Resolution Optical Tomography Research Articles

CANDELS Meets GSWLC: Evolution of the Relationship between Morphology and Star Formation Since z = 2

Abstract Galaxy morphology and its evolution over the cosmic epoch hold important clues for understanding the regulation of star formation (SF). However, studying the relationship between morphology and SF has been hindered by the availability of consistent data at different redshifts. Our sample, combining CANDELS (0.8 < z < 2.5) and the GALEX-SDSS-WISE Legacy Catalog (GSWLC; z ∼ 0), has physical parameters derived using consistent SED fitting with flexible dust attenuation laws. We adopt visual classifications from Kartaltepe et al. and expand them to z ∼ 0 using SDSS images matching the physical resolution of CANDELS rest-frame optical images and deep FUV GALEX images matching the physical resolution of the CANDELS rest-frame FUV images. Our main finding is that disks with SF clumps at z ∼ 0 make a similar fraction (∼15%) of star-forming galaxies as at z ∼ 2. The clumpy disk contribution to the SF budget peaks at z ∼ 1, rather than z ∼ 2, suggesting that the principal epoch of disk assembly continues to lower redshifts. Star-forming spheroids (“blue nuggets”), though less centrally concentrated than quenched spheroids, contribute significantly (∼15%) to the SF budget at z ∼ 1–2, suggesting that compaction precedes quenching. Among green valley and quiescent galaxies, the pure spheroid fraction drops after z ∼ 1, whereas spheroids with disks (S0-like) become dominant. Mergers at or nearing coalescence are enhanced in SFR relative to the main sequence at all redshifts by a factor of ∼2, but contribute ≲5% to the SF budget, with their contribution remaining small above the main sequence.

BROCELIANDE: A COMPARATIVE STUDY OF ATTRIBUTE PROFILES AND FEATURE PROFILES FROM DIFFERENT ATTRIBUTES

Abstract. Morphological attribute profiles (APs) are among the most effective spatial-spectral methods to perform multilevel image description based on hierarchical tree-based representation. They have been widely applied to the processing and characterization of remote sensing images, in particular to tackle classification task, in the literature. Recently, a novel extension of APs called FPs has been proposed by replacing pixel gray-levels with some statistical and geometrical features when forming the output profiles. FPs have been proved to be more efficient than the standard APs when generated from both inclusion and partition trees. The motivation of this article is to conduct a comparative study of APs and FPs using different attributes including some novel ones that have not been used in the literature. We also present our developed library called Broceliande, which proposes efficient implementation of APs and FPs to perform remote sensing image classification, with various choices of tree structures as well as attributes. We perform our experiments on two high resolution optical image data sets and provide comparative results of APs and FPs, showing and confirming their effectiveness to describe and classify remote sensing images.

FCC-Net: A Full-Coverage Collaborative Network for Weakly Supervised Remote Sensing Object Detection

With an ever-increasing resolution of optical remote-sensing images, how to extract information from these images efficiently and effectively has gradually become a challenging problem. As it is prohibitively expensive to label every object in these high-resolution images manually, there is only a small number of high-resolution images with detailed object labels available, highly insufficient for common machine learning-based object detection algorithms. Another challenge is the huge range of object sizes: it is difficult to locate large objects, such as buildings and small objects, such as vehicles, simultaneously. To tackle these problems, we propose a novel neural network based remote sensing object detector called full-coverage collaborative network (FCC-Net). The detector employs various tailored designs, such as hybrid dilated convolutions and multi-level pooling, to enhance multiscale feature extraction and improve its robustness in dealing with objects of different sizes. Moreover, by utilizing asynchronous iterative training alternating between strongly supervised and weakly supervised detectors, the proposed method only requires image-level ground truth labels for training. To evaluate the approach, we compare it against a few state-of-the-art techniques on two large-scale remote-sensing image benchmark sets. The experimental results show that FCC-Net significantly outperforms other weakly supervised methods in detection accuracy. Through a comprehensive ablation study, we also demonstrate the efficacy of the proposed dilated convolutions and multi-level pooling in increasing the scale invariance of an object detector.

Rapid and Large-Area Visualization of Grain Boundaries in MoS2 on SiO2 Using Vapor Hydrofluoric Acid.

