Roman_Gonzalez.pptx

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Prof. Dr. Mihai Datcu

SATELLITE IMAGE ARTIFACTS DETECTION BASED ON

COMPLEXITY DISTORTION THEORY

Avid ROMAN GONZALEZ Mihai DATCU

Avid Roman Gonzalez

2011 IEEE International Geoscience and Remote Sensing Symposium26/07/2011

Vancouver, Canada

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OUTLINE

The Artifacts, problematic. Rate-Distortion Function Kolmogorov Complexity Kolmogorov’s Structure Function Experiments and Results Conclusions

Avid Roman Gonzalez

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ARTIFACTS :

The artifacts are artificial structures that represent a structured perturbation of the signal. Therefore, these artifacts induce errors in the indexation of the images.

Avid Roman Gonzalez 3/47

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ARTIFACTS KINDS :

Aliasing Strips

Saturation

. . .

Blocking


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DATA CLEANING:

Data cleaning, or data cleansing or scrubbing

- Detecting and removing errors and inconsistencies from data in order to improve the quality of data .

Data quality problems are present in single data collections, such as files and databases, e.g.:

- Due to misspellings during data entry.- Missing information or other invalid data.


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Is to predict or determine the existence of defects, to model it, and then design a method to detect and correct them. For example we have the lines correction methods presented by [Hyung-Sup Jung 2009].

Specific Artifacts.

CLASICAL APPROACH :

OUR APPROACH : Using data compression techniques to implement

a method more generic PARAMETER FREE regardless the type or model of artifact.

Hyung-Sup Jung, Joong-Sun Won, Myung-Ho Kang, and Yong-Woong Lee, “Detection and Restoration of Defective Lines in the SPOT 4 SWIR Band”, Transaction on Image Processing, 2009.

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The Rate-Distortion (RD) Function is given by the minimum value of mutual information between source and receiver under some distortion restrictions.

The RD function shows how much compression (lossy compression) can be used without loss of distortion preset value.

RATE-DISTORTION FUNCTION:

),(min)( QpIDRDQQ


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For the artifacts detection, we propose to use the RD function obtained by compression of the image with different compression factors and examine how an artifact can have a high degree of regularity or irregularity for compression.

JPEG Lossy

Compression

Image cf 1Image cf 2Image cf 2

.

.

.Image cf n D

ecom

pre

ssio

n

+-

Features Vector

(compression errors)

Classification

Image

Images with different compression factor (cf)


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Kolmogorov Complexity


String 1 000000000000000

String 21010001010111011

K(x) is a non calculable function

is the length of a shortest program to compute x on a universal Turing machine

qxKxQq

min

xK

15 x (Write 0)

Write 1010001010111011

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Kolmogorov’s Structure Function


An approximation of the RD curve using the Kolmogorov complexity theory could be the Kolmogorov Structure Function (KSF).

The relation between the individual data and its explanation (model) is expressed by Kolmogorov’s structure function.

The original Kolmogorov structure function for a data x is defined by:

)(,:logmin SKxSShS

x

Where:S is a contemplated model for x. α is a non-negative integer value bounding the complexity of the contemplated S.

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Kolmogorov’s Structure Function for Texture Discrimination

Avid Roman Gonzalez

To evaluate the behavior of the KSF for different textures, we use de Brodatz images databases. We show the textures used for this experiments.

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Prof. Dr. Mihai DatcuAvid Roman Gonzalez

We can observe that the KSF can discriminate more or less the different structure, the curve KSF has a similar shape for each texture group, but the level is different.

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Artifacts Detection Using Kolmogorov’s Structure Function Approach

Avid Roman Gonzalez

To detect artifacts using Kolmogorov Structure Function (KSF), the first step is to watch the behavior of the KSF curve for images with artifacts and images without artifact.

One aspect to consider is how to generate the candidates for the necessary space S. For this purpose, we have generated the candidates using 2 methods: Candidates generation by JPEG lossy compression and Candidates generation by genetic algorithm.

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KSF using jpeg lossy compression KSF using genetic algorithm

We can observe that the better discrimination is done when we generate the candidates for the space S using the JPEG lossy compression. Also using JPEG lossy compression the approximation to the Rate-distortion analysis is better.

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We use the jpeg lossy compression for generate candidates and to draw the Kolmogorov Structure Function for each patch of a satellite image and try to detect the artifacts. For this experiments we use an image with aliasing introduces manually.

Aliasing detection in city environmental using KSF and candidate generation with JPEG lossy compression

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CONCLUSIONS

The Kolmogorov structure function represents the relationship between an element or data with its model, structure, or explanation.

In this work, we have used the Kolmogorov structure function as a approximation of rate-distortion function using Kolmogorov complexity theory and the complexity-distortion theory, so we can examine the complexity of the images to be analyzed, this complexity would be related to the presence or absence of artifacts.


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CONCLUSIONS

The generation of candidates for to calculation the Kolmogorov structure function is an important step, in this work was done experiments using 2 methods, generation of candidates by jpeg lossy compression and candidate generation using genetic algorithms, we obtain better results using lossy jpeg compression.


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THANK YOU FOR YOUR ATTENTION

[email protected]://www.avid-romangonzalez.com

Avid Roman Gonzalez

Roman_Gonzalez.pptx

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