This PDF file contains the front matter associated with SPIE Proceedings Volume 9976 including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

The Hyperspectral Thermal Emission Spectrometer (HyTES) is an airborne sensor capable of detecting trace gas emissions from sources on the ground. These sources may be a product of some process such as generating electricity or they could be unintentional leaks from pipelines or gas storage facilities. Emissions such as methane (CH4), hydrogen sulfide (H2S) and ammonia (NH3) are within its demonstrated capability. It typically flies low to the ground (altitude of 1000 to 1200m) in a Twin Otter Aircraft and captures a wide swath nearly 1km wide. Its spatial resolution is a few meters which is sharp enough to capture roof tops, large trucks, pipelines and other relevant features. It doesn’t rely on the sun for observations so could fly both nighttime and daytime campaigns. Recently, HyTES flew over the Southern California Gas leak in Porter Ranch, California. Its capability was in full display with large, overflowing plumes emanating from numerous sources. We will discuss the instrument, calibration techniques, lessons learned and plume retrievals.

The Aerospace Corporation’s sensitive Mako thermal infrared imaging spectrometer, which operates between 7.6 and 13.2 microns at a spectral sampling of 44 nm, and flies in a DeHavilland DHC-6 Twin Otter, has undergone significant changes over the past year that have greatly increased its performance. A comprehensive overhaul of its electronics has enabled frame rates up to 3255 Hz and noise reductions bringing it close to background-limited. A replacement diffraction grating whose peak efficiency was tuned to shorter wavelength, coupled with new AR coatings on certain key optics, has improved the performance at the short wavelength end by a factor of 3, resulting in better sensitivity for methane detection, for example. The faster frame rate has expanded the variety of different scan schemes that are possible, including multi-look scans in which even sizeable target areas can be scanned multiple times during a single overpass. Off-nadir scanning to ±56.4° degrees has also been demonstrated, providing an area scan rate of 33 km²/minute for a 2-meter ground sampling distance (GSD) at nadir. The sensor achieves a Noise Equivalent Spectral Radiance (NESR) of better than 0.6 microflicks (μf, 10^-6 W/sr/cm²/μm) in each of the 128 spectral channels for a typical airborne dataset in which 4 frames are co-added. An additional improvement is the integration of a new commercial 3D stabilization mount which is significantly better at compensating for aircraft motions and thereby maintains scan performance under quite turbulent flying conditions. The new sensor performance and capabilities are illustrated.

The Compact Wide Swath Imaging Spectrometer (CWIS) is a pushbroom imaging spectrometer for the solar reflected spectrum (380-2510 nm) with wide swath (1600/1280 elements), fast optical speed (F/1.8), and high uniformity (≥95%). CWIS is currently being tested at the Jet Propulsion Laboratory and is intended to address the need for high signal-to-noise ratio (SNR) compact imaging spectrometer systems for the visible to short wave infrared wavelength (VSWIR) range. We give an overview of the instrument functionality, describe the spectrometer alignment and system integration and report laboratory data that include spatial, spectral and radiometric calibration.

The Littrow form of spectrograph has a long and storied history in astronomical spectroscopy since its presentation in 1862 by Otto von Littrow. Light from an input slit traverses the same optical elements in reaching the dispersing element (prism or grating) and returning to a focused, dispersed image at the focal plane. This 1:1 symmetry helps cancel aberrations in the reimaging optics while presenting the dispersing element with the geometry most favorable to dispersion, efficiency and anamorphic scale change. Historically, Littrow spectrographs have not been pushed to high throughputs (fast f/ratios). However in the short- and mid-wave infrared particularly, high index, low dispersion materials like silicon and germanium can be combined effectively into compact, high throughput (<f/2.5), well corrected 1:1 reimaging systems that economize volume and cooling resources and are well-suited for moderately high resolution spectrographic space missions such as atmospheric sounders. We present some high throughput Littrow spectrograph concepts designed for infrared atmospheric sounding missions and incorporating both plane and immersion gratings.

