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Table Of Integrals Series And Products

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

IEEE Transactions on Pattern Analysis and Machine Intelligence

Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes characteristics about the size, orientation, and contrast of the spatial structures present in the image. Then, the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines (SVMs) using the available spectral information and the extracted spatial information. Spatial postprocessing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple-classifier (MC) system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral-spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.

/pdf/advances-in-spectral-spatial-classification-of-hyperspectral-vlt2ik4ef1.pdf

Advances in Spectral-Spatial Classification of Hyperspectral Images

Dempster-Shafer theory offers an alternative to traditional probabilistic theory for the mathematical representation of uncertainty. The significant innovation of this framework is that it allows for the allocation of a probability mass to sets or intervals. Dempster-Shafer theory does not require an assumption regarding the probability of the individual constituents of the set or interval. This is a potentially valuable tool for the evaluation of risk and reliability in engineering applications when it is not possible to obtain a precise measurement from experiments, or when knowledge is obtained from expert elicitation. An important aspect of this theory is the combination of evidence obtained from multiple sources and the modeling of conflict between them. This report surveys a number of possible combination rules for Dempster-Shafer structures and provides examples of the implementation of these rules for discrete and interval-valued data.

/pdf/combination-of-evidence-in-dempster-shafer-theory-244gn8j3se.pdf

Combination of Evidence in Dempster-Shafer Theory

Change detection methods often use denoised data because the original speckle noise has a strong influence on the detection results. The effect of using different data sources (different equivalent number of looks, original data, denoised data) and different threshold methods are studied based on four kinds of generalized likelihood ratio test approaches. NL-SAR [1] denoised data and the corresponding spatially varying equivalent number of looks are taken into account in the detection procedure. The bi-temporal experimental results on simulated data, realistic synthetic Sentinel-1 SAR data show the improvement of using equivalent number of looks of denoised data and corresponding adaptive thresholds for change detection in urban areas.

Urban area change detection based on generalized likelihood ratio test

This chapter is dedicated to very-high-resolution (VHR) SAR imagery, including interferometric applications. First, the principles of SAR data acquisition are presented as well as the different types of configurations. The widely adopted Gaussian complex model of fully developed speckle is described as well as more advanced statistical models for VHR SAR data that account for textures. The following two parts are devoted to SAR image estimation and to image denoising within two different frameworks. First, Markovian modeling is introduced and the associated optimization approaches are presented, including graph-cut-based optimization. The second framework is the patch-based non-local modeling of SAR complex data. Both frameworks are adapted to SAR images through the use of statistical models specific to SAR imagery. Their applications to amplitude data, interferometry, and fusion with optical data are illustrated. A special focus is given to phase unwrapping applied to single- and multi-channel interferometry, showing the usefulness of local and global contextual models.

Very-High-Resolution and Interferometric SAR: Markovian and Patch-Based Non-local Mathematical Models

Permanent scatterers (PS) approach allows the identification of radar targets not affected by decorrelation noise then suitable for reliable SAR interferometric measurements. This paper introduces a new technique allowing the selection of stable scatterers based on adaptative SVA (Spatially Variant Apodization) filtering. Indeed, SVA filters identify SAR pixels with strong reflectivity (maximum of the mainlobe) over a long temporal series of interferometric SAR images which is the main feature of Permanent Scatterers pixels. A comparison between PS candidates pixels and selected SVA pixels is discussed in this study.

/pdf/insar-permanent-scatterers-selection-using-sar-sva-filtering-1pjgpcuce7.pdf

InSAR permanent scatterers selection using SAR SVA filtering

—In this paper, we propose a generic multi-temporal SAR despeckling method to extend any single-image speckle reduction algorithm to multi-temporal stacks. Our method, RAtio-BAsed multi-temporal SAR despeckling (RABASAR), is based on ratios and fully exploits a " super-image " (i.e. temporal mean) in the process. The proposed approach can be divided into three steps: 1) calculation of the " super-image " through temporal averaging; 2) denoising the ratio images formed through dividing the noisy images by the " super-image " ; 3) computing denoised images by multiplying the denoised ratio images with the " super-image ". Thanks to the spatial stationarity improvement in the ratio images, denoising these ratio images with a speckle-reduction method is more effective than denoising the original multi-temporal stack. The data volume to be processed is also reduced compared to other methods through the use of the " super-image ". The comparison with several state-of-the-art reference methods shows numerically (peak signal-noise-ratio, structure similarity index) and visually better results both on simulated and real SAR stacks. The proposed ratio-based denoising framework successfully extends single-image SAR denoising methods in order to exploit the temporal information of a time series.

Ratio-based multi-temporal SAR images denoising

Synthetic aperture radar (SAR) images are widely used for Earth observation to complement optical imaging. By combining information on the polarization and the phase shift of the radar echos, SAR images offer high sensitivity to the geometry and materials that compose a scene. This information richness comes with a drawback inherent to all coherent imaging modalities: a strong signal-dependent noise called “speckle.” This paper addresses the mathematical issues of performing speckle reduction in a transformed domain: the matrix-log domain. Rather than directly estimating noiseless covariance matrices, recasting the denoising problem in terms of the matrix-log of the covariance matrices stabilizes noise fluctuations and makes it possible to apply off-the-shelf denoising algorithms. We refine the method MuLoG by replacing heuristic procedures with exact expressions and improving the estimation strategy. This corrects a bias of the original method and should facilitate and encourage the adaptation of general-purpose processing methods to SAR imaging. 

/pdf/speckle-reduction-in-matrix-log-domain-for-synthetic-2iydqbhg.pdf

Florence Tupin

Papers

Urban area change detection based on generalized likelihood ratio test

Very-High-Resolution and Interferometric SAR: Markovian and Patch-Based Non-local Mathematical Models

InSAR permanent scatterers selection using SAR SVA filtering

Ratio-based multi-temporal SAR images denoising

Speckle Reduction in Matrix-Log Domain for Synthetic Aperture Radar Imaging