Observations from the LPIS QA screening regarding CwRS image quality

Transcription

1 Observations from the LPIS QA screening regarding CwRS image quality Lemajic S., Milenov P., Tarko A. MARS Unit, IES, JRC Control Methods Workshop 2012 Palace Hotel, March 2012, Varese Outline Feedback on the quality of the CwRS orthoimagery for the LPIS QA Investigation of the usability of the RFV approach to complement the CwRS orthoimage information deficit 1

2 Feedback on the quality of the CwRS orthoimagery for the LPIS QA Purpose of the LPIS Quality Assessment Measure the performance of the LPIS on the ability to fulfil two explicit LPIS functions: unambiguous localisation of all claimed agricultural parcels quantification of all eligible area for crosschecks Inspection (called ETS) based on: Manual photointerpretation and delineation of the agriculture land cover On the area represented of by the reference parcels Including assessment of reference parcel design On the most recent orthoimagery as a reference Current year CwRS VHR imagery is used in more than 80% of the cases 2

3 Objective of the 2010 screening evaluate the inspection methodology and feedback issues and examples into an ETS revision identify issues with the application of the inspection methodology for each individual LPIS custodian so they can take remediating actions IMPORTANT NOTE: It is not intended to validate the 2010 scores nor to validate the analysis report produced by the individual LPIS custodians. JRC made no attempt to link this screening to the 2010 scores. Reference Orthoimagery The orthoimages displayed during the screening were those: Provided by JRC for the 2010 CwRS (more than 80% of cases) Used by the MS to perform the LPIS QA Made available to GeoCAP team via a WMS by CID or by the MS directly IMPORTANT NOTES: These portals might present a different version of the orthoimagery actually used for the LPIS QA Not acceptable! the WMS might have induced some loss of information content Often the original CwRS imagery was consulted! 3

4 Rate of skipped parcels due to bad image quality all processed parcels (100%) skipped parcels 4403 (10.90%) inspected parcels (89.10%) image-related reason of skipping (T2, T3, T4, C4) non-image related reason of skipping (T5, F1, E1) 3806 (9.42%) 597 (1.48%) % of skipped parcels 86.44% 13.56% Rate of skipped parcels due to bad image quality The numbers on the following tables and charts reflect input data from 37 screened Lots (ETS 2010) under the assumption that: all skipped parcels have been given in the LPIS Sample Pre-selection Status XML all reasons of skipping the parcel have been correctly identified and assigned by the MS 9 8 Number of occurrence of skipped parcels No of LOTS (with assigned reason of skipping) >200 4 Number of LOTS >200 Abundance of skipped parcels 4

5 Rate of skipped parcels grouped by the reason of skipping for all LOTs Reason for skipping Parcel partially or wholly outside image area Parcel outside control zone (i.e. VHR zone) Parcel partially or wholly covered by clouds Parcel geometry is not available LUI interpretation impossible with the given orthoimage Failure to inspect the reference parcel due to force majeure circumstances, observed on the LUI Obvious error not covered by another code T2 T3 T4 T5 C4 F1 E1 Number of skipped parcels % of skipped parcels % of all processed parcels Rate of skipped parcels grouped by the reason of skipping for all LOTs Reason for skipping Parcel partially or wholly outside image area Parcel outside control zone (i.e. VHR zone) Parcel partially or wholly covered by clouds Parcel geometry is not available LUI interpretation impossible with the given orthoimage Failure to inspect the reference parcel due to force majeure circumstances, observed on the LUI Obvious error not covered by another code T2 T3 T4 T5 C4 F1 E1 Number of skipped parcels % of skipped parcels % of all processed parcels Possibly issue related to ortho-production 5

6 Examples of skipped parcels Parcel outside control zone (i.e. VHR zone) Parcel partially or wholly covered by clouds Obvious error not covered by another code Examples of skipped parcels LUI interpretation impossible with the given orthoimage 6

7 Rate of skipped parcels grouped by the reason of skipping from all inspected parcels Rate of skipped parcels grouped by the reason of skipping from all skipped parcels 7

8 Impact of the image information content on the ETS interpretation occlusion of tree and shadows effect ambiguity in the CAPI CwRS VHR National Aerial Impact of the image information content on the ETS interpretation occlusion of tree and shadows effect ambiguity in the CAPI CwRS VHR National Aerial 8

