Heated Optical Fiber for Distributed Soil-Moisture Measurements: A Lysimeter Experiment

An Actively Heated Fiber Optics (AHFO) method to estimate soil moisture is tested and the analysis technique improved on. The measurements were performed in a lysimeter uniformly packed with loam soil with variable water content profiles. In the first meter of the soil profi le, 30 m of fiber optic cable were installed in a 12 loops coil. The metal sheath armoring the fiber cable was used as an electrical resistance heater to generate a heat pulse, and the soil response was monitored with a Distributed Temperature Sensing (DTS) system. We study the cooling following three continuous heat pulses of 120 s at 36 W m(-1) by means of long-time approximation of radial heat conduction. The soil volumetric water contents were then inferred from the estimated thermal conductivities through a specifically calibrated model relating thermal conductivity and volumetric water content. To use the pre-asymptotic data we employed a time correction that allowed the volumetric water content to be estimated with a precision of 0.01-0.035 (m(3) m(-3)). A comparison of the AHFO measurements with soil-moisture measurements obtained with calibrated capacitance-based probes gave good agreement for wetter soils [discrepancy between the two methods was less than 0.04 (m(3) m(-3))]. In the shallow drier soils, the AHFO method underestimated the volumetric water content due to the longertime required for the temperature increment to become asymptotic in less thermally conductive media [discrepancy between the two methods was larger than 0.1 (m(3) m(-3))]. The present work suggests that future applications of the AHFO method should include longer heat pulses, that longer heating and cooling events are analyzed, and, temperature increments ideally be measured with higher frequency.

Kernel-based methods for change detection in remote sensing images

Nowadays, the joint exploitation of images acquired daily by remote sensing instruments and of images available from archives allows a detailed monitoring of the transitions occurring at the surface of the Earth. These modifications of the land cover generate spectral discrepancies that can be detected via the analysis of remote sensing images. Independently from the origin of the images and of type of surface change, a correct processing of such data implies the adoption of flexible, robust and possibly nonlinear method, to correctly account for the complex statistical relationships characterizing the pixels of the images. This Thesis deals with the development and the application of advanced statistical methods for multi-temporal optical remote sensing image processing tasks. Three different families of machine learning models have been explored and fundamental solutions for change detection problems are provided. In the first part, change detection with user supervision has been considered. In a first application, a nonlinear classifier has been applied with the intent of precisely delineating flooded regions from a pair of images. In a second case study, the spatial context of each pixel has been injected into another nonlinear classifier to obtain a precise mapping of new urban structures. In both cases, the user provides the classifier with examples of what he believes has changed or not. In the second part, a completely automatic and unsupervised method for precise binary detection of changes has been proposed. The technique allows a very accurate mapping without any user intervention, resulting particularly useful when readiness and reaction times of the system are a crucial constraint. In the third, the problem of statistical distributions shifting between acquisitions is studied. Two approaches to transform the couple of bi-temporal images and reduce their differences unrelated to changes in land cover are studied. The methods align the distributions of the images, so that the pixel-wise comparison could be carried out with higher accuracy. Furthermore, the second method can deal with images from different sensors, no matter the dimensionality of the data nor the spectral information content. This opens the doors to possible solutions for a crucial problem in the field: detecting changes when the images have been acquired by two different sensors.

In vivo measurement of tissue oxygenation by time-resolved luminescence spectroscopy: advantageous properties of dichlorotris(1, 10-phenanthroline)-ruthenium(II) hydrate.

Measuring tissue oxygenation in vivo is of interest in fundamental biological as well as medical applications. One minimally invasive approach to assess the oxygen partial pressure in tissue (pO2) is to measure the oxygen-dependent luminescence lifetime of molecular probes. The relation between tissue pO2 and the probes' luminescence lifetime is governed by the Stern-Volmer equation. Unfortunately, virtually all oxygen-sensitive probes based on this principle induce some degree of phototoxicity. For that reason, we studied the oxygen sensitivity and phototoxicity of dichlorotris(1, 10-phenanthroline)-ruthenium(II) hydrate [Ru(Phen)] using a dedicated optical fiber-based, time-resolved spectrometer in the chicken embryo chorioallantoic membrane. We demonstrated that, after intravenous injection, Ru(Phen)'s luminescence lifetime presents an easily detectable pO2 dependence at a low drug dose (1 mg∕kg) and low fluence (120 mJ∕cm2 at 470 nm). The phototoxic threshold was found to be at 10 J∕cm2 with the same wavelength and drug dose, i.e., about two orders of magnitude larger than the fluence necessary to perform a pO2 measurement. Finally, an illustrative application of this pO2 measurement approach in a hypoxic tumor environment is presented.