Grain boundaries in two-dimensional (2D) material layers have an impact on their electrical, optoelectronic, and mechanical properties. Therefore, the availability of simple large-area characterization approaches that can directly visualize grains and grain boundaries in 2D materials such as molybdenum disulfide (MoS2) is critical. Previous approaches for visualizing grains and grain boundaries in MoS2 are typically based on atomic resolution microscopy or optical imaging techniques (i.e., Raman spectroscopy or photoluminescence), which are complex or limited to the characterization of small, micrometer-sized areas. Here, we show a simple approach for an efficient large-area visualization of the grain boundaries in continuous chemical vapor-deposited films and domains of MoS2 that are grown on a silicon dioxide (SiO2) substrate. In our approach, the MoS2 layer on a SiO2/Si substrate is exposed to vapor hydrofluoric acid (VHF), resulting in the differential etching of SiO2 at the MoS2 grain boundaries and SiO2 underneath the MoS2 grains as a result of VHF diffusing through the defects in the MoS2 layer at the grain boundaries. The location of the grain boundaries can be seen by the resulting SiO2 pattern using optical microscopy, scanning electron microscopy, or Raman spectroscopy. This method allows for a simple and rapid evaluation of grain sizes in 2D material films over large areas, thereby potentially facilitating the optimization of synthesis processes and advancing applications of 2D materials in science and technology.

VHR Semantic Labeling by Random Forest Classification and Fusion of Spectral and Spatial Features on Google Earth Engine

Semantic labeling is an active field in remote sensing applications. Although handling high detailed objects in Very High Resolution (VHR) optical image and VHR Digital Surface Model (DSM) is a challenging task, it can improve the accuracy of semantic labeling methods. In this paper, a semantic labeling method is proposed by fusion of optical and normalized DSM data. Spectral and spatial features are fused into a Heterogeneous Feature Map to train the classifier. Evaluation database classes are impervious surface, building, low vegetation, tree, car, and background. The proposed method is implemented on Google Earth Engine. The method consists of several levels. First, Principal Component Analysis is applied to vegetation indexes to find maximum separable color space between vegetation and non-vegetation area. Gray Level Co-occurrence Matrix is computed to provide texture information as spatial features. Several Random Forests are trained with automatically selected train dataset. Several spatial operators follow the classification to refine the result. Leaf-Less-Tree feature is used to solve the underestimation problem in tree detection. Area, major and, minor axis of connected components are used to refine building and car detection. Evaluation shows significant improvement in tree, building, and car accuracy. Overall accuracy and Kappa coefficient are appropriate.

Human Breast Numerical Model Generation Based on Deep Learning for Photoacoustic Imaging.

Photoacoustic imaging which combines high contrast of optical imaging and high resolution of ultrasound imaging, can provide functional information, potentially playing a crucial role in the study of breast cancer diagnostics. However, open source dataset for PA imaging research is insufficient on account of lacking clinical data. To tackle this problem, we propose a method to automatically generate breast numerical model for photoacoustic imaging. The different type of tissues is automatically extracted first by employing deep learning and other methods from mammography. And then the tissues are combined by mathematical set operation to generate a new breast image after being assigned optical and acoustic parameters. Finally, breast numerical model with proper optical and acoustic properties are generated, which are specifically suitable for PA imaging studies, and the experiment results indicate that our method is feasible with high efficiency.

The phase-only Tribonacci photon sieve

In this paper, a kind of diffractive optical elements (DOEs), i.e., the phase-only Tribonacci photon sieve (TriPS), is proposed. The TriPS constructed with the Tribonacci sequence can produce two equal-intensity axial foci and the ratio of the focal lengths is related to the 3-golden mean. In the design of the TriPS, the Hamming window function was utilized to modulate the distribution of the pinholes. It was found that the TriPS generates smaller foci than the corresponding Tribonacci zone plate (TriZP). The simulation and experimental results verified the performance of the proposed DOE. Moreover, a TriPS with an optimum order was found to possess smaller focal spots, which would be useful in high resolution optical imaging.

High-resolution measurement based on the combination of multi-vision system and synthetic aperture imaging

A new system that combines multi-vision system and synthetic aperture system is proposed to improve the measurement of small-scale or longer-range object. In the system, the parallax information is recorded by multi-vision system, meanwhile, a higher optical resolution image is obtained by synthesizing several optical apertures of multi-vision system to increase the effective aperture. By fusing the higher resolution image into the parallax images, we can acquire higher resolution images with parallax information from which the measurement can be improved greatly. As an example, a trinocular vision system improved by synthetic aperture has been studied experimentally. Results show that the method can improve the resolution in detection about two times and perform more accurate measurement than traditional multi-vision system.

