Recent advances in remote sensing image processing

Remote sensing image processing is nowadays a mature research area. The techniques developed in the field allow many real-life applications with great societal value. For instance, urban monitoring, fire detection or flood prediction can have a great impact on economical and environmental issues. To attain such objectives, the remote sensing community has turned into a multidisciplinary field of science that embraces physics, signal theory, computer science, electronics, and communications. From a machine learning and signal/image processing point of view, all the applications are tackled under specific formalisms, such as classification and clustering, regression and function approximation, image coding, restoration and enhancement, source unmixing, data fusion or feature selection and extraction. This paper serves as a survey of methods and applications, and reviews the last methodological advances in remote sensing image processing.

Urban image classification with semisupervised multiscale cluster kernels

This paper presents a semisupervised support vector machine (SVM) that integrates the information of both labeled and unlabeled pixels efficiently. Method's performance is illustrated in the relevant problem of very high resolution image classification of urban areas. The SVM is trained with the linear combination of two kernels: a base kernel working only with labeled examples is deformed by a likelihood kernel encoding similarities between labeled and unlabeled examples. Results obtained on very high resolution (VHR) multispectral and hyperspectral images show the relevance of the method in the context of urban image classification. Also, its simplicity and the few parameters involved make the method versatile and workable by unexperienced users.

Testosterone Restoration by Enclomiphene Citrate in Men with Secondary Hypogonadism: a pharmacodynamic and Pharmacokinetic study.

OBJECTIVES: To determine the pharmacodynamic (PD) profile of serum total testosterone levels (TT) and luteinizing hormone (LH) in men with secondary hypogonadism following initial and chronic daily oral doses of enclomiphene citrate in comparison to transdermal testosterone. To determine the effects of daily oral doses of enclomiphene citrate (Androxal®) in comparison to transdermal testosterone on other hormones and markers in men with secondary hypogonadism. PATIENTS AND METHODS: This was a randomized, single blind, two-center phase II study to evaluate three different doses of enclomiphene citrate (6.25mg, 12.5mg and 25 mg Androxal®), versus AndroGel®, a transdermal testosterone, on 24-hour LH and TT in otherwise normal healthy men with secondary hypogonadism. Forty-eight men were enrolled in the trial (ITT Population), but 4 men had T levels >350 ng/dL at baseline. Forty-four men completed the study per protocol (PP population). All subjects enrolled in this trial had serum TT in the low range (<350 ng/dL) and had low to normal LH (<12 IU/L) on at least two occasions. TT and LH levels were assessed each hour for 24 hours to examine the effects at each of three treatment doses of enclomiphene versus a standard dose (5 grams) of transdermal testosterone (AndroGel). In the initial profile TT and LH were determined in a naïve population following a single initial oral or transdermal treatment (Day 1). This was contrasted to that seen after six weeks of continuous daily oral or transdermal treatment (Day 42). The pharmacokinetics of enclomiphene was performed in a select subpopulation. Serum samples were obtained over the course of the study to determine levels of various hormones and lipids. RESULTS: After six weeks of continuous use, the mean ± SD concentration of TT at Day 42 C0hrTT, was 604 ± 160 ng/dL for men taking the highest of dose of enclomiphene citrate (enclomiphene, 25 mg daily) and 500 ± 278 ng in those men treated with transdermal testosterone. These values were higher than Day 1 values but not different from each other (p = 0.23, T-test). All three doses of enclomiphene increased C0hrTT, CavgTT, CmaxTT, CminTT and CrangeTT. Transdermal testosterone also raised TT, albeit with more variability, and with suppressed LH levels. The patterns of TT over 24 hour period following six weeks of dosing could be fit to a non-linear function with morning elevations, mid-day troughs, and rising night-time levels. Enclomiphene and transdermal testosterone increased levels of TT within two weeks, but they had opposite effects on FSH and LH Treatment with enclomiphene did not significantly affect levels of TSH, ACTH, cortisol, lipids, or bone markers. Both transdermal testosterone and enclomiphene citrate decreased IGF-1 levels (p<0.05) but suppression was greater in the enclomiphene citrate groups. CONCLUSIONS: Enclomiphene citrate increased serum LH and TT; however, there was not a temporal association between the peak drug levels and the Cmax levels LH or TT. Enclomiphene citrate consistently increased serum TT into the normal range and increased LH and FSH above the normal range. The effects on LH and TT persisted for at least one week after stopping treatment.

