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In the dynamic field of satellite imagery, the significance of super-resolution (SR) techniques, grounded on advanced deep learning methods, is paramount. A thorough understanding and remediation of the distinct challenges posed by various land cover types for image resolution enhancement form the essence of this research. This work diligently employs two unique neural networks, SRCNN and SwinIR Transformer, to scrutinize their varying impacts on a range of land cover types, ensuring a detailed and comprehensive exploration. This study transcends the mere enhancement of the Sentinel-2 dataset's resolution from 20 m/pix to 10 m/pix. It ambitiously seeks to excavate the intricate trends inherent to different land cover types and their corresponding interactions with SR processes. The application of neural networks on 255 × 254 pixel patches, covering six dominant types—forests, large fields, small fields, urban, sub-urban, and mixed—highlights substantial variations in metrics, underlining the individual interactions of each land cover type with SR techniques. A comprehensive accuracy assessment is meticulously conducted, employing an array of metrics and frequency domains to shed light on the nuanced differences and provide vital insights for optimizing each land cover type's SR approaches. Notably, the PSNR metric reveals significant disparities, particularly in the “forest” and “urban” categories for both SRCNN and SwinIR. According to the PSNR metric, the “forest” class yielded the best results with 66.06 for SRCNN and 67.00 for SwinIR, while the “urban” class marked the lowest with 55.09 and 57.02, respectively, reinforcing the critical nature of this study.

Wetlands play an important role in supporting biodiversity conservation and helping sustainable development. Time series wetland classification requires a series of sample sets acquired at different times. To meet this need, we proposed a method for automatically producing global wetland samples based on 13 global and regional wetland-related datasets and millions of images from Landsat 8 Operational Land Imager (OLI), Moderate Resolution Imaging Spectroradiometer (MODIS), Sentinel-1 Synthetic Aperture Radar Ground Range Detected, and Sentinel-2 Multispectral Instrument sensors. On this basis, we analyzed the classification capabilities of wetland features in the above four sensors and obtained a multisensor, full spectral band, multi-index wetland classification feature dataset at a global scale. The analysis results show that the shortwave infrared band and land surface water index can distinguish nonwetlands from other land cover types well, while the modified normalized difference water index, normalized difference vegetation index, normalized difference water index, and thermal infrared bands could help distinguish water bodies and wetlands. C bands in Sentinel 1 perform well in identifying water bodies, but the ability to distinguish wetlands is limited. The mapping coefficients of the corresponding spectral bands and indexes between Sentinel-2, MODIS data, and Landsat were obtained to help realize the collaborative use of multisensor image data. This article could provide guidance for large-scale wetland classification and mapping research.

The in situ measurement of thick oil slick thickness (>0.5 mm) and area in real time in order to estimate the volume of an oil spill is very important for determining the oil spill response strategy and evaluating the oil spill disposal efficiency. In this article, a method is proposed to assess the volume of oil slicks by simultaneously measuring the thick oil slick thickness and area using ultrasonic inspection and image processing methods, respectively. A remotely operated vehicle (ROV), integrating two ultrasonic immersion transducers, was implemented as a platform to receive ultrasonic reflections from an oil slick. The oil slick thickness was determined by multiplying the speed of sound by the ultrasonic traveling time within the oil slick, which was calculated using the cross-correlation method. Images of the oil slick were captured by an optical camera using an airborne drone. The oil slick area was calculated by conducting image processing on images of the oil slick using the proposed image processing algorithms. Multiple measurements were performed to verify the proposed method in the laboratory experiments. The results show that the thickness, area and volume of a thick oil slick can be accurately measured with the proposed method. The method could potentially be used as an applicable tool for measuring the volume of an oil slick during an oil spill response. [ABSTRACT FROM AUTHOR]