Change Detection Based on Feature Optimization in High Resolution Optical Image

Object-oriented change detection (CD) method can make full use of feature information of high resolution optical images. However, the feature information is redundancy in Object-oriented CD of high resolution optical image due to the fact that the image has multiple bands. Therefore, feature optimization is necessary to object-oriented CD. Aiming at the problem, a novel CD method based on feature optimization which combines improved locally linear embedding (ILLE) algorithm and object-oriented technology is proposed. Firstly, two temporal images are inverted into objects using multi-scale segmentation algorithm. Secondly, the spectral and texture features of the objects are extracted to construct the novel feature change vector. Thirdly, the improved LLE algorithm, which introduces the Geodesic distance metric, is designed to optimize the feature change vector. Finally, the CD result is obtained by FCM algorithm. Experiments construct on the real GF-1 images, and the results confirm the effectiveness of the proposed method.

Multi-plasmon resonances enhanced two-photon coherent anti-Stokes Raman scattering by nanorods.

The development of new structures allows two-photon coherent anti-Stokes Raman scattering (TPCARS) to be strongly enhanced by multiple surface plasmon resonances (MSPRs). In this paper, plasmonic structure consisting of two Ag nanorods is designed and the enhancement of TPCARS is investigated. By properly selecting designing structure parameters, strong MSPRs peaks at 1020nm and 505nm are obtained, which can enhance the TPCARS signal based on the frequency match of the fundamental frequency and frequency doubling. The enhancement factor of TPCARS can reach as high as 3.66×1028 with significant electric field enhancements under appropriate selection of system parameters. Furthermore, the two-photon process can be controlled at different optical frequencies by changing the geometric parameters of Ag nanorods. The new scheme advanced in this work can help to achieve single molecule level of CARS, and may have a potential to increase the intensity and resolution of nonlinear optical imaging.

Cloud Removal with Fusion of High Resolution Optical and SAR Images Using Generative Adversarial Networks

The existence of clouds is one of the main factors that contributes to missing information in optical remote sensing images, restricting their further applications for Earth observation, so how to reconstruct the missing information caused by clouds is of great concern. Inspired by the image-to-image translation work based on convolutional neural network model and the heterogeneous information fusion thought, we propose a novel cloud removal method in this paper. The approach can be roughly divided into two steps: in the first step, a specially designed convolutional neural network (CNN) translates the synthetic aperture radar (SAR) images into simulated optical images in an object-to-object manner; in the second step, the simulated optical image, together with the SAR image and the optical image corrupted by clouds, is fused to reconstruct the corrupted area by a generative adversarial network (GAN) with a particular loss function. Between the first step and the second step, the contrast and luminance of the simulated optical image are randomly altered to make the model more robust. Two simulation experiments and one real-data experiment are conducted to confirm the effectiveness of the proposed method on Sentinel 1/2, GF 2/3 and airborne SAR/optical data. The results demonstrate that the proposed method outperforms state-of-the-art algorithms that also employ SAR images as auxiliary data.

A Nano-Gap Nano-Antenna for Enhancing the Spatial Resolution of Optical Brain Imaging

Optical brain imaging has the potential for a bright future thanks to its low cost and portability relative to other biomedical imaging modalities. Temporal and spatial resolutions are considered to be the discriminatory features for selection of biomedical imaging equipment. Optical brain imaging systems, however, still face the bottleneck of limited spatial resolution. In this study, a novel method for guiding near infrared light at one of two particular gaps spaced nanometers apart has been presented. It includes the design of a nanogap nano-antenna for measurement of overlapping information on vicinities of only nanoscale separation distance, which could result in enhancement of the spatial resolution of optical brain imaging systems. The design of the proposed nano-gap nano-antenna channels near-infrared light to a specific path among two gaps separated by a nanometer-scale distance. A supportive analysis of gap design also is presented in this study. Additionally, the results of a comprehensive analysis of the behavior of light through the designed nano-gap nano-antenna are provided. The proposed methodology is a practical substitute for a high-density probe arrangement as well as a possible means of spatial resolution enhancement.

