Progress Meeting M3A Presentation of TD3

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Progress Meeting M3APresentation of TD3
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Selection Procedure
Prototype selection is based on a 3-step procedure
TD2
Qualitative pre-screening of algorithms
Quantitative evaluation of algorithms
Final ranking and prototype selection
Selected prototype
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Selection Criteria
8 classes of parameters are considered

• Scientific Background and Technical Soundness
• The selected algorithms should be based on a
  solid theoretical background that guarantees the
  accuracy of its results also at an operational
  level. The guidelines for rating are as follows
• The methodology is solid
• The methodology is technical convincing
• The methodology is at the state-of-the-art
• The methodology is published in high quality
  journals
• The methodology is included in several other
  scientific publications or project technical
  reports.

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Selection Criteria

• Robustness and Generality
• The method is suitable to be used with different
  kind of images
• The method shows high performances on different
  images and different test areas
• There are software implementations or examples
  for the implementation available
• The algorithm can be used in combination with
  other methodologies.

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Selection Criteria

• Novelty
• In order to get a high score, an algorithm should
  have been published or reported for the first
  time relatively recently in the literature. The
  guidelines for rating the novelty are
• The publications are after 2003 and introduce a
  novel, convincing and adequately tested solution
  to an existing problem
• The publications in remote sensing are after
  1998
• The method is not implemented in commercial SW
  packages.

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Selection Criteria

• Operational Requirements
• Operational requirements are evaluated in terms
  of computational complexity, time effort, cost
  etc. The guidelines for rating of operational
  perspectives are as follows
• The requested modifications to KIM architecture
  are few
• The algorithm works fast (e.g., near real time)
• The processing time scaling is likely to be
  linear with image size
• The hardware and disk-storage requirements are
  low.

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Selection Criteria

• Accuracy
• Both absolute and relative accuracy in all
  operative conditions will be evaluated. The
  guidelines for rating the accuracy are
• The algorithm matches the end-user requirements
  and can be optimized according to them
• The accuracy does not depend on the
  availability/amount of prior information.

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Selection Criteria

• Range of Applications
• The number and kinds of applications that an
  algorithm can address is evaluated
• The algorithm is suitable for a high number of
  application areas
• The algorithm has a high number of estimated
  final users for the application areas
• The algorithm has a high impact on the considered
  application areas.

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Selection Criteria

• Level of Automation
• From an operational point of view, it is
  preferable that an algorithm is able to run in a
  completely automatic way. The main guidelines for
  rating of the perspectives for automation are
• The number of parameters to be defined by the
  operator is low
• The physical meaning of parameters is clear
• The method is automatic
• Ground truth or prior information is not
  requested.

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Selection Criteria

• Specific end-users requirements
• From an operational point of view, capability of
  an algorithm to satisfy and meet different
  possible end-users requirements is an important
  parameter of evaluation. The main guidelines for
  driving this ranking are
• The algorithm is flexible in meeting different
  possible accuracy requirements
• The algorithm can be reasonably included in an
  operational procedure.

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Selection Procedure

• Step 1 Qualitative pre-screening of algorithms
• A pre-screening of the algorithms identified and
  described in TD2 is carried out in order to
  identify the most relevant methodologies with
  respect to the IIM-TS project objectives.
• The preliminary qualitative evaluation is driven
  from the same selection criteria used also in the
  next quantitative steps. In this step a high
  level analysis of these criteria is conducted in
  order to identify techniques that clearly cannot
  reach a satisfactory ranking on several
  categories of parameters.
• These techniques are discarded and not further
  considered in the next steps.