The large-aperture static imaging spectrometer (LASIS) is an interference spectrometer with high device stability, high throughput, a wide spectral range, and a high spectral resolution. One frame image of the original data cube acquired by the LASIS shows the image superimposed with interference fringes, which is distinctly different from traditional hyperspectral images. For compression studies using this new type of data, a lossless compression scheme that combines a novel data rearrange method and the lossless multispectral and hyperspectral image compression standard CCSDS-123 is presented. In the rearrange approach, the LASIS data cube is rearranged such that the interference information overlapped on the image can be separated, and the results are then processed using the CCSDS-123 standard. Then, several experiments are conducted to investigate the performance of the rearrange method and examine the impact of different CCSDS-123 parameter settings for the LASIS. The experimental results indicate that the proposed scheme provides a 32.9% higher ratio than traditional rearrange methods. Moreover, an adequate parameter combination for this compression scheme for LASIS is presented, and it yields a 19.6% improvement over the default settings suggested by the standard. [ABSTRACT FROM AUTHOR]

The transport sector is experiencing a fast and revolutionary development, moving toward the deployment of autonomous vehicles. Thus, the accuracy of the position information and the integrity of the navigation system have become key factors. In this article, we address the problem of the enhancement of the integrity of the position provided by a global navigation satellite system receiver by exploiting sensor fusion. To this aim, we estimate the lateral offset and heading of the vehicle with respect to a georeferenced roadway centerline from the images supplied by an on-board camera. Moreover, we perform integrity monitoring based on the implementation of the solution separation in the parity space. The numerical results indicate that the use of sensor fusion and digital map allows us to attain a longitudinal protection level reduction with respect to the case in which sensor fusion is not exploited. More specifically, a decrease of about 70% is achieved when a single constellation is used, while reduction is less relevant, about 15%, when two constellations are employed. [ABSTRACT FROM AUTHOR]

A segmented primary mirror is very important for extra-large astronomical telescopes, in order to detect the phase error between segmented mirrors. Traditional iterative algorithms are hard to detect co−phasing aberrations in real time due to the long-time iterative process. Deep learning has shown large potential in wavefront sensing, and it gradually focuses on detecting piston error. However, the current methods based on deep learning are mainly applied to coarse phase sensing, and only consider the detection of piston error with no tip/tilt errors, which is inconsistent with reality. In this paper, by innovatively designing the form of pupil mask, and further updating the OTF in the frequency domain, we obtain a new decoupled independent feature image that can simultaneously detect the piston error and tilt/tilt error of all sub-mirrors, which is effectively decoupled, and eliminates the dependence of the data set on the imaging object. Then, the Bi−GRU network is used to recover phase error information with high accuracy from the feature image proposed in this paper. The network's detection accuracy ability is verified under single wavelength and broadband spectrum in simulation. This paper demonstrates that co−phasing errors can be accurately decoupled and extracted by the new feature image we proposed and will contribute to the fine phasing accuracy and practicability of the extended scenes for the segmented telescopes. [ABSTRACT FROM AUTHOR]

The large-aperture static imaging spectrometer (LASIS) is an interference spectrometer with high device stability, high throughput, a wide spectral range, and a high spectral resolution. One frame image of the original data cube acquired by the LASIS shows the image superimposed with interference fringes, which is distinctly different from traditional hyperspectral images. For compression studies using this new type of data, a lossless compression scheme that combines a novel data rearrange method and the lossless multispectral and hyperspectral image compression standard CCSDS-123 is presented. In the rearrange approach, the LASIS data cube is rearranged such that the interference information overlapped on the image can be separated, and the results are then processed using the CCSDS-123 standard. Then, several experiments are conducted to investigate the performance of the rearrange method and examine the impact of different CCSDS-123 parameter settings for the LASIS. The experimental results indicate that the proposed scheme provides a 32.9% higher ratio than traditional rearrange methods. Moreover, an adequate parameter combination for this compression scheme for LASIS is presented, and it yields a 19.6% improvement over the default settings suggested by the standard.

