This PDF file contains the front matter associated with SPIE Proceedings Volume 10607, including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

Color cast is a major problem of target detection and recognition base on image feature analysis. A color cast detection and correction algorithm for video-image in nature scene is proposed in this paper. To overcome the inaccurate detection in existed color cast detection method based on RGB space, the analysis method based on the color distribution characteristics of the equivalent circle in Lab space is proposed. In order to achieve better color correction results, the gray balance and perfect reflection method is combined to reproduce the actual color of the objects, especially a modified method for images with severe color deviation. Experimental results show that the proposed methods can detect the degree of color cast correctly, achieve better correction results, and meet the real-time requirements.

In this paper, a new kind of symmetrical aluminum (Al) nanotips structure based on localized rather traditional propagating surface plasmon polarizations (SPPs) focusing are designed and fabricated successfully. The simulation results about near-field distribution of electric field and reflectance calculations using finite-difference time-domain (FDTD) simulation theory are exhibited and then the device is fabricated mainly by coating Al films with the thickness of 100 nm on n-type doping silicon (Si), cutting into scale of 15mm×15mm by wafer dicing, electron beam lithography (EBL) exposure and ICP etching. The near-field focusing properties about small spot breaking the diffracting limitation with one order enhancement in the near-tip area of this structure are demonstrated experimentally using scanning nearfield optical microscopy (SNOM), and the comparisons to simulation results are analyzed, so as to reveal a potential application in capturing near-field focusing images quickly by applying exterior voltage signals based on our structure with nanotips.

In the extraction of Raman spectra, the signal will be affected by a variety of background noises, and then the effective information of Raman spectra is weakened or even submerged in noises, so the spectral analysis and denoising processing is very important. The traditional ensemble empirical mode decomposition (EEMD) method is to remove the noises by removing the IMF components that mainly contain the noises. However, it will lose some details of the Raman signal. For the problem of EEMD algorithm, the denoising method of smoothing filter combined with EEMD is proposed in this paper. First, EEMD is used to decompose the Raman noise signal into several IMF components. Then, the components mainly containing noises are selected using the self-correlation function, and the smoothing filter is used to remove the noises of the components. Finally, the sum of the denoised components is added with the remaining components to obtain the final denoised signal. The experimental results show that compared with the traditional denoising algorithm, the signal-to-noise ratio (SNR), the root mean square error (RMSE) and the correlation coefficient are significantly improved by using the proposed smoothing filter combined with EEMD.

In this paper, a new liquid-crystal microlens array (LCMLA) with patterned ring-electrode arrays (PREAs) is investigated, which has an ability to acquire multiple-mode two-dimensional images with better electrically tunable efficiency than common liquid-crystal devices. The new type of LCMLA can be used to overcome several remarkable disadvantage of conventional liquid-crystal microlens arrays switched and adjusted electrically by relatively complex mechanism. There are two layer electrodes in the LCMLA developed by us. The top electrode layer consists of PREAs with different featured diameter but the same center for each single cell, and the bottom is a plate electrode. When both electrode structures are driven independently by variable AC voltage signal, a gradient electric field distribution could be obtained, which can drive liquid-crystal molecules to reorient themselves along the gradient electric field shaped, so as to demonstrate a satisfactory refractive index distribution. The common experiments are carried out to validate the performances needed. As shown, the focal length of the LCMLA can be adjusted continuously according to the variable voltage signal applied. According to designing, the LCMLA will be integrated continuously with an image sensors to set up a camera with desired performances. The test results indicate that our camera based on the LCMLA can obtain distinct multiple-mode two-dimensional images under the condition of using relatively low driving signal voltage.

In this paper, we propose a new device composed of patterned sub-wavelength arrays to investigate surface plasmons (SPs) over sub-wavelength metal nano-structures. The device consists of silicon substrate and sub-wavelength patterns fabricated on a layer of aluminum film with nanometer thickness. Each sub-wavelength pattern formed in aluminum film is composed of a basic nano-square and twelve triangles for shaping single nano-pattern, which are uniformly distributed on the four sides of each square. Reflectance spectra and electric field distribution in infrared region are simulated. Numerical simulation results demonstrate that the device can efficiently lower its reflectance in infrared spectrum, and the response frequency can be controlled by only changing the device parameters such as square side length and then triangle vertex angle. Besides, the simulated electric field distribution of the device shows obviously field localization effect at the edges of aluminum film nano-structure. The electric filed around the tips of aluminum triangles is localized into sub-wavelength scale, so as to be beyond the common diffraction limitation. Our work will help to reveal the interesting properties of SPs device, and also bring new prospect of photonic device.

