Edoardo Arnoldi Provincia autonoma di Trento - Italy TOULOUSE 24-25 june 2008 3RD GRISI CAPITALIZATION WORKSHOP 3RD GRISI CAPITALIZATION WORKSHOP The stakes of Geographical Information The stakes of Geographical Information for the interregional cooperation in Europe for the interregional cooperation in Europe Session 4: Session 4: The new applications of The new applications of Geomatics Geomatics for sustainable development of the rural areas for sustainable development of the rural areas
Transcript
Edoardo ArnoldiProvincia autonoma di Trento - Italy
TOULOUSE24-25 june 2008
3RD GRISI CAPITALIZATION WORKSHOP3RD GRISI CAPITALIZATION WORKSHOPThe stakes of Geographical Information The stakes of Geographical Information for the interregional cooperation in Europefor the interregional cooperation in Europe
Session 4:Session 4:
The new applications of The new applications of GeomaticsGeomatics
for sustainable development of the rural areas for sustainable development of the rural areas
The region I will talk about is Trentino in the North-East of Italy;
In this presentation the focus will be on the agricultural areas;
Need for higher resolution geographical information for management and control duties;
Need to relate the lands cultivated by each farm business (land registry) to the high resolution land use;
Very high fragmentation of land properties in Trentino;
Need for very-high resolution agricultural land use data (1-2 m), as for cadastral maps;
Need for frequent territorial data updates;
Common classification techniques (photointerpretation) too work-intensive and expensive. TOULOUSE
24-25 june 2008
ObjectivesObjectives
Test the modern remote-sensing technologies to:
automatically discriminate and recognize different types of agricultural land coverageswith a high geometrical resolution;
cut down on costs and times of land-use maps production for the whole Trentino area;
Define an automatic/semi-automatic analysis process of remote-sensed data able to:
provide thematic maps of areas under investigation avoiding the photo-interpretersindividual subjectivity;
allow a frequent update of coverage data (1-2 years max);
Integrate thematic maps with the PAT Information Systems;
Relate rural-areas geographical data to the other informations contained in the farm business file (electronic folder).
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RequirementsRequirements
Given the project goals, there is the need to dispose of thematic maps with the following requirements:
Thematic classes: main interest on vineyards, fruit orchards (mainly apple orchards), grass lawn and pasture areas;
Information of interest: maps are positioned in an appropriate geo-referenced system and aligned with the land/cadastral maps of the Autonomous Province of Trento;
Work area: at full development all the territory of the Province of Trento, but this study is limited to a representative sample (105 km2 on 6.208 km2);
Geometrical resolution: 1-1.5 m (analogous to the one of PAT cadastral maps).
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IntroductionIntroductionIntroduction
One of the main remote-sensing applications regards the extraction of geographical information from remote-sensed data supporting the management and monitoring activities of agricultural areas;
The recent availability of high geometrical resolution satellitar images (i.e. Quickbird and Ikonos) has enabled many precision activities related to environmental monitoring and territorial control, with a relevant impact on agricultural areas;
Automated classification is difficult with high spatial resolution images;
The high spatial resolution, in fact, affects the spectral one making the pixel-by-pixel thematic soil mapping very complex (as it is based only on the pixel spectral signature);
There is the need to merge spectral features with geometric and textural ones, in order to recognize different crops which are characterized by well-distinguishable structures (i.e. vineyards and apple orchards). Those features have to be processed with an automatic classification technique, able to cope with the high system complexity;
In this work an advanced analysis and classification system is being proposed, which is able to get spectral, textural and spatial information from high-geometrical resolution images and to use it to obtain high-accuracy thematic maps of agricultural areas.
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Area under studyArea under studyArea under study
About 105 km2 in the South area of Trento.Good representation of the classifying problem. Main interesting classes are well represented:
vineyard;apple and fruit orchard;grass lawn and pasture;forest;waters;urban area.
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DataDataData
In order to better characterize the main intreresting information classes a multitemporal dataset consisting of two images has been chosen: the first one acquired in autumn (october 2005) and the second one in summer (july 2006);
In particular, Quickbird imagery has been used in order to satisfy the need to obtain a first thematic map with a geometric resolution higher than 1 m;
In order to train the classifier and to validate the land classification, ground truth data have been collected for each informative class condidered:
apple orchard, other fruit orchrd, forest, grass lawn/pasture, protected crops, arable crops, waters, urban area.
oct 2005
jul 2006
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Classification system architectureClassification system architectureClassification system architecture
The classification system setup is based on different processingsteps, as shown by the following scheme
1. Image pre-processing to limit geometric distorsions and embed images in a georeferenced geographic system;
2. Image analysis to extract a feature set able to model all the geographical data (spectral, geometric and textural) to properly feed the classification algorithm;
3. Classification step based on a Support Vector Machine(SVM) technique;
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4. Image post- processing dealing with the merging of classification maps together with existing cadastral maps, to limit classification noise.