Preliminary testing of a three-slit prism-based spectrometer is presented to test means of exploiting data from a multi-slit spectrometer as well as some potential ways of dealing with complications that arise when using multiple slits. When using a multiple slit spectrometer to boost SNR there are two primary concerns: first, the spectral axis of each slit must be nearly identical to effective average and second, the image from each slit must be well-registered. Based on some of these complications it seems, given the current technology, the best operational mode is to use the sensor to increase area coverage.

Scientific consensus from a 2015 pre-Decadal Survey workshop highlighted the essential need for a wide-swath (mapping) low earth orbit (LEO) instrument delivering carbon dioxide (CO₂), methane (CH₄), and carbon monoxide (CO) measurements with global coverage. OCO-2 pioneered space-based CO₂ remote sensing, but lacks the CH₄, CO and mapping capabilities required for an improved understanding of the global carbon cycle. The Carbon Balance Observatory (CARBO) advances key technologies to enable high-performance, cost-effective solutions for a space-based carbon-climate observing system. CARBO is a compact, modular, 15-30° field of view spectrometer that delivers high-precision CO₂, CH₄, CO and solar induced chlorophyll fluorescence (SIF) data with weekly global coverage from LEO. CARBO employs innovative immersion grating technologies to achieve diffraction-limited performance with OCO-like spatial (2x2 km²) and spectral (λ/Δλ ≈ 20,000) resolution in a package that is >50% smaller, lighter and more cost-effective. CARBO delivers a 25- to 50-fold increase in spatial coverage compared to OCO-2 with no loss of detection sensitivity. Individual CARBO modules weigh < 20 kg, opening diverse new space-based platform opportunities.

This paper presents best practices for modeling the resolution of dispersive imaging spectrometers. The differences between sampling, width, and resolution are discussed. It is proposed that the spectral imaging community adopt a standard definition for resolution as the full-width at half maximum (FHWM) of the total line spread function (LSF). Resolution should be computed for each of the spectral, cross-scan spatial, and along-scan spatial/temporal dimensions separately. A physical optics resolution model is presented that incorporates the effects of slit diffraction and partial coherence, the result of which is a narrower slit image width and reduced throughput.

In present paper, an ability to differentiate basal cell carcinoma (BCC) and healthy skin by combining multi-spectral autofluorescence imaging, principle component analysis (PCA), and linear discriminant analysis (LDA) has been demonstrated. For this purpose, the experimental setup, which includes excitation and detection branches, has been assembled. The excitation branch utilizes a mercury arc lamp equipped with a 365-nm narrow-linewidth excitation filter, a beam homogenizer, and a mechanical chopper. The detection branch employs a set of bandpass filters with the central wavelength of spectral transparency of λ = 400, 450, 500, and 550 nm, and a digital camera. The setup has been used to study three samples of freshly excised BCC. PCA and LDA have been implemented to analyze the data of multi-spectral fluorescence imaging. Observed results of this pilot study highlight the advantages of proposed imaging technique for skin cancer diagnosis.

Raman scattering microscopy is a powerful imaging technique used to identify chemical composition, structural and conformational state of molecules of complex samples in biology, biophysics, medicine and materials science. In this work, we have shown that Raman techniques allow the measurement of the iron content in protein mixtures and cells. Since the mechanisms of iron acquisition, storage, and excretion by cells are not completely understood, improved knowledge of iron metabolism can offer insight into many diseases in which iron plays a role in the pathogenic process, such as diabetes, neurodegenerative diseases, cancer, and metabolic syndrome. Understanding of the processes involved in cellular iron metabolism will improve our knowledge of cell functioning. It will also have a big impact on treatment of diseases caused by iron deficiency (anemias) and iron overload (hereditary hemochromatosis). Previously, Raman studies have shown substantial differences in spectra of transferrin with and without bound iron, thus proving that it is an appropriate technique to determine the levels of bound iron in the protein mixture. We have extended these studies to obtain hyperspectral images of transferrin in cells. By employing a Raman scanning microscope together with spectral detection by a highly sensitive back-illuminated cooled CCD camera, we were able to rapidly acquire and process images of fixed cells with chemical selectivity. We discuss and compare various methods of hyperspectral Raman image analysis and demonstrate the use of these methods to characterize cellular iron content without the need for dye labeling.