When lightwave passes through a metal thin film with a periodic subwavelength hole arrays structure, its transmittance is significantly improved in the partial band compared to other wavelength. Changing the size of the hole, the period or metal material, will make the transmission curve different. Here, we add a layer of dielectric material on the surface of the metal film, such as liquid crystal(LC), by controlling voltage on LC to change the refractive index of this layer, then we can change the transmission curve, and achieve using voltage to move the transmission curve. When there is need for polarization, the holes can be made of a rectangle whose length and width are different or other shapes, for different polarization state of the light, and the film will display different transmission characteristics.

The intense surface plasmons (SPs) can be generated by patterned metal nano-structure arrays, through coupling incident light onto the functioned metal surface, so as to construct highly constrained surface electromagnetic modes. Therefore, a localized micro-nano-field array with a highly compressed surface electron distribution, can also be shaped and even nano-focused over the surface, which will lead to a lot of special physical effects such as anti-reflection effect, and thus indicate many new potential applications in the field of nano-photonics and -optoelectronics. In this paper, several typical patterned sub-wavelength metal nano-structure arrays were designed according to the process, in which common silicon wafer was employed as the substrate material and aluminum as the metal film with different structural size and arrangement circle. In addition, by adjusting the dielectric constant of metal material appropriately, the power control effect on metallic nanostructure was simulated. The key properties such as the excitation intensity of the surface plasmons were studied by simulating the reflectivity characteristic curves and the electric field distribution of the nanostructure excited by incident infrared beams. It is found that the angle of corners, the arrangement cycle and the metal material properties of the patterned nano-structures can be utilized as key factors to control the excitation intensity of surface plasmons.

In this paper, a kind of electronically controlled liquid crystal microlens arrays (LCMAs) with non-uniform coil electrodes arrays (NCEAs) is presented. The focal length of the electronically controlled LCMAs can be easily adjusted by applying the appropriate AC signal. The structure of the LCMAs is designed as a NCE array, which can then produce non-uniform electric field to drive liquid crystal molecules. The top electrode is fabricated by depositing an indium-tinoxide (ITO) semiconductor transparent conductive film based on a non-uniform electrode coil, and the bottom electrode is a conventional plate electrode. Due to the design of non-uniform electrode coil array is small, in addition to the traditional lithography process, the etching process we used is dry etching (ICP etching). The simulation results show that, the focal length of the LCMAs with the NCEAs can be tuned easily by applying the appropriate AC signal.

Under the condition of existing intense turbulence, the object's wavefront may be severely distorted. So, the wavefront sensors based on the traditional microlens array (MLA) with a fixed focal length can not be used to measure the wavefront effectively. In order to obtain a larger measurement range and higher measurement accuracy, we propose a liquid-crystal microlens array (LCMLA) with needed ability of swing focus over the focal plane and further adjusting focal length, which is constructed by a dual patterned ITO electrodes. The main structure of the LCMLA is divided into two layers, which are made of glass substrate with ITO transparent electrodes. The top layer of each liquid-crystal microlens consists of four rectangular electrodes, and the bottom layer is a circular electrode. In common optical measurements performed, the operations are carried out such as adding the same signal voltage over four electrodes of each microlens to adjust the focal length of the lens cell and adding a signal voltage with different RMS amplitude to adjust the focus position on the focal plane. Experiments show that the LCMLA developed by us demonstrate a desired focal length adjustable function and dynamic swing ability, so as to indicate that the method can be used not only to measure wavefront but also correct the wavefront with strong distortion.

Surface plasmon polarizations (SPP) is a nano-scale photon control technology which can converge the spread of oscillation electron driven by incident light. In recent years, SPP has become an advanced research hotspot and has been studied more and more widely. The convergence effect of SPP has extensive applications, such as Schottky barrier detector in which the higher power hotspot, the lower signal-to-noise ratio. In this paper, studies have been done about the interaction of light and matter. Different geometric shapes have been simulated, which were obtained by graphic clipping. Via comparing the power of the hot spot and the minimum location on the transmittance line, we concluded the relationship of the interaction and the structure. It’s found that every absorption peak corresponds a mode of LSPP spread. Therefore, we can design figure to control the spread of the SPP, and achieve fantastic goal. Finally, a typical figure with high power hotspot was given.

Previous studies have presented the usefulness of typical liquid-crystal Fabry-Perot (LC-FP) infrared filters for spectral imaging detection. Yet, their infrared transmission performances still remain to improve or even rise. In this paper, we propose a new type of electrically tunable LC-FP infrared filter to solve the problem above. The key component of the device is a FP resonant cavity composed of two parallel plane mirrors, in which the zinc selenide (ZnSe) materials with a very high transmittance in the mid-long-wavelength infrared regions are used as the electrode substrates and a layer of nano-aluminum (Al) film, which is directly contacted with liquid-crystal materials, is chosen to make high reflective mirrors as well as the electrodes. Particularly, it should be noted that the directional layer made up of ployimide (PI) used previously is removed. The experiment results indicate that the filter can reduce the absorption of infrared wave remarkably, and thus highlight a road to effectively improve the infrared transmittance ability.