Data pre-processingData preData pre--processingprocessing
correct topographical distorsions;
position remote-sensed images in a valid referencing scheme with the highest possible precision;
This process is made up of:
a) orthorectification: to neutralize the distorsions introduced by soil orography;b) pansharpening: the merging of the panchromatic band with the
multispectral ones in order to improve the spatial detail;c) alignment to cadastral maps. TOULOUSE
extract the features which best allow to discriminate the different land use classes from remote-sensed images
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SVM classifierSVM classifierSVM classifier
In this study a classification method based on Support Vector Machine (SVM) has been used;
This kind of classifier achieves a higher level of classification accuracy than other methods and can be used with small training datasets.
Class Training Test Validation
other fruit orchard
3970 6514 11141
arable crops 826 2914 9527
apple orchard 16992 57304 146599
vineyard 11720 72210 162770
protected crops 507 1066 1633
grass lawn /pasture
7058 23668 49517
waters 2531 4222 7883
forest 8145 26189 50205
urban - rocks 10126 25778 33350
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Post-processingPostPost--processingprocessing
A further processing phase has been performed over the pixel-based map obtained with the SVM classifier;The proposed post-processing algorithm merges the thematic map with cadastral data, assigning each cadastral parcel the dominant class. If more than one relevant class (the relevance threshold tuned by the user) is detected in one single parcel, this is therefore classified as mixed and related to each relevant class detected;This phase gives:
punctual noise reduction affecting some of the classified areas;a global increase in map quality (quantitative and qualitative).
Confusion matrix computed from the validation set;Ground truth samples label extended to all homogeneous pixels (belonging to the same cadastral parcel)
Vineyards and apple orchards classified with an accuracy higher than 95%, whereas critical classes are “arable crops” and “other fruit orchards”
The high accuracyobtained with thisquantitative analysis isconfirmed at visual examination of the thematic mapsobtained.
The visual analysis, conducted by technicians of the province of Trento, reveals the good adherence of the random sample quantitative analysis to the whole thematicmap accuracy.
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ConclusionsConclusionsConclusions
The outcomes of this work met the targets for the following reasons:Overall kappa accuracy coefficient, referrered to cadastral parcels, > 0.98;Most interesting information classes for this study (apple orchard, vineyards, grass lawn and pasture) are featured with a very high accuracy, better than the one obtainable with traditional systems;Maps obtained with the automatic system can be integrated within the provincial GIS system with the aim of updating local geographical databases;It is possible to increase thematic detail without changing geometrical resolution only using high resolution spectral data (hyperspectral images);Remote sensing data, along with different automatic processing methods, can be used for other analisys outstanding in agricultural field:
yield forecasting;crops health monitoring;forecast crop harvest stage;traceability;……
Remote sensing data, processed or not, can be sinergically used in different application contexts (urbanistic, forestry, geology, environmental and civil protection, … );
Research topics:• Remote Sensing;• Automatic classification;• Estimation of biophysical parameters;• Data fusion (multispectral, SAR, Lidar, etc);• Applications of remote sensing (agriculture,
forestry, civil protection, analysis of urban areas, etc.).
Looking forwardLooking forwardLooking forward
Work in progress ……
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Classification of pasture/grass lawn areas using digital orthophotos
Number of samplesClass
Training Set Test Set
Grass 4885 12119
Forest 4310 11959
Urban area 635 290
Shadow 1334 1350
Ground truth samples:
Data Set description:• 50 cm spatial resolution;• 4 spectral channels (3 visible and 1near infrared)
Study Area
Maximum-likelihood Classification
Class Grass Rest Shadow User Acc.
Grass 11749 263 1 97.80%
Rest 370 11986 17 96.87%
Shadow 0 0 1332 100%
Producer Acc. 96.95% 97.85% 98.67%
Overall Accuracy 97.47%
Kappa Coefficient 0.9538
Texture feature based on occurrance matrix
Grass
Rest
Shadow
Support Vector Machines Classification
Class Grass Rest Shadow User Acc.
Grass 12028 356 0 97.13%
Rest 91 11888 0 99.24%
Shadow 0 5 1350 99.63%
Producer Acc. 99.25% 97.05% 100%
Overall Accuracy 98.24%
Kappa Coefficient 0.9680
Grass
Rest
Shadow
Texture feature based on occurrance matrix
Classification of agriculture fields with (mono-temporal) satellite Quickbird image
Other woody culture
Apple tree
Vineyard
Water
Urban area
Forest
Kappa coefficient of accuracy =
0,9866
Support Vector Machine classification using spectral, texture and geometric features
To Remote sensing Laboratory team Department of Information and Communication TechnologyUniversity of Trento – Italyprof. Lorenzo Bruzzonewww.dit.unitn.it/rslab/people.php
To the technicians and researchers of the Istituto agrario di San Michele all’Adige and of the Centre for Alpine Ecology- Trento