Effective hyperspectral thermal infrared imaging requires accurate atmospheric compensation to convert the measured at-sensor radiance to the ground radiance. The ground radiance consists of the thermal emission of the material and the reflected downwelling radiance. An accurate estimate of the downwelling radiance is required for temperature-emissivity separation (TES) to remove the spectrally sharp reflected atmospheric effects and retrieve a smooth and accurate material emissivity to use for detection. Determination of the downwelling radiance is difficult due to the fact that a down-looking sensor has no knowledge of the atmospheric properties above its line of sight. As the sensor altitude increases and more of the atmospheric emitters are below the sensor, a relationship forms between the upwelling and downwelling radiances. This relationship comes at the expense of increased pixel size, which increases the likelihood of mixed pixels and nonlinear spectral mixing. In this paper improvements to methods used to estimate the downwelling radiance of low altitude collections are proposed. The ground radiances of reflective pixels are used to estimate the atmosphere above the sensor. The reflective pixels are identified from their sharp atmospheric spectral features. Using the assumption that emissivity spectra are smooth across the narrow reflected atmospheric downwelling radiance features, the temperatures and emissivities are then separated for the reflective pixels using a look-up-table of downwelling radiances. The downwelling radiance that provides the best overall fit for the reflective pixels is then chosen as the scene downwelling radiance.

The HT-FRTC is a principal component based fast radiative transfer code that can be used across the electromagnetic spectrum from the microwave through to the ultraviolet to calculate transmittance, radiance and flux spectra. The principal components cover the spectrum at a very high spectral resolution, which allows very fast line-by-line, hyperspectral and broadband simulations for satellite-based, airborne and ground-based sensors. The principal components are derived during a code training phase from line-by-line simulations for a diverse set of atmosphere and surface conditions. The derived principal components are sensor independent, i.e. no extra training is required to include additional sensors. During the training phase we also derive the predictors which are required by the fast radiative transfer code to determine the principal component scores from the monochromatic radiances (or fluxes, transmittances). These predictors are calculated for each training profile at a small number of frequencies, which are selected by a k-means cluster algorithm during the training phase. Until recently the predictors were calculated using a linear regression. However, during a recent rewrite of the code the linear regression was replaced by a Gaussian Process (GP) regression which resulted in a significant increase in accuracy when compared to the linear regression. The HT-FRTC has been trained with a large variety of gases, surface properties and scatterers. Rayleigh scattering as well as scattering by frozen/liquid clouds, hydrometeors and aerosols have all been included. The scattering phase function can be fully accounted for by an integrated line-by-line version of the Edwards-Slingo spherical harmonics radiation code or approximately by a modification to the extinction (Chou scaling).

For every possible mixture of clouds and ocean in WorldView-2 8-band data, we construct an anomaly detector (called a “clairvoyant” because we never know which mixture is appropriate in any given pixel). Then we combine these using a fusion technique. The usual method of deriving an analytic expression describing the envelope of all the clairvoyants’ decision boundaries is not possible. Instead, we compute the intersections of infinitesimally close boundaries generated by differential changes in the mixing fraction. When glued together, these 6-dimensional hyperstrings constitute the desired 7-dimensional decision boundary of the fused anomaly detector. However, no closed-form solution exists for the fused result. Therefore, we construct an approximation to the fused detection boundary by first flattening the strings into 6-dimensional hyperplanes and then gluing them together à la 3D printing.

Hyperspectral sensors operating in the long wave infrared (LWIR) have a wealth of applications including remote material identification and rare target detection. While statistical models for modeling surface reflectance in visible and near-infrared regimes have been well studied, models for the temperature and emissivity in the LWIR have not been rigorously investigated. In this paper, we investigate modeling hyperspectral LWIR data using a statistical mixture model for the emissivity and surface temperature. Statistical models for the surface parameters can be used to simulate surface radiances and at-sensor radiance which drives the variability of measured radiance and ultimately the performance of signal processing algorithms. Thus, having models that adequately capture data variation is extremely important for studying performance trades. The purpose of this paper is twofold. First, we study the validity of this model using real hyperspectral data, and compare the relative variability of hyperspectral data in the LWIR and visible and near-infrared (VNIR) regimes. Second, we illustrate how materials that are easily distinguished in the VNIR, may be difficult to separate when imaged in the LWIR.