In vivo time-resolved spectroscopy of the human bronchial early cancer autofluorescence.

Time-resolved measurements of tissue autofluorescence (AF) excited at 405 nm were carried out with an optical-fiber-based spectrometer in the bronchi of 11 patients. The objectives consisted of assessing the lifetime as a new tumor/normal (T/N) tissue contrast parameter and trying to explain the origin of the contrasts observed when using AF-based cancer detection imaging systems. No significant change in the AF lifetimes was found. AF bronchoscopy performed in parallel with an imaging device revealed both intensity and spectral contrasts. Our results suggest that the spectral contrast might be due to an enhanced blood concentration just below the epithelial layers of the lesion. The intensity contrast probably results from the thickening of the epithelium in the lesions. The absence of T/N lifetime contrast indicates that the quenching is not at the origin of the fluorescence intensity and spectral contrasts. These lifetimes (6.9 ns, 2.0 ns, and 0.2 ns) were consistent for all the examined sites. The fact that these lifetimes are the same for different emission domains ranging between 430 and 680 nm indicates that there is probably only one dominant fluorophore involved. The measured lifetimes suggest that this fluorophore is elastin.

Sparse regularization for fiber ODF reconstruction: from the suboptimality of ℓ2 and ℓ1 priors to ℓ0.

Diffusion MRI is a well established imaging modality providing a powerful way to probe the structure of the white matter non-invasively. Despite its potential, the intrinsic long scan times of these sequences have hampered their use in clinical practice. For this reason, a large variety of methods have been recently proposed to shorten the acquisition times. Among them, spherical deconvolution approaches have gained a lot of interest for their ability to reliably recover the intra-voxel fiber configuration with a relatively small number of data samples. To overcome the intrinsic instabilities of deconvolution, these methods use regularization schemes generally based on the assumption that the fiber orientation distribution (FOD) to be recovered in each voxel is sparse. The well known Constrained Spherical Deconvolution (CSD) approach resorts to Tikhonov regularization, based on an ℓ(2)-norm prior, which promotes a weak version of sparsity. Also, in the last few years compressed sensing has been advocated to further accelerate the acquisitions and ℓ(1)-norm minimization is generally employed as a means to promote sparsity in the recovered FODs. In this paper, we provide evidence that the use of an ℓ(1)-norm prior to regularize this class of problems is somewhat inconsistent with the fact that the fiber compartments all sum up to unity. To overcome this ℓ(1) inconsistency while simultaneously exploiting sparsity more optimally than through an ℓ(2) prior, we reformulate the reconstruction problem as a constrained formulation between a data term and a sparsity prior consisting in an explicit bound on the ℓ(0)norm of the FOD, i.e. on the number of fibers. The method has been tested both on synthetic and real data. Experimental results show that the proposed ℓ(0) formulation significantly reduces modeling errors compared to the state-of-the-art ℓ(2) and ℓ(1) regularization approaches.

Quantitative comparison of reconstruction methods for intra-voxel fiber recovery from diffusion MRI.

Validation is arguably the bottleneck in the diffusion magnetic resonance imaging (MRI) community. This paper evaluates and compares 20 algorithms for recovering the local intra-voxel fiber structure from diffusion MRI data and is based on the results of the "HARDI reconstruction challenge" organized in the context of the "ISBI 2012" conference. Evaluated methods encompass a mixture of classical techniques well known in the literature such as diffusion tensor, Q-Ball and diffusion spectrum imaging, algorithms inspired by the recent theory of compressed sensing and also brand new approaches proposed for the first time at this contest. To quantitatively compare the methods under controlled conditions, two datasets with known ground-truth were synthetically generated and two main criteria were used to evaluate the quality of the reconstructions in every voxel: correct assessment of the number of fiber populations and angular accuracy in their orientation. This comparative study investigates the behavior of every algorithm with varying experimental conditions and highlights strengths and weaknesses of each approach. This information can be useful not only for enhancing current algorithms and develop the next generation of reconstruction methods, but also to assist physicians in the choice of the most adequate technique for their studies.