基于区域性IHS_NSST的国产高分合成孔径雷达和光学图像融合

基于区域性IHS_NSST的国产高分合成孔径雷达和光学图像融合

Development of a hybrid photoacoustic and optical monitoring system for the study of laser ablation processes upon the removal of encrustation from stonework

In the context of this work, a prototype hybrid photoacoustic (PA) and optical system for the on-line monitoring of laser cleaning procedures is presented. The developed apparatus has enabled the detection of MHz frequency range acoustic waves generated during the laser ablation process. The intrinsically generated PA signals combined with high resolution optical images provide the opportunity to follow the cleaning process accurately and in real time. Technical mock-ups have been used to demonstrate the potential of this novel technique with emphasis given to applications that refer to the restoration of Cultural Heritage (CH) surfaces. Towards this purpose, the real time monitoring of the laser assisted removal of unwanted encrustation from stonework has been achieved using IR and UV wavelengths. This novel approach has allowed for the precise determination of the critical number of laser pulses required for the elimination of the encrustation layer, while highlighting the dominant ablation mechanisms according to the irradiation wavelength. The promising results obtained using the prototype hybrid PA and optical system can open up new perspectives in the monitoring of laser cleaning interventions, promoting an improved restoration outcome.

Evaluation of gap-filling methods for Landsat 7 ETM+ SLC-off image for LULC classification in a heterogeneous landscape of West Africa

ABSTRACTThe Landsat mission which has existed over five decades has remained at the forefront of providing consistent moderate spatial and temporal resolution optical images of the earth. The failure of the scan line corrector (SLC) on board the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) in May 2003 has permanently resulted in data gaps on each Landsat 7 scene. Due to the obvious negative impacts on the image usability, a number of methods have been developed to fill the no-data areas in the image. This study assessed the performance of four Landsat 7 ETM+ SLC-off gap-filling methods in a highly heterogeneous landscape of West Africa for two different seasons (dry and rainy). The methods considered are: (1) Weighted Linear Regression (WLR) integrated with Laplacian Prior Regularization Method (LPRM), (2) Localised Linear Histogram Matching (LLHM), (3) Neighbourhood Similar Pixel Interpolator (NSPI) and (4) Geostatistical Neighbourhood Similar Pixel Interpolator (GNSPI). All the images used were Landsat 7 ETM+ SLC-off images, temporally close and from the same season for each set of time step. Visual comparison, mean, and standard deviations of the histograms of all bands of only the filled areas were used to assess the results. Additionally, overall accuracy (OA), kappa coefficient (κ), and balanced accuracy (BA) per class were used to evaluate a land use/cover (LULC) classification based on the gap-filled images. Visually, all the four methods were able to completely fill the gaps in the Landsat 7 ETM+ SLC-off image. They all look similar and spatially continuous with no anomalies or artefacts on them. The histograms from each band for only the filled areas for all the four methods also gave similar means and standard deviations in most cases. All the four gap-filling methods provided satisfactory results (OA >96% and κ> 0.937 in all methods for images in the dry season and OA >93% and κ> 0.877 for the image in the rainy season) in the land cover classification considering the complexity of the study area. But the GNSPI was superiority in all cases with the highest OA of 97.1% and κ of 0.947 in the dry season and OA of 94.6% and κ of 0.899 in the rainy season. This implies that the GNSPI is more robust in gap filling of Landsat 7 ETM+ SLC-off images than the other three methods in a heterogeneous landscape of West Africa regardless of the season. This study suggests that gap filling of Landsat 7 ETM+ SLC-off images will help to increase the number of Landsat images needed to build time-series data for a data-scarce region such as West Africa.

Recent advances in STED and RESOLFT super-resolution imaging techniques.

Stimulated emission depletion (STED) and reversible saturable optical fluorescence transition (RESOLFT) microscopy are the super-resolution imaging techniques that can acquire nanoscale spatial resolution. The spatial resolution of the other far-field optical microscopic techniques is bound by diffraction limit, however, STED/RESOLFT techniques eliminate the diffraction barrier. These microscopic techniques have taken the limits of optical image resolution down to the nanometer scale and opened new paths for biomedical and nanophosphor research. In this paper, we review the recent advancements of these techniques in the field of nanoscopy using continuous wave (CW) laser sources. Further, we discuss the main limitation of the STED microscopy in terms of essential requirements of higher depletion beam power and photobleaching issues. The RESOLFT microscopic technique can be considered as an alternate technique to overcome limitations of existing STED microscopy. Moreover, the Bessel and Gaussian-Bessel beam STED microscopic techniques are also reviewed to produce deep images with faster scanning of the samples. The organic molecules as well as the fluorescent doped nanoparticles like ZnSe:Mn having characteristics of excited state absorption can be investigated using RESOLFT microscopy.