This paper revisits hyperspectral data collected from the SpecTIR hyperspectral airborne Rochester Experiment (SHARE) in 2010. It has been determined that there were calibration issues in the SWIR portion of the data. This calibration issue is discussed and has been rectified. Approaches for calibration to radiance and compensation to reflectance are discussed based on in-scene information and radiative transfer codes. In addition to the entire flight line, a much large target detection test and evaluation chip has been created which includes an abundance of potential false alarms. New truth masks are created along with results from target detection algorithms. Co-registered LiDAR data is also presented. Finally, all ground truth information (ground photos, metadata, MODTRAN tape5, ASD ground spectral measurements, target truth masks, etc.), in addition to the HSI flight lines and co-registered LiDAR data, has been organized, packaged and uploaded to the Center for Imaging Science / Digital Imaging and Remote Sensing Lab web server for public use.

Over the past decade, manifold and graph representations of hyperspectral imagery (HSI) have been explored widely in HSI applications. Among many data-driven approaches to deriving manifold coordinate representations including Isometric Mapping (ISOMAP), Local Linear Embedding (LLE), Laplacian Eigenmaps (LE), and Diffusion Kernels (DK), ISOMAP is the only global method that well represents the large scale nonlinear geometric structure of the data. In recent years, methods such as ENH-ISOMAP as well as its parallel computing accelerations makes ISOMAP practical for hyperspectral image dimensionality reduction. However, the noise problem in these methods has not been well addressed, which is critical to classification accuracy based on the manifold coordinates derived from these methods. While standard linear techniques to reduce the effects of noise can be applied as a preliminary step, these are based on global statistics and are applied globally across the entire data set, resulting in the risk of losing subtle nonlinear features before classification. To solve this problem, in this paper, we explore several approaches to modeling and mitigating noise in HSI in a local sense to improve the performance of the ENH-ISOMAP algorithm, aiming to reduce the noise effect on the manifold representations of the HSI. A new method to split data into local spectral subsets is introduced. Based on the local spectral subsets obtained with this method, a local noise model guided landmark selection scheme is proposed. In addition, a new robust adaptive neighborhood method using intrinsic dimensionality information to construct the k-Nearest Neighbor graph is introduced to increase the fidelity of the graph, based on the same framework of local spectral subsetting. The improved algorithm produces manifold coordinates with less noise, and shows a better classification accuracy using k-Nearest Neighbor classifier.

Multispectral and hyperspectral imaging can facilitate vehicle tracking across a series of images by gathering spectral information that distinguishes the vehicle of interest from confusers. Developing effective algorithms for utilizing this information requires an understanding of the sources and nature of both the common and unique components in vehicle spectra, as well as the variations associated with lighting, view angle, and part of the vehicle being observed. In this study, focusing on the VNIR-SWIR spectral region, we analyze hyperspectral data from a recent field experiment at the Rochester Institute of Technology. We describe the spectra of painted vehicle surfaces in general terms, and demonstrate effective classification of automobiles based on spectra from upward facing surfaces (the roof, hood or trunk) using a method that combines the Support Vector Machine with data pre-conditioning.

