JECAM SAR Inter-Comparison Experiment

Transcription

1 JECAM SAR Inter-Comparison Experiment Crop Type Identification & Mapping Laura Dingle Robertson, Andrew Davidson, Heather McNairn, Mehdi Hosseini, Scott Mitchell, & Michael H. Cosh February 15, 2018

2 Agenda 1. JECAM SAR Inter- Comparison Experiment 2. Crop Type Identification & Mapping Activities 3. Partners 4. Field data received 5. EO data pre-processing 6. SAR speckle filter testing 7. Next steps Sentinel 1 RADARSAT2

3 JECAM SAR Inter-Comparison Experiment 1. Crop Type Identification & Mapping Compare and develop crop mapping methods that use SAR only and SAR/optical combination(s) over JECAM sites around the world to come up with best practices for EO crop type identification & mapping 2. LAI & Biomass (Mehdi Hosseini) Compare models that use SAR for LAI & Biomass AAFC 2016 Annual Crop Inventory Optical and SAR-based decision tree classifier Classified Alberta, Saskatchewan & Manitoba since 2009 Classified agricultural extent since 2011

4 Crop Type Identification Activity 1a. Applying Agriculture and Agri-Food Canada (AAFC) Earth Observation Crop Inventory Method to other JECAM Sites Activity 1b. Applying JECAM Member Sites SAR and Optical, OR SAR only (single frequency) Classification Methodologies to Multiple Regions Activity 2. Reducing the Impact of Cloud Cover on Operational Crop Inventories & Mapping Goals Activity 3 and 4. Multi-frequency SAR imagery for Crop Type Mapping AND Compact polarimetry and/or polarimetric decomposition variables for Crop Type Mapping. Participation Document

5 JECAM Partners

6 Partner Survey data LAI & Biomass 2018 Field Plan Argentina (3 Crops) Survey & LAI/Biomass Bangladesh (LAI & Moisture: Rice) LAI Belgium (~22 Crops, ~1004 fields) Brazil Sao Paulo Brazil Tocantins (~14 Land cover Types, ~1441 polygons) (9 Landover Types, ~900 polygons) Canada Carman (Survey) (LAI, Biomass, SM: Corn, Wheat) Canada Casselman (8 Crops, ~810 fields) Survey only Survey & LAI/Biomass * plus new site Cerrados Survey only Survey only France Toulouse (8 Crops, ~985 fields Survey & LAI/Biomass/Soil Moisture Germany (DEMMIN) (7 Crops, ~42 fields) (LAI & Biomass (in progress)) India (5 Crops, ~64 fields) (LAI: Biomass, Rice, Cotton, Banana, Sugarcane) Italy Apulia Tavoliere (5 Crops, ~53 fields) (LAI: Wheat, Barley & Oats) Survey & LAI/Biomass/Soil Moisture Survey & LAI/Biomass/Soil Moisture Survey & LAI/Biomass/Soil Moisture Poland (LAI: Wheat & Corn) LAI/Biomass/Soil Moisture Taiwan (LAI/Biomass: Rice) LAI/Biomass/Soil Moisture Ukraine (Survey)* (LAI: Wheat, Corn, Soy) USA Georgia (~14 Crops, ~586 fields) Survey & LAI/Biomass/Soil Moisture USA Iowa (Based on CDL) (SMAPVEX 2016 data) Survey (CDL) USA Massachusetts (Based on CDL) Survey (CDL & Survey) USA-Michigan (Based on CDL) Survey (CDL) & LAI USA North Dakota (Based on CDL) (LAI, SM, Biomass) (LAI, SM, Biomass + CDL) Field Data from JECAM Partners for 2014 to 2017 & 2018 Collection Plan 18 Partners provided field data including 10 for LAI/Biomass 16 partners committed to collecting field data in 2018 to go along with RADARSAT2 acquisitions RADARSAT2 quad polarization data will be collected to support the compact polarimetry portion of the experiment A new Partner - Brazil Cerrados!

7 EO SAR Data Preprocessing SAR-Simulation Terrain Correction Literature shows support for both SAR Simulation Terrain Correction and Range Doppler Correction (Bayanudin & Jatmiko 2016; Jiang et al., 2016) with SAR Simulation having slightly more rigorous results. As a matter of best practice SAR Simulation Terrain Correction was selected Normalization to incidence angle Only processed for Component 1. Component 2 uses Local Incidence angle as a parameter in the model Variations of normalization to: local incidence angle; projected local incidence angle; derived angle based upon ellipsoid; and no normalization were tested. While there were no significant differences between these for small-area, relatively flat sites, as a matter of best practice it was decided to normalize all images to local incidence angle. Order of operations testing Typically literature recommends filtering before ortho-rectifying /terrain correction. Testing of order of operations showed slightly degraded overall classification accuracies and longer processing time with filtering first.