Object detection is an important task for rapidly localizing target objects using high-resolution satellite imagery (HRSI). Although deep learning has been shown an efficient means of detection, object detection in HRSI remains problematic due to variations in object scale and size. In this article, we present a novel deep neural network (DNN) that combines double-shot neural network with misplaced localization strategy that adapts to object detection tasks in satellite images. This novel architecture optimizes the localization of small and narrow rectangular objects, which frequently appear in HRSI images, without accuracy loss on other size and width/height ratio objects. This method outperforms other state-of-art methods. We evaluated our proposed method on the NWPU VHR-10 public dataset and a new benchmark dataset (seven classes of small and narrow rectangular objects, SNRO-7). The NWPU VHR-10 dataset built a dataset for multiclass object detection; however, most labels are assigned in normal size and width/height ratios. SNRO-7 focuses on multiscale and multisize object detection and includes many small-size and narrow rectangular objects. We also evaluated the accuracy difference on DNN training and testing between gray scale and RGB datasets. The results of the experiment on object detection reveal that the mean average precision (MaP) of our method is 82.6% in NWPU VHR-10 and 79.3% in SNRO-7, which exceeds the MaPs of other state-of-the-art object detection neural networks. The model trained with the RGB dataset can achieve similar accuracy (around 79.0% MIoU) testing in both RGB and gray scale datasets. When training the model by mixing RGB and gray scale datasets in different ratios, the accuracy in the RGB channel significantly decreases with increasing gray scale images, but this does not influence the accuracy in the gray scale dataset.

As one of the world's largest freshwater ecosystems, the Great Lakes Basin houses thousands of acres of wetlands that support a variety of crucial ecological and environmental functions at the local, regional, and global scales. Monitoring these wetlands is critical to conservation and restoration efforts; however, current methods that rely on field monitoring are labor-intensive, costly, and often outdated. In this article, we present a graphical user interface constructed in Google Earth Engine called the Wetland Extent Tool (WET), which allows semiautomatic wetland classification according to a user-input area of interest and date range. WET conducts multisource, moderate resolution processing utilizing Landsat 8 Operational Land Imager, Sentinel-2 MultiSpectral Instrument, Sentinel-1 C-SAR, and Shuttle Radar Topography Mission (SRTM) datasets to classify wetlands in the entire Great Lakes Basin. We evaluated classification results of wetlands, uplands, and open water from May-September 2019, and tested whether SRTM elevation, slope, or the Dynamic Surface Water Extent produced the most accurate results in each Great Lake Basin in conjunction with optical indices and radar composites. We found that slope produced the most accurate classification in Lake Michigan, Huron, Superior, and Ontario, while elevation performed best in Lake Erie. Classification results averaged 86.2% overall accuracy, 70.0% wetland consumer's accuracy, and 82.7% wetland producer's accuracy across the Great Lakes Basin. WET leverages cloud-computing for multisource processing of moderate resolution remote sensing data, and employs a user interface in Google Earth Engine that wetland managers and conservationists can use to monitor wetland extent in the Great Lakes Basin in near real-time.

Abstract Monitoring of subgrade settlement in railway and highway helps to maintain traffic safety and reduce infrastructure losses. When an excess subgrade settlement is detected, maintenances of the unqualified subgrade will be processed in time, and early warning of constructions will be processed to prevent the occurrence of safety accident. In this article, a noncontact, real-time, and unattended system based on optical method is introduced to monitor the subgrade settlement remotely. Several high-power infrared LED targets are mounted on the subgrade and they will move as the subgrade subsides. The movements of the LED targets are detected by an optical detection device based on the principle of optical imaging. The displacement data is remotely transmitted to a server for analysis via GPRS. The remote monitoring system has been applied to the monitoring of subgrade settlement of Zhensheng highway in Guizhou province of China in 2014. This technology helped to detect the subgrade settlement that was caused by the construction of Beiyinpo railway tunnel. Four test points on road surface were monitored at a sampling rate every 20 minutes. The effectiveness of the system has been verified by the practical application.