This study investigates methods for characterizing materials that are mixtures of granular solids, or mixtures of liquids, which may be linear or non-linear. Linear mixtures of materials in a scene are often the result of areal mixing, where the pixel size of a sensor is relatively large so they contain patches of different materials within them. Non-linear mixtures are likely to occur with microscopic mixtures of solids, such as mixtures of powders, or mixtures of liquids, or wherever complex scattering of light occurs. This study considers two approaches for use as generalized methods for un-mixing pixels in a scene that may be linear or non-linear. One method is based on earlier studies that indicate non-linear mixtures in reflectance space are approximately linear in albedo space. This method converts reflectance to single-scattering albedo (SSA) according to Hapke theory assuming bidirectional scattering at nadir look angles and uses a constrained linear model on the computed albedo values. The other method is motivated by the same idea, but uses a kernel that seeks to capture the linear behavior of albedo in non-linear mixtures of materials. The behavior of the kernel method can be highly dependent on the value of a parameter, gamma, which provides flexibility for the kernel method to respond to both linear and non-linear phenomena. Our study pays particular attention to this parameter for responding to linear and non-linear mixtures. Laboratory experiments on both granular solids and liquid solutions are performed with scenes of hyperspectral data.

Weather and environmental satellites that collect earth imaging and scientific data are the ultimate source of “Big Data”. A 2004 study conducted by a Committee on Environmental Satellite Data Utilization organized by National Research Council (NRC) of National Academies coined the term “The Data Tsunami” to describe the increasing data rates and volumes of global satellite observations. Between 2004 and 2014, upward of 100 new satellites were launched with ever increasing sensing capabilities and concomitant increases in data and information volumes. NOAA, the US agency that plans, operates, collects, archives and distributes national weather and environmental satellite data has, to date, a total archive of more than 13,000 terabytes from year 2015 alone! According to another report from NOAA, NOAA’s digital archives have now grown to almost 20 times their 1999 volume at a growth rate of approximately 1.2 petabytes per year. Note that NOAA’s total archive, at the dawn of the 21st century, was then less than 1.2 petabytes. It has become obvious that knowing about “what to do with Big Data” will be a truly significant challenge. Furthermore, dramatic developments in earth imaging capacity are being delivered by ambitious startups that are launching large fleets of small, interconnected satellites. The number of earth observing imaging satellites nearly doubled during 2014 alone. With views into various aspects of global activity being refreshed at a faster rate and higher resolution than ever before, the rapid increase of this vast volume of imaging data is already changing how we use satellite Big Data in the business world. In this paper, I attempt to show how the emerging field of Big Data Analytics is relevant to the vast and growing volumes of weather and environmental satellite data. How are we to harvest valuable weather and environmental information from “The Big Data Tsunami” yielded by national and international satellite assets?

Using Landsat-7 ETM remote sensing data, the inversion of spectral reflectance of green wheat in visible and near infrared waveband in Yingke, China is studied. In order to solve the problem of lower inversion accuracy, custom atmospheric conditions method based on moderate resolution transmission model (MODTRAN) is put forward. Real atmospheric parameters are considered when adopting this method. The atmospheric radiative transfer theory to calculate atmospheric parameters is introduced first and then the inversion process of spectral reflectance is illustrated in detail. At last the inversion result is compared with simulated atmospheric conditions method which was a widely used method by previous researchers. The comparison shows that the inversion accuracy of this paper’s method is higher in all inversion bands; the inversed spectral reflectance curve by this paper’s method is more similar to the measured reflectance curve of wheat and better reflects the spectral reflectance characteristics of green plant which is very different from green artificial target. Thus, whether a green target is a plant or artificial target can be judged by reflectance inversion based on remote sensing image. This paper’s research is helpful for the judgment of green artificial target hidden in the greenery, which has a great significance on the precise strike of green camouflaged weapons in military field.

The detection limit of DNB was proposed as a function of the brightness temperature (BT) at 3.7 μm, where the transmittance of cloud could be observed as a change of surface temperature. The shortwave infrared band exhibited a wide distribution in BT more than the thermal infrared band for the same level of DNB radiance. The lights from surface were identified even under the full Moon condition with the proposed method, where clouds were reflecting the lunar lights. A different distribution of clouds for day to day and a change of the Moon phase with its elevation make this problem more complicated. But the approach of contrast based evaluation of surface lights and lunar reflected lights could be one solution to distinguish the lights from the surface. Currently, a validation is necessary in the future to confirm this algorithm and to validate the detected pixels to be fishing boats with the stable light sources. The time series data of fishing boats could be studied to analyze the region of fishing area relative to the distribution of sea surface temperature and/or chlorophyll-a.