Active Learning of Very-High Resolution Optical Imagery with SVM: Entropy vs Margin Sampling

An active learning method is proposed for the semi-automatic selection of training sets in remote sensing image classification. The method adds iteratively to the current training set the unlabeled pixels for which the prediction of an ensemble of classifiers based on bagged training sets show maximum entropy. This way, the algorithm selects the pixels that are the most uncertain and that will improve the model if added in the training set. The user is asked to label such pixels at each iteration. Experiments using support vector machines (SVM) on an 8 classes QuickBird image show the excellent performances of the methods, that equals accuracies of both a model trained with ten times more pixels and a model whose training set has been built using a state-of-the-art SVM specific active learning method

Domain Adaptation in remote sensing: increasing the portability of land-cover classifiers

Among the types of remote sensing acquisitions, optical images are certainly one of the most widely relied upon data sources for Earth observation. They provide detailed measurements of the electromagnetic radiation reflected or emitted by each pixel in the scene. Through a process termed supervised land-cover classification, this allows to automatically yet accurately distinguish objects at the surface of our planet. In this respect, when producing a land-cover map of the surveyed area, the availability of training examples representative of each thematic class is crucial for the success of the classification procedure. However, in real applications, due to several constraints on the sample collection process, labeled pixels are usually scarce. When analyzing an image for which those key samples are unavailable, a viable solution consists in resorting to the ground truth data of other previously acquired images. This option is attractive but several factors such as atmospheric, ground and acquisition conditions can cause radiometric differences between the images, hindering therefore the transfer of knowledge from one image to another. The goal of this Thesis is to supply remote sensing image analysts with suitable processing techniques to ensure a robust portability of the classification models across different images. The ultimate purpose is to map the land-cover classes over large spatial and temporal extents with minimal ground information. To overcome, or simply quantify, the observed shifts in the statistical distribution of the spectra of the materials, we study four approaches issued from the field of machine learning. First, we propose a strategy to intelligently sample the image of interest to collect the labels only in correspondence of the most useful pixels. This iterative routine is based on a constant evaluation of the pertinence to the new image of the initial training data actually belonging to a different image. Second, an approach to reduce the radiometric differences among the images by projecting the respective pixels in a common new data space is presented. We analyze a kernel-based feature extraction framework suited for such problems, showing that, after this relative normalization, the cross-image generalization abilities of a classifier are highly increased. Third, we test a new data-driven measure of distance between probability distributions to assess the distortions caused by differences in the acquisition geometry affecting series of multi-angle images. Also, we gauge the portability of classification models through the sequences. In both exercises, the efficacy of classic physically- and statistically-based normalization methods is discussed. Finally, we explore a new family of approaches based on sparse representations of the samples to reciprocally convert the data space of two images. The projection function bridging the images allows a synthesis of new pixels with more similar characteristics ultimately facilitating the land-cover mapping across images.

En face optical coherence tomography of foveal microstructure in full-thickness macular hole: a model to study perifoveal müller cells.

PURPOSE: To characterize perifoveal intraretinal cavities observed around full-thickness macular holes (MH) using en face optical coherence tomography and to establish correlations with histology of human and primate maculae. DESIGN: Retrospective nonconsecutive observational case series. METHODS: Macular en face scans of 8 patients with MH were analyzed to quantify the areas of hyporeflective spaces, and were compared with macular flat mounts and sections from 1 normal human donor eye and 2 normal primate eyes (Macaca fascicularis). Immunohistochemistry was used to study the distribution of glutamine synthetase, expressed by Müller cells, and zonula occludens-1, a tight-junction protein. RESULTS: The mean area of hyporeflective spaces was lower in the inner nuclear layer (INL) than in the complex formed by the outer plexiform (OPL) and the Henle fiber layers (HFL): 5.0 × 10(-3) mm(2) vs 15.9 × 10(-3) mm(2), respectively (P < .0001, Kruskal-Wallis test). In the OPL and HFL, cavities were elongated with a stellate pattern, whereas in the INL they were rounded and formed vertical cylinders. Immunohistochemistry confirmed that Müller cells followed a radial distribution around the fovea in the frontal plane and a "Z-shaped" course in the axial plane, running obliquely in the OPL and HFL and vertically in the inner layers. In addition, zonula occludens-1 co-localized with Müller cells within the complex of OPL and HFL, indicating junctions in between Müller cells and cone axons. CONCLUSION: The dual profile of cavities around MHs correlates with Müller cell morphology and is consistent with the hypothesis of intra- or extracellular fluid accumulation along these cells.