9 Specific issues found Down-sampling of orthoimagery used for LPIS QA Down-sampling??? Pixel size degraded??? 1:1500, Ikonos, pix. 1m 1:1500, Aerial image, pix. 1m Specific issues found Image saturation CwRS VHR delivered CwRS VHR enhanced in JRC Lack of proper metadata on radiometric postprocessing! 9

10 Specific issues found Local artefacts CwRS VHR Key Fact of image radiometry Aerial vs. Satellite Aerial Digital photogrammetry Scalable GSD Weakly defined, nonstandard products Variability of instruments and data collection scenarios Several data providers and subjective preferences Undefined cal/val VHR satellite imaging Fixed GSD Well-defined, standard output products Well-defined instruments and data collection scenarios Single data provider for each instrument Global, vicarious cal/val Ref: Eija Honkavaara, Lauri Markelin, Finnish Geodetic Institute, EduServ 10, Dublin, March, 2012 Advantages that should not be lost during the processing 10

11 ad Initial acceptance of the orthoimage for ETS Visualize the orthoimage. Consult its metadata. Consult the metadata of the raw image Is the orthoimage pixel size bigger than the GSD of the raw image? No Is the resampling of the orthoimage applied i ncorrectly? No Are there any inacceptabl e artifacts from the pan-sharpening? No Are there any inacceptabl e l ocal artifacts from the geometri c orthocorrection? No Are there channel s missing on the orthoimage, that were provi ded with the raw data? No Is the radiometric quality insuffici ent (saturation of histogram, bi t depth)? No Are there inacceptable artifacts caused by the mosaicking process? No Was the orthoimage compressed (ECW)? No Yes Accept the orthoimage for ETS inspection Does the image compression rate lead to an inacceptable degradation of the ini tial image quality? No Yes Yes Yes Yes Yes Yes Yes Yes Stop the ETS inspection for the area covered w ith the giv en orthoimage, and report Conclusions & Comments The quality of the CwRS VHR orthoimagery is crucial for the correct conduction of the LPIS QA inspection and for the reliability of the quality results The quality of CwRS VHR orthoimage is indeed not optimal in certain cases CwRS imagery must be processed in a way to meet both the LPIS QA and CwRS requirements The investigation of alternatives for obtaining good quality datasets recommended by the LPIS QA peer-reviewers More attention should be paid to orthoimage production and quality control Some additional checks are introduced in LPIS QA methodology for 2012 Additional image check introduced for 2011 LPIS (ETS v.5.1) The ETS operator should conduct certain basic checks of the orthoimage properties and parameters prior to ETS inspection A list of issues needs to be verified before the orthoimage be accepted as suitable for ETS 11

12 Investigation of the usability of the RFV approach to complement the CwRS orthoimage information deficit Feedback from the RFV data RFV general RFV provided data RFV data screening Examples 12

13 RFV general Control method LPIS QA CwRS Type of check Check unclear cases of land cover/land use interpretation during CAPI Clarify unclear LUI boundary due to feature obstruction or insufficient information content Check the land use (mainly crop recognition) Possibly check cross compliance Unit of check Land Under Inspection Agriculture parcel LUI = Land Under Inspection (the area represented by the reference parcel) RFV data Screening of RFVs data Data structure/tree Logical Consistency/Data formats Completeness OrthoImagery availability / CID Portal Orthoimage quality RFVs data availability RFVs data usability RFVs data reliability 13

14 Comparison of two RFVs Country Country 1 Country 2 Number of RPs in scope Number of RPs subject to RFV 9 99 Number of field observations Approx. number of field observations per RP Number of reasons for triggering RFV Not possible to confirm presence/absence of multi-parcel from satellite imagery 2. Not possible to clearly determine land classification from satellite imagery 1. Double declaration 2. Impossible to determine field boundary and crop 3. Impossible to determine the crop 4. Impossible to determine the field boundary 5. Impossible to determine the GAEC Example of RFV data: 14

15 RP=2/051/097/46 15

16 16

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18 Example nr.2: RP=1/039/136/20 Example: RP=1/039/136/20 18

19 ? Conclusions & Comments Is the RFV data collected during CwRS enough for LPIS QA? It s useful, but data might not be not always enough (deliverables are not complete, not consistent) To become a authentic data, the instructions and workflow how to collect and how to deliver RFV data must be defined 19

20 Thank you for your attention! For technical issues regarding the LPIS quality assessment: Wim Devos - overall coordination Piotr Wojda - data exchange Pavel Milenov - ETS inspection procedure Slavko Lemajic - Field inspection procedure Romuald Franielczyk - Mediawiki - LPIS QA portal 20