8 SAR speckle filter testing: window size & filter type Test parameters Crop types of corn, soybean, wheat/no wheat/double crop with three test partners: Canada Casselman: small field size (~3 ha) (Complete) Argentina: medium field size (~25ha, In progress) USA Iowa: large field size (~30-60 ha continuous fields resulting in large homogenous areas of same crop types). Filters: Gamma Map (current AAFC operational filter), Touzi (Touzi, 2002) and Multi-Temporal (Quegan et al., 2000). Multiple window sizes and passes.

9 Gamma MAP & Touzi Filters GAMMA Map Filter Current operational filter with a 7x7 window; reduces speckle as a function of the co-efficient of variation within the window R is the center smoothed pixel, found as: R = I, for Ci less than or equal to Cu; OR R = BB II+ DD 2 AAAAAAAA, for Cu < Ci < Cmax; OR R = CP, for Ci greater than or equal to Cmax where: NLOOK = Number of Looks VAR = Variance in filter window CP = Initial center pixel value I = Mean value in the filter window Cu = Ci = II NNNNNNNNNN VVVVVV II Cmax = 2 CCCC 1+CCCC 2 ALFA = CCCC 2 CCCC 2 B = ALFA-NLOOK-1 Touzi Filter is an adaptive filter where the underlying features are identified based upon different detectors and sub-windows. As features are identified and filtered they are set aside ; Window size adapts as each filter step occurs. 5 iterative steps: 1. Point Target Filtering 2. Curvilinear Filtering 3. Homogenous Area Filtering 4. Multiresolution Edge Detection and Filtering 5. Stationary Area Filtering

10 Filtered RADARSAT2 VV Images Casselman, Ontario Casselman RADARSAT2-VV, May 29, 2016 Gamma MAP 7 x 7 Window Casselman RADARSAT2-VV, May 29, 2016 Gamma MAP 13 x 13 Window Casselman RADARSAT2-VV, May 29, 2016 Touzi 13 x13 Window Casselman RADARSAT2-VV, May 29, 2016 Touzi 25 x 25 Window

11 Filter Testing Outcomes 92.0 Comparison of different SAR filters and window sizes based upon the percent overall accuracy of AAFC's operational Decision Tree Classifier and SAR Only data. Multi-Temporal filter has poor results with less dense temporal stacks Percent overall accuracy Casselman Iowa Best Gamma MAP results Filter Type and Window Size Best Touzi results

12 JECAM Casselman Best Outcome (3 crops) Touzi Filter 25 x 25 window Wheat Corn Soybeans Wheat Corn Soybeans Overall accuracy: 88.9%, Kappa: 0.79 Gamma MAP 13 x 13 window Wheat Corn Soybeans Wheat Corn Soybeans Overall accuracy: 86.7%, Kappa: 0.75 User s Accuracy Wheat Corn Soybeans User s Accuracy Wheat Corn Soybeans Producer s Accuracy Producer s Accuracy McNemar s Chi-squared test with continuity correction (no statistical significance) McNemar's chi-squared = , df = 1, p-value <

13 JECAM Iowa Best Outcome (2 crops) Touzi Filter 13 x 13 window Corn Soybeans Corn Soybeans User s Accuracy Corn Soybeans Producer s Accuracy Overall accuracy: 90.5%, Kappa: 0.77 Gamma MAP 11 x 11 window Corn Soybeans User s Accuracy Producer s Accuracy Corn Soybeans Corn Soybeans Overall accuracy: 90.4%, Kappa: 0.77 No statistical difference

14 Finalize filter selection Next Steps Finalize pre-processing automation EO & field data stack creation and distribution to JECAM Partners (February/March) Running AAFC Crop Inventory Decision Tree Method on all Stacks Running Cloud Cover Iterations on All Stacks re-launch

15 Acknowledgements & Thanks Funding provided by the Canadian Space Agency under the Government Related Initiatives Program (GRIP). Many thanks to the Agriculture and Agri-Food Canada Earth Observation team especially Catherine Champagne, Patrick Rollin, Thierry Fisette, Ziad Aly, Elizabeth Eidness, and University of Guelph and University of Waterloo students: Hanna Holman, Benjamin Kovacs, Natalija Nikolic, and Holden Ciufo.