Infrared sensors have been deployed in many video surveillance systems because of the insensibility of their imaging procedure to some extreme conditions (e.g. low illumination condition, dim environment). To reduce human labor in video monitoring and perform intelligent infrared video understanding, an important issue we need to consider is how to locate the object of interest in consecutive video frames accurately. Therefore, developing a robust object tracking algorithm for infrared videos is necessary. However, the infrared information may not be reliable (e.g. thermal crossover), and appearance modeling with only the infrared modality may not be able to achieve good results. To address these issues, with the wide deployment of RGB-infrared camera systems, this paper proposes an infrared tracking framework in which information from RGB-modality will be exploited to assist the infrared object tracking. Specifically, within the tracking framework, in order to deal with the contaminated features caused by large appearance variations, an online non-negative feature template learning model is designed. The non-negative constraint enables the model to capture the local part-based characteristic of the target appearance. To ensure more important modality contribute more in appearance representation, an adaptive modality importance weight learning scheme is also incorporated in the proposed feature learning model. To guarantee the model optimality, an iterative optimization algorithm is derived. The experimental results on various RGB-infrared videos show the effectiveness of the proposed method.

High-speed optical reconstruction of 3D-scenes can be achieved using digital holography with binary digital micromirror devices (DMD) or a ferroelectric spatial light modulator (fSLM). There are many algorithms for binarizing digital holograms. The most common are methods based on global and local thresholding and error diffusion techniques. In addition, hologram binarization is used in optical encryption, data compression, beam shaping, 3D-displays, nanofabrication, materials characterization, etc. This paper proposes an adaptive binarization method based on a combination of local threshold processing, hologram division into blocks, and error diffusion procedure (the LDE method). The method is applied for binarization of optically recorded and computer-generated digital holograms of flat objects and three-dimensional scenes. The quality of reconstructed images was compared with different methods of error diffusion and thresholding. Image reconstruction quality was up to 22% higher by various metrics than that one for standard binarization methods. The optical hologram reconstruction using DMD confirms the results of the numerical simulations.

Monitoring of subgrade settlement in railway and highway helps to maintain traffic safety and reduce infrastructure losses. When an excess subgrade settlement is detected, maintenances of the unqualified subgrade will be processed in time, and early warning of constructions will be processed to prevent the occurrence of safety accident. In this article, a noncontact, real-time, and unattended system based on optical method is introduced to monitor the subgrade settlement remotely. Several high-power infrared LED targets are mounted on the subgrade and they will move as the subgrade subsides. The movements of the LED targets are detected by an optical detection device based on the principle of optical imaging. The displacement data is remotely transmitted to a server for analysis via GPRS. The remote monitoring system has been applied to the monitoring of subgrade settlement of Zhensheng highway in Guizhou province of China in 2014. This technology helped to detect the subgrade settlement that was caused by the construction of Beiyinpo railway tunnel. Four test points on road surface were monitored at a sampling rate every 20 minutes. The effectiveness of the system has been verified by the practical application. [ABSTRACT FROM AUTHOR]

Nowadays coastline extraction and tracking of its changes become of high importance because of the climate change, global warming and rapid growth of human population. Coastal areas play a significant role for the economy of the entire region. In this paper we propose a new methodology for automatic extraction of the coastline using aerial images. A combination of a four step algorithm is used to extract the coastline in a robust and generalizable way. First, noise distortion is reduced in order to ameliorate the input data for the next processing steps. Then, the image is segmented into two regions, land and sea, through the application of a local threshold to create the binary image. The result is further processed by morphological operators with the aim that small objects are being eliminated and only the objects of interest are preserved. Finally, we perform edge detection and active contours fitting in order to extract and model the coastline. These algorithmic steps are illustrated through examples, which demonstrate the efficacy of the proposed methodology. [ABSTRACT FROM AUTHOR]

