Automatic land-cover map production of agricultural … land-cover map production of agricultural...

Automatic land-cover map production ofagricultural areas using supervised

classification of SPOT4(Take5) and Landsat-8image time series.

Jordi Inglada

2014/11/18

SPOT4/Take5 User Workshop – 2014/11/18 – 1

Outline

1 Sentinel-2 Agriculture

2 Crop type

3 Algorithm benchmarking

4 Comparison criteria

5 Proposed algorithms


Sentinel-2 Agriculture

Outline1 Sentinel-2 Agriculture

2 Crop type

3 Algorithm benchmarkingRationaleInput dataClassifiersFeaturesMetrics for the evaluationConclusions of the explorationFiltering rather than segmentation





The Sentinel-2 Agriculture project

I ESA fundedI Aims at showing on a large scale, the capabilities of Sentinel-2

mission for agriculture monitoringI by providing an open source processing softwareI to generate, among other products, crop type maps.

I The project consortium is made of the following partners :I Université Catholique de LouvainI CESBIOI C-SI C-S România



The products

I Cloud free compositesI Dynamic crop maskI Crop typeI Vegetation status

I LAI, mono and multi-temporal approaches



Presentations in this workshop

I M. Kadiri : Definition, test and evaluation of a monthly compositeproduct for Sentinel-2, based on SPOT4 (Take5)

I J. Inglada : Automatic land-cover map production of agriculturalareas using supervised classification of SPOT4(Take5) andLandsat8 image time series. Algorithm comparison over 12 sitesfor Sentinel-2 Agriculture Project.

I S. Valero : Real time production of a crop mask using high spatialand temporal resolution time series.

I D. Morin : Cartography of irrigated crops and estimation ofbiophysical variables with high temporal and spatial resolutionimages



The validation

I 2014 : Benchmarking on Spot4(Take5), Landsat-8 and Rapid-Eyedata

I 12 sites over the world

I 2015 : Development of the operational processing chainI 2016 : Production with real Sentinel-2 data

I 3 full countriesI size of France,I 2 African countries

I 5 sites (300 km × 300 km)



The validation


I 12 sites over the worldI 2015 : Development of the operational processing chain

I 2016 : Production with real Sentinel-2 dataI 3 full countries

I size of France,I 2 African countries

I 5 sites (300 km × 300 km)



The validation


I 12 sites over the worldI 2015 : Development of the operational processing chainI 2016 : Production with real Sentinel-2 data

I 3 full countriesI size of France,I 2 African countries

I 5 sites (300 km × 300 km)


Crop type


2 Crop type





Crop type

Crop type product description

I Map of the main crop types or crop groups in a given region.I those covering a minimum area of 10% of the annual cropland and

for which the cumulative area reaches more than 75% of the annualcropland in the region.

I A maximum of 5 crop types will be considered per site.I The 4 key crops in the GEO Global Agricultural Monitoring

(GEOGLAM) initiative and the Agricultural Market InformationSystem (AMIS) will be prioritized whenever possible :

I wheat, maize, rice and soybean.I The distinction between rain-fed and irrigated crops will also be

included as an additional attribute.


Crop type






I wheat, maize, rice and soybean.

I The distinction between rain-fed and irrigated crops will also beincluded as an additional attribute.


Crop type






I wheat, maize, rice and soybean.I The distinction between rain-fed and irrigated crops will also be

included as an additional attribute.


Crop type


I The delivery time of the first product is set-up to 2 weeks after thefirst half of the season.

I The last (and most accurate) crop type map will be delivered 2weeks after the end of the season.

I The crop type maps will be provided on a regular grid at 10 mresolution.


Algorithm benchmarking


2 Crop type





Algorithm benchmarking Rationale

Goals of benchmarking

I Compare 5 algorithms for the crop type productionI Choice of the algorithms to be compared based on :

I literature,I best practices.



Work done so far

I Our proposition of algorithms for the benchmark is not only basedon the literature.

I Best practices for multi-temporal high resolution optical imagerydon’t exist.

I Therefore, implementation of a large number of classifiers hasbeen carried out.

I An evaluation on real data has been performed in order to gaininsight on what best practices should be.



