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Progress Meeting M3A Presentation of TD3. 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. Selection Criteria.

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selection procedure
Selection Procedure

Prototype selection is based on a 3-step procedure


Qualitative pre-screening

of algorithms

Quantitative evaluation

of algorithms

Final ranking and

prototype selection

Selected prototype

selection criteria
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.
selection criteria1
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.
selection criteria2
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.
selection criteria3
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.
selection criteria4
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.
selection criteria5
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.
selection criteria6
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.
selection criteria7
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.
selection procedure1
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 evaluationis 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.
pre screening of algorithms
Pre-screening of algorithms

Binary Change Detection

Multispectral data

pre screening of algorithms1
Pre-screening of algorithms

Binary Change Detection

SAR and Polarimetric SAR data

pre screening of algorithms2
Pre-screening of algorithms

Binary Change Detection

Multisensor data

pre screening of algorithms3
Pre-screening of algorithms

Multiclass Change Detection

pre screening of algorithms4
Pre-screening of algorithms

Shape Change Detection

pre screening of algorithms5
Pre-screening of algorithms

Trend Analysis of Temporal Series of Images

Pixel-based techniques

pre screening of algorithms6
Pre-screening of algorithms

Trend Analysis of Temporal Series of Images

Context-based techniques

pre screening of algorithms7
Pre-screening of algorithms

Pre-processing Multispectral Data

pre screening of algorithms8
Pre-screening of algorithms

Pre-processing SAR Data

pre screening of algorithms9
Pre-screening of algorithms

Pre-processing SAR Data

pre screening of algorithms10
Pre-screening of algorithms

Pre-processing Multisensor Data

selection procedure2
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.
method sheets organization
Method Sheets Organization

Algorithm characteristics

selection procedure3
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.
total score computation
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:
total score computation1
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.

total score computation2
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:

design of the architecture
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.
design of the architecture1



Information theoretically similatity

Measures (KL divergence, etc.)

Polarimetric change indeces

(correlation, etc.)

Shape Measures


Thresholding based on

the Bayes decision theory

Context-based approaches

Multiscale approaches

Multimodal approaches

Multivatiarte Alteration Detection

Neural networks

Satellite linear-based index

Fourier and Wavelet Analysis

Spatio-temporal clustering

Statistical analysis of areas of interest

(by GIS or object analysis)

Shape Change Detection

Multiclass Change Detection

Binary Change Detection

Trend Analysis

Design of the Architecture

Raw SAR images

Raw optical images


Geometric corrections

Radiometric corrections

Radiometric normalization

Mutitemporal filtering

Mosaiking Segmentation

Time varying segmentation




Radiometric corrections

Cloud detection

Topographic corrections


Image filtering

Feature extraction



Post-classification Comparison

Direct-Multidate Classification

Compound Classification

Unsupervised approaches

Multisensor techniques

design of the architecture2
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?)
design of the architecture3
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