Phase correlation (PC) is a well-established image matching algorithm. It is robust to the variation of image contrast and brightness, but whether it is invariant to local illumination change, particularly sun angle change, is yet to be investigated. This paper presents our study to prove and demonstrate the robustness of illumination invariance of the PC algorithm via mathematical analysis and image matching experiments. First, a 3-D space named slope-aspect-intensity (SAI) is proposed to characterize the 3-D relationship between image intensity and terrain slope/aspect angles for a given illumination geometry. Based on the SAI space, the mathematical relationship between PC cross-power spectra and illumination direction (e.g., solar azimuth and zenith angles) is analyzed. The impact of illumination angle variation between two images for matching is then rigorously investigated via experiments using simulated terrain shading images from a digital elevation model and real optical images taken under different sun illumination conditions. Our study confirms that PC is robustly invariant to illumination and therefore can achieve reliable matching between images taken under different solar illumination conditions for various remote sensing applications. [ABSTRACT FROM PUBLISHER]

A large proportion of individuals who live with type-2 diabetes suffer from plantar sensory neuropathy. Regular testing and assessment for the condition is required to avoid ulceration or other damage to patient's feet. Currently accepted practice involves a trained clinician testing a patient's feet manually with a hand-held nylon monofilament probe. The procedure is time consuming, labor intensive, requires special training, is prone to error, and repeatability is difficult. With the vast increase in type-2 diabetes, the number of plantar sensory neuropathy sufferers has already grown to such an extent as to make a traditional manual test problematic. This paper presents the first investigation of a novel approach to automatically identify the pressure points on a given patient's foot for the examination of sensory neuropathy via optical image processing incorporating plantar anthropometry. The method automatically selects suitable test points on the plantar surface that correspond to those repeatedly chosen by a trained podiatrist. The proposed system automatically identifies the specific pressure points at different locations, namely the toe (hallux), metatarsal heads and heel (Calcaneum) areas. The approach is generic and has shown 100% reliability on the available database used. The database consists of Chinese, Asian, African, and Caucasian foot images. [ABSTRACT FROM AUTHOR]

In this paper, we present a new method for detecting in-plane displacements in microelectromechanical systems (MEMS) with an unprecedented sub-ångström accuracy. We use a curve-fitting method that is commonly employed in spectroscopy to find peak positions in a spectrum. We fit a function to the intensity profile of the image of a silicon beam that was captured with a CCD camera on an optical microscope. The position resolution depends on the amount of pixel noise and on how the moving feature is spread across the detector pixels. The resolution is usually limited by photon shot noise, which can be controlled and lowered in several ways. To demonstrate the technique we measure the adhesion snap-off of two silicon surfaces. We assess the accuracy of the technique using two different silicon MEMS devices and an experimental ultrananocrystalline diamond device. The lowest position noise that we report is obtained by summing 1 577 image lines and is as low as 60 pm root mean square. [2014-0040] [ABSTRACT FROM AUTHOR]

adjacent effect caused by a nonideal system point spread function (PSF) is an important source generating additional errors in radiometric measurements. Traditional recovery methods, i.e., inverse and Wiener filters, are attempted to restore the scene without the influence of RSF, which is inexistent in real observations and usually leads to the ringing artifact as well as uncertainties in noise amplification (NA). In this paper, a novel spatial-response matched filter (SRMF) and its processing method are established, where the aimed scene is supposed to be observed by instruments with higher PSF performance. Also, NA effects triggered by SRMF are precisely modeled and used for the further evaluation of radiometric accuracy (RA) variation after SRMF processing. Simulation results indicate that the SRMF method is more suitable for images with lower noise level and greater variation in the targets' radiation, where RA performance could be improved in a certain extent. Meanwhile, when the imager in a Multifunctional Transport Satellite is selected as the reference one with high-performance PSF, the SRMF for Fengyun-2 satellite is set up and applied for observations in thermal infrared band during April andMay 2013. After SRMF processing, the recovered images show significant improvements in both visual effects and RA, where the increase of RA is expected to be 2-6 K at 190 K in statistics. Such a progress is believed to be beneficial to tropical cyclone intensity estimation as well as for other relevant products, i.e., cloud classification generation. [ABSTRACT FROM AUTHOR]