Exploration of strategies


Algorithm benchmarking Input data

3 different sites


Algorithm benchmarking Input data

L2 and gap-fillingI Comparisons between raw L2 data, raw L2 data plus masks and

gap-filledI Allows regular temporal resampling for inter-annual supervised

learningI 2 interpolation methods have been implemented : linear and cubic

spline

0

0.2

0.4

0.6

0.8

1

0 2 4 6 8 10 12 14 16 18

Invalid datesNDVI

LinearSpline


Algorithm benchmarking Classifiers

Classifiers

StatisticalI Kernel-based SVM

I LinearI RBF

I Neural NetworksI Multi-layer perceptron

Decision treesI Standard DTI DT committees

I RFI GBT


Algorithm benchmarking Features

Tested image features

I TOAI NDVII NDVI-like

combinationsI Landsat

Tasseled Cap

I Morocco

I MiPy


Algorithm benchmarking Features

Feature selection

I Forward and backwardI Feature selection depends on sites and method

1 2 3 4 5 6 7NDVI Vert-Mir2 Brillance Bleu-Vert Bleu-Rouge Vert-Mir Brightness

8 9 10 11 12 13 14Mir Vert-Rouge Bleu-Mir2 Pir Bleu-Mir Brillance L Vert15 16 17 18 19 20 21

Rouge-Mir2 Greenness Wetness Mir-Mir2 Pir-Mir Rouge-Mir Bleu-Pir22 23 24 25 26

Bleu Mir2 Pir-Mir2 Rouge Vert-PirResults of the forward algorithm on Midi-Pyrénées


Algorithm benchmarking Metrics for the evaluation

Quantitative indices

κ / OAI κ = Po−Pe

1−Pe

I where Po = 1n

∑ri=1 nii is the agreement

I and Pe = 1n2

∑ri=1 ni.n.i

I OA =∑r

i=1 nii∑ri=1

∑rj=1 nij

FScore1I FScore = 2×UA×PA

UA+PAI Harmonic mean between UA and PA

Computation time


Algorithm benchmarking Conclusions of the exploration

Conclusions of the exploration

I Parameter ranges for all algorithms OK

I Neural networks are rejectedI 2 types of classifiers : RF and RBF-SVM

I although RF are better than SVMI No need for fancy combinations of bands

I TOCR + NDVI + NDWI + BrightnessI Stability across 3 sites (robustness)I Need for gap-filling, but linear is enough




I Parameter ranges for all algorithms OKI Neural networks are rejectedI 2 types of classifiers : RF and RBF-SVM

I although RF are better than SVM

I No need for fancy combinations of bandsI TOCR + NDVI + NDWI + Brightness

I Stability across 3 sites (robustness)I Need for gap-filling, but linear is enough






I TOCR + NDVI + NDWI + Brightness

I Stability across 3 sites (robustness)I Need for gap-filling, but linear is enough






I TOCR + NDVI + NDWI + BrightnessI Stability across 3 sites (robustness)

I Need for gap-filling, but linear is enough






I TOCR + NDVI + NDWI + BrightnessI Stability across 3 sites (robustness)I Need for gap-filling, but linear is enough


Algorithm benchmarking Filtering rather than segmentation

Segmentation



Segmentation

Fields overlayed on the image 3-date multi-temporal segmentation



Filtering

I A crisp segmentation may introduce errors impossible to correctI no segmentation

I However, introducing an edge-preserving regularization of theimages may help in achieving less noisy classification results

I The filtering step (before thresholding) of the Mean-shiftsegmentation algorithm is a fast, streamable, multi-channel,edge-preserving regularization.


Comparison criteria


2 Crop type





Comparison criteria

Quality criteria and thresholds

I Overall Accuracy + min per class FScoreI Computation time (secondary criterion)

I Robustness : decrease in OAI wrt data gapsI wrt errors in reference dataI only on sites with enough EO data and ground truth quality

I The relative weight of these criteria on the final ranking is not yetdefined

I Acceptance thresholds :I OA > 50%I FScore of the main class > 65%I (if reference and EO data are acceptable)


Comparison criteria

Quality criteria and thresholds

I Overall Accuracy + min per class FScoreI Computation time (secondary criterion)I Robustness : decrease in OA

I wrt data gapsI wrt errors in reference dataI only on sites with enough EO data and ground truth quality

I The relative weight of these criteria on the final ranking is not yetdefined

I Acceptance thresholds :I OA > 50%I FScore of the main class > 65%I (if reference and EO data are acceptable)


Proposed algorithms


2 Crop type





Proposed algorithms

Proposed algorithms forbenchmarking

I Input dataI All algorithms will work on linearly gap-filled L2 data.I Features are : TOCRefl, NDVI, NDWI, Brightness

I Algorithms1 Random Forest classifier2 RBF-SVM classifier3 Best classifier with Mean-shift filtering4 Best classifier with temporal regular resampling5 Dempster-Shafer fusion of the previous approaches

I Benchmarking on 12 sites just started


Proposed algorithms

Q&A

Land cover map on the East and West Sudmipy tracks. κ ≈ 0.9.


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Automatic land-cover map production of agricultural … land-cover map production of agricultural...

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