Resolution limit of label-free far-field microscopy

The Abbe diffraction limit, which relates the maximum optical resolution to the numerical aperture of the lenses involved and the optical wavelength, is generally considered a practical limit that cannot be overcome with conventional imaging systems. However, it does not represent a fundamental limit to optical resolution, as demonstrated by several new imaging techniques that prove the possibility of finding the subwavelength information from the far field of an optical image. These include super-resolution fluorescence microscopy, imaging systems that use new data processing algorithms to obtain dramatically improved resolution, and the use of super-oscillating metamaterial lenses. This raises the key question of whether there is in fact a fundamental limit to the optical resolution, as opposed to practical limitations due to noise and imperfections, and if so then what it is. We derive the fundamental limit to the resolution of optical imaging and demonstrate that while a limit to the resolution of a fundamental nature does exist, contrary to the conventional wisdom it is neither exactly equal to nor necessarily close to Abbe’s estimate. Furthermore, our approach to imaging resolution, which combines the tools from the physics of wave phenomena and the methods of information theory, is general and can be extended beyond optical microscopy, e.g., to geophysical and ultrasound imaging.

Evaluating Resilience-Centered Development Interventions with Remote Sensing

Natural disasters are projected to increase in number and severity, in part due to climate change. At the same time a growing number of disaster risk reduction (DRR) and climate change adaptation measures are being implemented by governmental and non-governmental organizations, and substantial post-disaster donations are frequently pledged. At the same time there has been increasing demand for transparency and accountability, and thus evidence of those measures having a positive effect. We hypothesized that resilience-enhancing interventions should result in less damage during a hazard event, or at least quicker recovery. In this study we assessed recovery over a 3 year period of seven municipalities in the central Philippines devastated by Typhoon Haiyan in 2013. We used very high resolution optical images (<1 m), and created detailed land cover and land use maps for four epochs before and after the event, using a machine learning approach with extreme gradient boosting. The spatially and temporally highly variable recovery maps were then statistically related to detailed questionnaire data acquired by DEval in 2012 and 2016, whose principal aim was to assess the impact of a 10 year land-planning intervention program by the German agency for technical cooperation (GIZ). The survey data allowed very detailed insights into DRR-related perspectives, motivations and drivers of the affected population. To some extent they also helped to overcome the principal limitation of remote sensing, which can effectively describe but not explain the reasons for differential recovery. However, while a number of causal links between intervention parameters and reconstruction was found, the common notion that a resilient community should recover better and more quickly could not be confirmed. The study also revealed a number of methodological limitations, such as the high cost for commercial image data not matching the spatially extensive but also detailed scale of field evaluations, the remote sensing analysis likely overestimating damage and thus providing incorrect recovery metrics, and image data catalogues especially for more remote communities often being incomplete. Nevertheless, the study provides a valuable proof of concept for the synergies resulting from an integration of socio-economic survey data and remote sensing imagery for recovery assessment.

Optimal quantum estimation of the displacement in optical imaging

We present a general method to find the ultimate limits on the resolution of optical image on a surface perpendicular to the light beam propagation. We prove that the optimal sensitivity always obeys the standard quantum limit for a single mode light. In the absence of photon losses, we find the possibility to achieve the Heisenberg limit by using two-mode or multi-mode lights. We also use the variational approach to obtain the lower bound on the sensitivity considering the effects of photon losses.

Light filters influence on the chromaticity for Fresnel microprisms

The application of Fresnel microprisms always reduces the contrast of the formed optical images. During ophthalmology diagnostics with microprisms the visual acuity of the patients decreases depending on the contract of test chart optotypes. The main reason for reducing the contrast and for worsening resolution of optical images is the chromaticity of white light that occurs when the light rays of different wavelengths are refracting on the prism surfaces. A light filters can be used to diminish the chromaticity, so the quantitative data on this effect are necessary. Also, the ways for reducing the chromatic aberrations with light filters should be considered. From this point a modified rigid Fresnel microprisms with protected relief developed at the Institute for Information Recording (Ukraine) were investigated. Comparative optical characteristics were calculated for microprisms with yellow-blue and green-blue filters. Experimental data on chromaticity aberrations were obtained using new electronic method for registering the resolution of test stroke images with and without filters. The influence of light filters on the visual images through Fresnel microprisms was registered. The reasons for contrast decrease and chromaticity zone diminishing with light filters are discussed for all of investigated ophthalmic microprisms.