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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

The variety of human settlements and their spatial characteristics can be captured by means of remote sensors on board of airborne and/or satellite platforms Urban remote sensing, however, is much more than visual display or even interpretation and analysis of these images Quantitative evaluation of physical properties such as land covers and material status, as well as the inference of further information such as land uses, are at the core of this research field This issue tries and shows some of the innovative researches going on and provides an overview of the state-of-the-art It also describes some of the challenges to be faced in the near future

Introduction to the Issue on Remote Sensing of Human Settlements: Status and Challenges

The problem of interferometric SAR image coregistartion is addressed. For classical images, the application of the Symmetric Phase Only Matching Filtering (SPOMF) to the Fourier-Mellin Invariant (FMI) descriptors allows an accurate and efficient registration of translated, rotated and scaled images. This paper discusses an extension of the technique to cover the FMI descriptors of two interferometric SAR images. This method is tested on two pairs of InSAR data in France and Tunisa. The results are compared with those of classical cross-correlation registration techniques.

Interferometric SAR image coregistration based on the Fourier-Mellin invariant descriptor

Speckle fluctuations seriously limit the interpretability of synthetic
aperture radar (SAR) images. Speckle reduction has thus been the subject of
numerous works spanning at least four decades. Techniques based on deep neural
networks have recently achieved a new level of performance in terms of SAR
image restoration quality. Beyond the design of suitable network architectures
or the selection of adequate loss functions, the construction of training sets
is of uttermost importance. So far, most approaches have considered a
supervised training strategy: the networks are trained to produce outputs as
close as possible to speckle-free reference images. Speckle-free images are
generally not available, which requires resorting to natural or optical images
or the selection of stable areas in long time series to circumvent the lack of
ground truth. Self-supervision, on the other hand, avoids the use of
speckle-free images. We introduce a self-supervised strategy based on the
separation of the real and imaginary parts of single-look complex SAR images,
called MERLIN (coMplex sElf-supeRvised despeckLINg), and show that it offers a
straightforward way to train all kinds of deep despeckling networks. Networks
trained with MERLIN take into account the spatial correlations due to the SAR
transfer function specific to a given sensor and imaging mode. By requiring
only a single image, and possibly exploiting large archives, MERLIN opens the
door to hassle-free as well as large-scale training of despeckling networks.
The code of the trained models is made freely available at
https://gitlab.telecom-paris.fr/RING/MERLIN.

As if by magic: self-supervised training of deep despeckling networks with MERLIN

Speckle noise strongly affects Synthetic Aperture Radar (SAR) images, causing strong intensity fluctuations that make them difficult to analyze. Although many speckle reduction algorithms have been proposed, how to effectively deal with the spatial correlations of speckle remains an open question, especially in the most recent deep learning approaches. This paper tries to address this problem. Existing approaches to tackle the speckle correlations are described. Then, a standard training strategy for deep learning is proposed. Two models are trained and the increased robustness brought by including a Total Variation (TV) term in the loss function is analyzed on Sentinel-1 images.

How to handle spatial correlations in SAR despeckling? Resampling strategies and deep learning approaches

In this paper, we present a novel unsupervised framework for change detection between two high resolution remote sensing images. Thanks to the use of local descriptors, the method does not need any image co-registration and is able to identify changes even with images acquired from different incidence angles and by different sensors. Local descriptors are used to both locally align images and identify changes. The setting of thresholds as well as the final grouping of isolated changes are performed thanks to a contrario statistical procedures. This provides a complete and automatic pipeline, whose efficiency is shown through several challenging pairs of high resolution urban images, acquired through different satellites.

Florence Tupin

Papers

Introduction to the Issue on Remote Sensing of Human Settlements: Status and Challenges

Interferometric SAR image coregistration based on the Fourier-Mellin invariant descriptor

As if by magic: self-supervised training of deep despeckling networks with MERLIN

How to handle spatial correlations in SAR despeckling? Resampling strategies and deep learning approaches

Unsupervised change detection between multi-sensor high resolution satellite images