Generating controlled image sets in cognitive neuroscience research.

The investigation of perceptual and cognitive functions with non-invasive brain imaging methods critically depends on the careful selection of stimuli for use in experiments. For example, it must be verified that any observed effects follow from the parameter of interest (e.g. semantic category) rather than other low-level physical features (e.g. luminance, or spectral properties). Otherwise, interpretation of results is confounded. Often, researchers circumvent this issue by including additional control conditions or tasks, both of which are flawed and also prolong experiments. Here, we present some new approaches for controlling classes of stimuli intended for use in cognitive neuroscience, however these methods can be readily extrapolated to other applications and stimulus modalities. Our approach is comprised of two levels. The first level aims at equalizing individual stimuli in terms of their mean luminance. Each data point in the stimulus is adjusted to a standardized value based on a standard value across the stimulus battery. The second level analyzes two populations of stimuli along their spectral properties (i.e. spatial frequency) using a dissimilarity metric that equals the root mean square of the distance between two populations of objects as a function of spatial frequency along x- and y-dimensions of the image. Randomized permutations are used to obtain a minimal value between the populations to minimize, in a completely data-driven manner, the spectral differences between image sets. While another paper in this issue applies these methods in the case of acoustic stimuli (Aeschlimann et al., Brain Topogr 2008), we illustrate this approach here in detail for complex visual stimuli.

A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.

MSight: an image analysis software for liquid chromatography-mass spectrometry.

Images obtained from high-throughput mass spectrometry (MS) contain information that remains hidden when looking at a single spectrum at a time. Image processing of liquid chromatography-MS datasets can be extremely useful for quality control, experimental monitoring and knowledge extraction. The importance of imaging in differential analysis of proteomic experiments has already been established through two-dimensional gels and can now be foreseen with MS images. We present MSight, a new software designed to construct and manipulate MS images, as well as to facilitate their analysis and comparison.

L'image de la Suisse à l'étranger

L'image qu'un pays a dans le monde est importante à plusieurs titres. Elle peut soutenir la commercialisation de biens et de services exportés, elle revêt un caractère tout particulier dans le cadre des promotions touristique et économique et elle peut aussi être de nature à contribuer aux relations qu'un pays entretient avec d'autres pays aux niveaux politique, économique ou culturel. L'image de la Suisse a fait l'objet d'études dans de nombreux pays, dont les Etats-Unis, l'Allemagne et la Chine, auprès d'échantillons représentatifs de la population ainsi qu'auprès de groupes de leaders d'opinion et cet ouvrage présente de manière synthétique les principaux résultats de ces études. Après une description de l'image globale de la Suisse auprès des personnes interrogées et une analyse des associations faites à l'évocation de la Suisse, une partie importante est consacrée aux dimensions qui caractérisent l'image du pays en différenciant notamment entre les dimensions liées à la Suisse en tant qu'espace socioculturel et les dimensions liées aux aspects économiques. Pour terminer, un dernier chapitre analyse l'impact de faits ayant marqué l'actualité helvétique, comme le grounding de Swissair, sur l'image de la Suisse dans les pays étudiés.

Using a NPWE model observer to assess suitable image quality for a digital mammography quality assurance programme.

A method of objectively determining imaging performance for a mammography quality assurance programme for digital systems was developed. The method is based on the assessment of the visibility of a spherical microcalcification of 0.2 mm using a quasi-ideal observer model. It requires the assessment of the spatial resolution (modulation transfer function) and the noise power spectra of the systems. The contrast is measured using a 0.2-mm thick Al sheet and Polymethylmethacrylate (PMMA) blocks. The minimal image quality was defined as that giving a target contrast-to-noise ratio (CNR) of 5.4. Several evaluations of this objective method for evaluating image quality in mammography quality assurance programmes have been considered on computed radiography (CR) and digital radiography (DR) mammography systems. The measurement gives a threshold CNR necessary to reach the minimum standard image quality required with regards to the visibility of a 0.2-mm microcalcification. This method may replace the CDMAM image evaluation and simplify the threshold contrast visibility test used in mammography quality.