Finding a correlation between image pixel information and non-aqueous phase liquid (NAPL) saturation is an important issue in bench-scale geo-environmental model studies that employ optical imaging techniques. Another concern is determining the best dye color and its optimum concentration as a tracer for use in mapping NAPL zones. Most bench scale flow studies employ monochromatic gray-scale imaging to analyze the concentration of mostly red dyed NAPL tracers in porous media. However, the use of grayscale utilizes a third of the available information in color images, which typically contain three color-space components. In this study, eight color spaces consisting of 24 color-space components were calibrated against dye concentration for three color-dyes. Additionally, multiple color space components were combined to increase the correlation between color-space data and dyed NAPL concentration. This work is performed to support imaging of NAPL migration in transparent synthetic soils representing the macroscopic behavior of natural soils. The transparent soil used in this study consists of fused quartz and a matched refractive index mineral-oil solution that represents the natural aquifer. The objective is to determine the best color dye concentration and ideal color space components for rendering dyed sucrose-saturated fused quartz that represents contamination of the natural aquifer by a dense NAPL (DNAPL). Calibration was achieved for six NAPL zone lengths using 3456 images (24 color space components×3 dyes×48 NAPL combinations) of contaminants within a defined criteria expressed as peak signal to noise ratio. The effect of data filtering was also considered and a convolution average filter is recommended for image conditioning. The technology presented in this paper is fast, accurate, non-intrusive and inexpensive method for quantifying contamination zones using transparent soil models. [ABSTRACT FROM AUTHOR]

Shadows in remote sensing images often cause problems, especially in land-cover classification and change detection. Hence, it is very useful if the radiance of shadow areas is corrected to the same level as that of shadow-free areas. In this study, a radiance measurement was carried out to investigate the spectral characteristics of sunlight. Then a method is proposed for shadow detection and correction of optical imagery. First, building shadow areas are detected using an object-based classification method that employs brightness values and their relationship with the neighboring area. Next, the detected shadow areas are corrected using a linear function to produce a shadow-free image. The shadow pixels with different darkness levels are corrected by using different ratios to obtain a smoothly restored image. The proposed semi-automated method was applied to a QuickBird and a WorldView-2 images of Tokyo, Japan, to demonstrate the effectiveness of the method. [ABSTRACT FROM PUBLISHER]

This paper presents an experimental study using laser mouse sensors for the contactless revolutions per minute (RPM) measurement of a rotating shaft. The sensor performance characterization experiment is firstly conducted under different parameter setups. After the optimal parameter value has been found, the rotor RPM experiment is then conducted with a speed sweep from 500 to 3800 rpm, and data are gathered at 30 different speeds and processed using two different methods to convert the sensor readings into the RPM of the rotating shaft; the results are then displayed. The performance differences between the two methods are compared, and the observation is that both the linearity and the signal-to-noise ratio of the frequency correlation method are several times better than those for the amplitude correlation method. The conclusion summarizes the experimental results and the advantage associated with this new RPM sensing method and provides the motivation for its potential applications and its future works. [ABSTRACT FROM PUBLISHER]

