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POLInSAR Workshop 2003

Session Summary: Land-Agriculture Application

    Chair/s:  J.S. Lee/ S. Quegan

    Session papers:
    All papers in this session dealt with classification and segmentation techniques using polarimetric SAR data.
    · A common data set used in several papers is the AIRSAR P-, L-, C-Band polarimetric SAR data of Flevoland, The Netherlands, but the EMISAR multitemporal C and L-band dataset from Denmark is a very important data holding.
    · A new classification scheme (Dr. Hoekman), that transforms the covariance matrix into 9 intensity components, produces results with high classification rate as claimed by the authors.  This algorithm uses segmented data and a model for class statistics (not just the Wishart distribution, which is suitable for field statistics).  The results were obtained on the particular Flevoland test site and further verification of the high classification rate and robustness is required on other test sites.
    · Three principal classification approaches were used: hierarchical decision trees (T. Le Toan, S. Quegan and H. Skriver), feature-based ML and the Wishart classifier (J.S. Lee, E. Pottier and L. Ferro-Famil), the latter two perhaps linked to an ISODATA algorithm (as done in the paper by Quegan et al.).  The Flevoland data indicated that both techniques have their merits, depending on the crop conditions and hence period in the growing season.
    · Pixel-based, (GIS) area-based and (automatic) segmentation-based methods were used. Pixel-based methods give good results when state-of-the-art filters are used. The best results used area-based methods; a number of methods are available to segment polarimetric images.
    · Partial polarimetric data (dual polarizations) can be used to separate winter from spring crops, although the best separation used the HH-VV correlation coefficient.
    · A POLSAR analysis and classification software package: POLSAR PRO, contains very up-to-date algorithms.  This package is developed by University of Rennes under the supervision of E. Pottier.  This software package is free.
    · The paper by X. Blaes was cancelled.

    Roundtable Discussion
    1. Is fully polarimetric data necessary? Which frequency is the best for land-agriculture applications?

    Yes, fully polarimetric data is necessary for land-agriculture applications.  There was some discussion about the relative merits of L-band (Lee’s view) or C-band (Quegan et al.’s opinion).  However, L-band is preferred also for other applications such as sea ice and forest (Lee’s opinion).

    2. How to verify the high classification rate of Dr. Hoekman’s classification algorithm and other algorithms?

    Recommend establishing common data sets for evaluating classification algorithms. Their purpose is to test robustness (transferability) of algorithms and to compare algorithms.
    · The Flevoland data used by many papers in this session has some merits for this purpose, but has rather special properties and cannot be considered typical even of European agriculture. Data sets from other sites are needed.
    · Multi-temporal data are needed to evaluate the classification capabilities at various crop growth stages.
    · Training sets together with well-defined and trustworthy ground data need to be pre-defined for classification evaluation and for fair comparisons of algorithms (Quegan’s comment: this depends on the training strategy, which might be algorithm specific).
    · The rules for comparison need to be clearly stated, e.g., no tweaking of parameters, full processing chain specified and transferable, training and learning process fully specified, etc...
    · Computational efficiency is another important comparison for operational application.
    As well as providing procedures for evaluating and comparing algorithms, effort must go into understanding WHY the best algorithms are the best.

    3. Besides crop classification, what can additional crop parameters (height, biomass, plant structure, etc.) be reliably extracted using POLSAR and Pol-INSAR data?

    · POLSAR can be used winter crop and spring crop separation, and broad leaf (random) versus cereal (structured) crop types.  There is some evidence that partially polarimetric SAR can be estimate biomass through the HV/HH and/or VV/HH ratios; this needs to be tested with ASAR.
    · It may be possible to extract crop height using POL-INSAR technique.  More research needs to be done in this area, and C-band is probably a better frequency to use than L-band.

    4. Does POLSAR supervised classification performs better than unsupervised classification? What are their merits?

    Supervised Classification:
    · Advantages:
    · In general, supervised classification has better classification rate than unsupervised classification.
    · Positive crop identification.
    · Classes of close scattering properties may be separated, but they could be grouped into one class in the unsupervised classification.
    · Disadvantage: Needs ground truth collection to establish training sets.

    Unsupervised Classification:
    · Advantages:
    · Automated techniques easy to apply.  No ground truth collection is needed.
    · Scattering characteristics (even bounce, odd bounce, volume scattering, entropy, anisotropy, etc) of each class may provide hints for crop identification.
    · Distinct classes not corresponding to predetermined crop types are preserved.
    · Disadvantage: No positive crop identification.  Classes separated using supervised techniques may be grouped into one.

    5. What can ENVISAT ASAR with C-band dual polarizations achieve in land-agriculture applications?

    · Biomass (HV/HH and VV/HH).
    · Rice crop monitoring.
    · Separation of cereal versus root crop (HH/VV).


    Crop classification with multitemporal polarimetric SAR data
    Professor Shaun Quegan

    Assessing the benefit of SAR Polarimetry for Land Cover Classification
    Alex Rodrigues

    A new polarimetric classification approach evaluated for agricultural crops
    Dr. Dirk Hoekman

    Polarimetric indices for crop monitoring based on model simulations and satellite observations
    Xavier Blaes

    A comparison of statistical segmentation techniques for multifrequency polarimetric SAR: region growing versus simulated annealing
    Tiziana Macrì Pellizzeri

    Model-based segmentation techniques for multifrequency polarimetric SAR
    Prof. Pierfrancesco Lombardo

    Statistical Segmentation of Polarimetric SAR Data
    Dr. Laurent Ferro-Famil


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