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Pre-screening of algorithms
Binary Change Detection
Multispectral data
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Pre-screening of algorithms
Binary Change Detection
SAR and Polarimetric SAR data
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Pre-screening of algorithms
Binary Change Detection
Multisensor data
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Pre-screening of algorithms
Multiclass Change Detection
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Pre-screening of algorithms
Shape Change Detection
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Pre-screening of algorithms
Trend Analysis of Temporal Series of Images
Pixel-based techniques
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Pre-screening of algorithms
Trend Analysis of Temporal Series of Images
Context-based techniques
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Pre-screening of algorithms
Pre-processing Multispectral Data
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Pre-screening of algorithms
Pre-processing SAR Data
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Pre-screening of algorithms
Pre-processing SAR Data
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Pre-screening of algorithms
Pre-processing Multisensor Data
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Selection Procedure

• Step 2 Quantitative evaluation of algorithms
• Algorithms that pass the pre-screening in step 1
  are analyzed in greater detail with a
  quantitative evaluation.
• This analysis is based on different parameters
  (scientific and technical analysis, possible
  impacts on the application and end-users, etc).
• For each algorithm (or cluster of algorithms) a
  method sheet is filled in, which reports details
  of the algorithm and individual scores for each
  parameter considered.

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Method Sheets Organization
Algorithm characteristics
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Method Sheets Organization
Evaluation
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Method Sheets Organization
Evaluation
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Selection Procedure

• Step 3 Final ranking and prototype selection
• According to an analysis of methods sheets a
  final score is given to each algorithm and
  method.
• This value is used for ranking algorithms
  according to their relevance with respect to
  IIM-TS objectives
• The algorithms to be prototyped are identified on
  the basis of the score and of a final discussion
  of the ranking.

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Total Score Computation

• Total score computation
• 1 point is given to each considered class of
  parameters for each positive answer in the
  corresponding category of the method sheet. Then
  the category score is normalized.
• Few points are assigned to each method according
  to the number of citations per year of the
  algorithms in scientific papers (or in technical
  reports) following this table

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Total Score Computation

• The score achieved for each single class is
  properly weighted in order to take into account
  its relevance with respect to the goals of the
  project. The following equation is used

The final score indicates the relevance of the
method with respect to the prototyping procedure
within IIM-TS project.
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Total Score Computation
wn (n 1,9) is the weight assigned to the n-th
category of criteria, and represents the relative
relevance of the considered criterion with
respect to the others
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Table of Ranking
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Table of Ranking
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Design of the Architecture

• The selection of the prototype algorithms among
  those with the highest scores in the ranking
  should be finalized taking into account the
  possible synergy between different techniques.
• The final selection should be also based on an
  adequate balancing among techniques belonging to
  the different classes.

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Design of the Architecture
Raw SAR images
Raw optical images
Focusing Geometric corrections Radiometric
corrections Radiometric normalization Mutitemporal
filtering Mosaiking Segmentation Time varying
segmentation
Registration Ortho-rectification Mosaiking Radiome
tric corrections Cloud detection Topographic
corrections Pan-sharpening Image
filtering Feature extraction
Pre-processing
Pre-processing
Post-classification Comparison Direct-Multidate
Classification Compound Classification Unsupervise
d approaches Multisensor techniques
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Design of the Architecture

• Pre-processing chain for multispectral images
  (geometric corrections and radiometric
  corrections)
• Pre-processing chain for SAR data (geometric
  corrections and radiometric corrections)
• Binary change detection
• Set of measures for image comparison (difference,
  magnitude of the difference vector, ratio,
  log-ratio, KL, similarity measures)
• Image splitting
• Bayesian framework for the analysis of the
  results of the comparison (minim error and cost
  decision rules, Gaussian model, Generalize
  Gaussian model (?), MRF context-sensitive
  decision, manual or automatic initialization?)

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Design of the Architecture

• Multiclass change detection
• Unsupervised method based on autochange algorithm
• Supervised methods based on MDC and PCC (need for
  a distribution-free classification module)
• Rule based multisensor classifier
• Trend analysis of time series
• Spatio-temporal clustering (data mining)
• Tools for FT and WT
• Hot spot monitoring via GIS fusion
• Shape change detection measure