thematic categories– Concise description of the whole methodology and

processing chain (including algorithms and parameters, with references to relevant literature wherever available)

• Evaluation and scoring was based upon the complexity, time- and resource efficiency of the methodology and the data requirements for processing

And the winner is…


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• Exchange of e-mails with several teams interested in the challenge, but finally only one valid and successful submission:

• University of Debrecen, Department of Physical Geography and Geoinformation Systems:Zoltán Kovács, Boglárka Balázs, Szilárd Szabó

The Solution by the Debrecen Team


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• Solution provided for area A (Landsat 8)Workflow:

1. Extraction of pixel values for the reference points (ArcGIS/Python)

2. Use of the in-house developed HypDA (Hyperspectral Data Analyst) to select the spectral indices most suitable for the separation of predefined classes

3. Rattle data mining module in R with several classification algorithms, quality measures and probability maps• Cloud masking to remove bias• GLM (General Linear Model) technique found to perform

best


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The Solution by the Debrecen Team


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Conclusions an Outlook


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• The Debrecen Team has provided an elegant and efficient solution using only Landsat data, yet providing very accurate results

• Track description and data still available – if you would like to try yourself:

http://iqmulus.eu/iqmulus-processing-contest-2015/

• We are open to receive and evaluate other solutions – outside the „official” track




Thank you!Further information:

Dániel Kristóf (FÖMI)[email protected]

Szilárd Szabó (Debrecen University)[email protected]

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www.iqmulus.eu

www.linkedin.com/groups

/IQmulus-FP7-project-7470531


IQmulus (FP7-ICT-2011-318787) is a 4-year Integrating Project (IP) in the area of Intelligent Information Management within ICT 2011.4.4 Challenge 4: Technologies for Digital Content and Languages. IQmulus started on November 1, 2012, and will finish October 31, 2016.

mailto:[email protected]






http://www.iqmulus.eu/

http://www.linkedin.com/groups/IQmulus-FP7-project-7470531




Science

IQPC'15