Multi-temporal satellite images acquisition with different illumination conditions cause radiometric difference to have a huge effect on image quality during remote sensing image processing. In particular, image matching of satellite stereo images with great difference between acquisition dates is very difficult for the high-precision DSM generation in the field of satellite photogrammetry. Therefore, illumination normalization is one of the greatest application technology to eliminate radiometric difference for image matching and other image applications. In this paper, we proposed a novel method of object-based illumination normalization to improve image matching of different temporal satellite stereo images in urban area. Our proposed method include two main steps: 1) the object extraction 2) multi-level illumination normalization. Firstly, we proposed a object extraction method for the same objects extraction among the multi-temporal satellite images, which can keep the object structural attribute. Moreover, the multi-level illumination normalization is proposed by combining gradient domain method and singular value decomposition (SVD) according to characteristic information of relevant objects. Our proposed method has great improvement for the illumination of object area to be benefit for image matching in urban area with multiple objects. And the histogram similarity parameter and matching rate are used for illumination consistency quantitative evaluation. The experiments have been conducted on different satellite images with different acquisition dates in the same urban area to verify the effectiveness of our proposed method. The experimental results demonstrate a good performance by comparing other methods.

Hyperspectral data unmixing and target detection and classification are two important areas of hyperspectral data processing. This paper provides a seminal view on the relevance of four most widely used models in hyperspectral data unmixing. We describe the algorithms in summary and discuss the connections and differences of these four models. Unconstrained least squares unmixing (ULSU) is an earlier approach to estimate abundance fractions based on linear mixing models. Orthogonal subspace projection (OSP), Mixture-Tuned Matched Filtering Approach (MTMF) and Matched Filter with False Alarm Mitigation (MF-FAM) are developed for target detection at first but soon show their potential in hyperspectral data unmixing. Theoretically, LSMA without any constraints on the abundance fractions can estimate all abundance fractions for one time with the prior knowledge of entire signatures. OSP is able to gain abundance fractions and detect constituent materials for each pixel on the premise of the prior knowledge of desired and undesired signature. Each operation obtains each pixel result. By comparing, we find that an abundance-unconstrained LSMA has the same classification feature as OSP but with an extra constant. According to the first part MF of MTMF, the linear optimal signal detector of OSP is a background rejecter followed by a matched filter with the matched signal when the constant is equal to one. Both MTMF and MF-FAM firstly use matched filter to get MF scores. MTMF decomposes into two parts, the first part corresponds to MF, while the second part resembles MT. Both parts are based on projection. Versus the morphing space, the first part MF of MTMF can be taken as projection on the vertical axis, and MT are projected to the horizontal axis. MF-FAM also makes a vertical projection at first to obtain MF values, but it acquires false alarm mitigation by using probability density function of pixels from a statistical perspective.

Latent low-rank representation (LatLRR) has been attached considerable attention in the field of remote sensing image segmentation, due to its effectiveness in exploring the multiple subspace structures of data. However, the increasingly heterogeneous texture information in the high spatial resolution remote sensing images, leads to more severe interference of pixels in local neighborhood, and the LatLRR fails to capture the local complex structure information. Therefore, we present a local sparse structure constrainted latent low-rank representation (LSSLatLRR) segmentation method, which explicitly imposes the local sparse structure constraint on LatLRR to capture the intrinsic local structure in manifold structure feature subspaces. The whole segmentation framework can be viewed as two stages in cascade. In the first stage, we use the local histogram transform to extract the texture local histogram features (LHOG) at each pixel, which can efficiently capture the complex and micro-texture pattern. In the second stage, a local sparse structure (LSS) formulation is established on LHOG, which aims to preserve the local intrinsic structure and enhance the relationship between pixels having similar local characteristics. Meanwhile, by integrating the LSS and the LatLRR, we can efficiently capture the local sparse and low-rank structure in the mixture of feature subspace, and we adopt the subspace segmentation method to improve the segmentation accuracy. Experimental results on the remote sensing images with different spatial resolution show that, compared with three state-of-the-art image segmentation methods, the proposed method achieves more accurate segmentation results.