Ever-expanding networks of surf cameras offer a unique opportunity to monitor the coastline over large expanses at very little cost compared to traditional in situ survey methods. Here, we describe and test a new coastal monitoring system maintained by CoastalCOMS Pty Ltd. at their test site at Gold Coast, Australia. The two-camera system monitors two highly sensitive 4-km stretches of sandy coastline adjacent to high-value assets. The traditional static multi-camera setup has been replaced by a single rotational camera. A 14-month data set, encompassing one major storm, a recovery period, and a seasonal cycle, was analyzed. Positive shoreline detections using the new camera system were available 64% of the time (roughly 145 days of the available 226, where daily offshore significant wave heights Hs ≤ 1 m). Comparison of the CoastalCOMS-derived shorelines and in situ survey data showed a mean shoreward bias of 25.5 m. Daily shoreline estimates were used to calculate weekly and five-week running mean beach widths at both sites. Analysis showed that both sites eroded between 15-22 m during the May 2009 storm and then recovered during the proceeding five-month calm period. Distinct intersite variability was observed between the more exposed Northern Beaches that displayed an annual shoreline cycle and very little intrasite variability and the more sheltered southern Palm Beach site that displayed large intrasite spatial variability and sensitivity to changes to both wave direction and wave height. [ABSTRACT FROM AUTHOR]

The nonlinear response of light transmission characteristics of a liquid crystal (LC) spatial light modulator (SLM) is studied. The results show that the device exhibits a wide range of variations with different control parameters and input settings. Experiments were performed to obtain intensity modulation that is best described by either power-law or sigmoidal functions. Based on the inverse transformation, an appropriate pre-processing scheme for electrically addressed input gray-scale images, particularly important in several optical processing and imaging applications, is suggested. Further, the necessity to compensate the SLM image nonlinearities in a volume holographic data storage and retrieval system is demonstrated. [ABSTRACT FROM AUTHOR]

Spatial characteristics of thermal infrared (TIR) images are generally described by image-derived point spread function (IDPSF) for sampled imaging system. Most investigators working on IDPSF on-orbit evaluation focus on visible and near-infrared images and pay little attention on TIR ones, the reason of which lies on the fact that the assumption of an observed target with an ideal step profile is not suitable for TIR band. A new approach for TIR images is proposed here based on a more common slope-profile model, where the transition features, i.e., the sample number (Nslope) and the beginning position of slope interval, are extracted from the observed signals. Simulation results show that the estimation performance will be increased, with lower noise level as well as higher system modulation transfer function value at half of normalized spatial frequency (MTF0.5) or smaller Nslope. For a good performance system with noise squared-root variance (SRV) nomore than 1.0 count and MTF0.5 between 0.3 and 0.4, the relative error between simulated and estimated MTF0.5's is less than 20%. The IDPSF of FY-2C satellite TIR band is estimated, and the derived MTF0.5 is properly close to on-ground testing results. The image quality of FY-2C TIR band has been improved by using Wiener filtering and the estimated IDPSF, which eventually benefits the cloud detection product with more detectable low fractus and increased detection accuracy by about 1% in winter. Considering a step-profile target as a special case of this model, the proposed approach is also suitable for visible and near-infrared images. [ABSTRACT FROM AUTHOR]

We investigated the use of an analog self-electrooptic effect device based on GaAs multiple quantum wells as an image processor chip to early vision processes. We focused our attention at the edge detection process using layouts of photo detectors to extract Laplacians of images. Laplacians of linear, constant, step, and Gaussian intensity profiles agree with the expected values. These results show that the device works properly as an edge detector. [ABSTRACT FROM AUTHOR]