Hyperspectral images can realize crackajack features discriminability for supplying diagnostic characteristics with high spectral resolution. However, various degradations may generate negative influence on the spectral information, including water absorption, bands-continuous noise. On the other hand, the huge data volume and strong redundancy among spectrums produced intense demand on compressing HSIs in spectral dimension, which also leads to the loss of spectral information. The reconstruction of spectral diagnostic characteristics has irreplaceable significance for the subsequent application of HSIs. This paper introduces a spectrum restoration method for HSIs making use of segmented multi-Gaussian model (SMGM) and sparse representation. A SMGM is established to indicating the unsymmetrical spectral absorption and reflection characteristics, meanwhile, its rationality and sparse property are discussed. With the application of compressed sensing (CS) theory, we implement sparse representation to the SMGM. Then, the degraded and compressed HSIs can be reconstructed utilizing the uninjured or key bands. Finally, we take low rank matrix recovery (LRMR) algorithm for post processing to restore the spatial details. The proposed method was tested on the spectral data captured on the ground with artificial water absorption condition and an AVIRIS-HSI data set. The experimental results in terms of qualitative and quantitative assessments demonstrate that the effectiveness on recovering the spectral information from both degradations and loss compression. The spectral diagnostic characteristics and the spatial geometry feature are well preserved.

Photoacoustic imaging (PAI) involves both optical and ultrasound imaging, owing to this combination the system is capable of generating high resolution images with good penetration depth. With the growing applications of PAI in neurology, vascular biology, dermatology, ophthalmology, tissue engineering, angiogenesis etc., there is a need to make the system more compact, cheap and effective. Therefore we designed an economical and compact version of PAI systems by replacing expensive and sophisticated lasers with a robust pulsed laser diode of 905 nm wavelength. In this study, we determine the feasibility of the Photoacoustic imaging with a very low excitation energy of 0.1uJ in Photoacoustic microscopy. We developed a low cost portable Photoacoustic Imaging including microscopy (both reflection) Phantom study was performed in this configuration and also ex-vivo image was obtained from mouse skin.

Building extraction currently is important in the application of high-resolution remote sensing imagery. At present, quite a few algorithms are available for detecting building information, however, most of them still have some obvious disadvantages, such as the ignorance of spectral information, the contradiction between extraction rate and extraction accuracy. The purpose of this research is to develop an effective method to detect building information for Chinese GF-1 data. Firstly, the image preprocessing technique is used to normalize the image and image enhancement is used to highlight the useful information in the image. Secondly, multi-spectral information is analyzed. Subsequently, an improved morphological building index (IMBI) based on remote sensing imagery is proposed to get the candidate building objects. Furthermore, in order to refine building objects and further remove false objects, the post-processing (e.g., the shape features, the vegetation index and the water index) is employed. To validate the effectiveness of the proposed algorithm, the omission errors (OE), commission errors (CE), the overall accuracy (OA) and Kappa are used at final. The proposed method can not only effectively use spectral information and other basic features, but also avoid extracting excessive interference details from high-resolution remote sensing images. Compared to the original MBI algorithm, the proposed method reduces the OE by 33.14% .At the same time, the Kappa increase by 16.09%. In experiments, IMBI achieved satisfactory results and outperformed other algorithms in terms of both accuracies and visual inspection

The extraction of optical parameters of the atmosphere from the scanning-lidar signals is possible only for the horizontally stratified atmosphere. The significant issue this technique is that no reliable methods exist for determining whether the searched atmosphere is horizontally stratified, and if not, where the heterogeneous areas are located. In the paper the essence and specifics for practically locating such areas of heterogeneous, poorly stratified atmosphere, from which lidar data should be excluded from analysis, are considered. To apply the three-directional differential solution, the lidar backscatter signals measured under three elevation angles are selected and analyzed. The theoretical basis of this solution is delineated. The specifics inherent to this remote sensing method are illustrated by simulated and experimental lidar data.