In this paper, we examine the accuracy of manifold coordinate representations as a reduced representation of a hyperspectral imagery (HSI) lookup table (LUT) for bathymetry retrieval. We also explore on a more limited basis the potential for using these coordinates for modeling other in water properties. Manifold coordinates, are chosen because they are a data-driven intrinsic set of coordinates, which naturally parameterize nonlinearities that are present in HSI of water scenes. The approach is based on the extraction of a reduced dimensionality representation in manifold coordinates of a sufficiently large representative set of HSI. The manifold coordinates are derived from a scalable version of the isometric mapping algorithm. In the present and in our earlier works, these coordinates were used to establish an interpolating LUT for bathymetric retrieval by associating the representative data with ground truth data, in this case from a Light Detection and Ranging (LIDAR) estimate in the representative area. While not the focus of the present paper, the compression of LUTs could also be applied, in principle, to LUTs generated by forward radiative transfer models, and some preliminary work in this regard confirms the potential utility for this application. In this paper, we analyze the approach using data acquired by the Portable Hyperspectral Imager for Low-Light Spectroscopy (PHILLS) hyperspectral camera over the Indian River Lagoon, Florida, in 2004. Within a few months of the PHILLS overflights, Scanning Hydrographic Operational Airborne LIDAR Survey, LIDAR data were obtained for a portion of this study area, principally covering the beach zone and, in some instances, portions of contiguous river channels. Results demonstrate that significant compression of the LUTs is possible with little loss in retrieval accuracy. [ABSTRACT FROM AUTHOR]

High-speed optical reconstruction of 3D-scenes can be achieved using digital holography with binary digital micromirror devices (DMD) or a ferroelectric spatial light modulator (fSLM). There are many algorithms for binarizing digital holograms. The most common are methods based on global and local thresholding and error diffusion techniques. In addition, hologram binarization is used in optical encryption, data compression, beam shaping, 3D-displays, nanofabrication, materials characterization, etc. This paper proposes an adaptive binarization method based on a combination of local threshold processing, hologram division into blocks, and error diffusion procedure (the LDE method). The method is applied for binarization of optically recorded and computer-generated digital holograms of flat objects and three-dimensional scenes. The quality of reconstructed images was compared with different methods of error diffusion and thresholding. Image reconstruction quality was up to 22% higher by various metrics than that one for standard binarization methods. The optical hologram reconstruction using DMD confirms the results of the numerical simulations. [ABSTRACT FROM AUTHOR]

The images recorded by optical telescopes are often degraded by aberrations that induce phase variations in the pupil plane. Several wavefront sensing techniques have been proposed to estimate aberrated phases. One of them is phase diversity, for which the joint least-square approach introduced by Gonsalves et al. is a reference method to estimate phase coefficients from the recorded images. In this paper, we rely on the asymptotic theory of Toeplitz matrices to show that Gonsalves' technique provides a consistent phase estimator as the size of the images grows. No comparable result is yielded by the classical joint maximum likelihood interpretation (e.g., as found in the work by Paxman et at). Finally, our theoretical analysis is illustrated through simulated problems. [ABSTRACT FROM AUTHOR]

In vivo fluorescence molecular imaging and tomography has facilitated monitoring of genomics and proteomics over time and on the same animal. A highly important issue, however, has been the robust registration of animals imaged at different time points to obtain accurate description of activity and location. This paper presents a method for aligning temporal data of small animals based on surface anatomical features and improving the accuracy of monitoring fluorophore distribution. The method can account for differences in the positioning and compression of small animals and can be extended to three-dimensional as well as to other imaging modalities. [ABSTRACT FROM AUTHOR]

An efficient, physics-based remote bathymetry method for the littoral zone is described and illustrated with applications to QuickBird, Littoral Airborne Sensor: Hyper- spectral (LASH), and Airborne Visible/Infrared Spectrometer (AVIRIS) spectral imagery. The method combines atmospheric correction, water reflectance spectral simulations, and a linear unmixing bathymetry algorithm that accounts for water surface reflections, thin clouds, and variable bottom brightness, and can incorporate blends of bottom materials. Results include depth maps, bottom color visualizations, and in favorable cases, approximate descriptions of the water composition. In addition, atmospheric correction was advanced through new capabilities added to the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) and Moderate Resolution Transmittance (MODTRAN) codes, including characterization of the aerosol wavelength dependence and a discrete-ordinate-method radiative transfer scaling technique for rapid calculation of multiply scattered radiance. [ABSTRACT FROM AUTHOR]