In this paper, a polarization difference liquid-crystal microlens array (PD-LCMLA) for three dimensional imaging application through turbid media is fabricated and demonstrated. This device is composed of a twisted nematic liquidcrystal cell (TNLCC), a polarizer and a liquid-crystal microlens array. The polarizer is sandwiched between the TNLCC and LCMLA to help the polarization difference system achieving the orthogonal polarization raw images. The prototyped camera for polarization difference imaging has been constructed by integrating the PD-LCMLA with an image sensor. The orthogonally polarized light-field images are recorded by switching the working state of the TNLCC. Here, by using a special microstructure in conjunction with the polarization-difference algorithm, we demonstrate that the three-dimensional information in the scattering media can be retrieved from the polarization-difference imaging system with an electrically tunable PD-LCMLA. We further investigate the system’s potential function based on the flexible microstructure. The microstructure provides a wide operation range in the manipulation of incident beams and also emerges multiple operation modes for imaging applications, such as conventional planar imaging, polarization imaging mode, and polarization-difference imaging mode. Since the PD-LCMLA demonstrates a very low power consumption, multiple imaging modes and simple manufacturing, this kind of device presents a potential to be used in many other optical and electro-optical systems.

With rapid advancement of infrared detecting technology in both military and civil domains, the photo-electronic performances of near-infrared detectors have been widely concerned. Currently, near-infrared detectors demonstrate some problems such as low sensitivity, low detectivity, and relatively small array scale. The current studies show that surface plasmons (SPs) stimulated over the surface of metallic nanostructures by incident light can be used to break the diffraction limit and thus concentrate light into sub-wavelength scale, so as to indicate a method to develop a new type of infrared absorber or detector with very large array. In this paper, we present the design and characterization of periodically patterned metallic nanostructures that combine nanometer thickness aluminum film with silicon wafer. Numerical computations show that there are some valleys caused by surface plasmons in the reflection spectrum in the infrared region, and both red shift and blue shift of the reflection spectrum were observed through changing the nanostructural parameters such as angle α and diameters D. Moreover, the strong E-field intensity is located at the sharp corner of the nano-structures.

Traditional imaging based on common optical lens can only be used to collect intensity information of incident beams, but actually lightwave also carries other mode information about targets and environment, including: spectrum, wavefront, and depth of target, and so on. It is very important to acquire those information mentioned for efficiently detecting and identifying targets in complex background. There is a urgent need to develop new high-performance optical imaging components. The liquid-crystal microlens (LCMs) only by applying spatial electrical field to change optical performance, have demonstrated remarkable advantages comparing conventional lenses, and therefore show a widely application prospect. Because the physical properties of the spatial electric fields between electrode plates in LCMs are directly related to the light-field performances of LCMs, the quality of voltage signal applied to LCMs needs high requirements. In this paper, we design and achieve a new type of digital voltage equipment with a wide adjustable voltage range and high precise voltage to effectively drive and adjust LCMs. More importantly, the device primarily based on field-programmable gate array(FPGA) can generate flexible and stable voltage signals to cooperate with the various functions of LCMs. Our experiments show that through the electronic control system, the LCMs already realize several significant functions including: electrically swing focus, wavefront imaging, electrically tunable spectral imaging and light-field imaging.

Fisheye cameras have been widely used in many applications including close range visual navigation and observation and cyber city reconstruction because its field of view is much larger than that of a common pinhole camera. This means that a fisheye camera can capture more information than a pinhole camera in the same scenario. However, the fisheye image contains serious distortion, which may cause trouble for human observers in recognizing the objects within. Therefore, in most practical applications, the fisheye image should be rectified to a pinhole perspective projection image to conform to human cognitive habits. The traditional mathematical model-based methods cannot effectively remove the distortion, but the digital distortion model can reduce the image resolution to some extent. Considering these defects, this paper proposes a new method that combines the physical spherical model and the digital distortion model. The distortion of fisheye images can be effectively removed according to the proposed approach. Many experiments validate its feasibility and effectiveness.

In the process of image restoration, the result of image restoration is very different from the real image because of the existence of noise, in order to solve the ill posed problem in image restoration, a blind deconvolution method based on L1/L2 regularization prior to gradient domain is proposed. The method presented in this paper first adds a function to the prior knowledge, which is the ratio of the L1 norm to the L2 norm, and takes the function as the penalty term in the high frequency domain of the image. Then, the function is iteratively updated, and the iterative shrinkage threshold algorithm is applied to solve the high frequency image. In this paper, it is considered that the information in the gradient domain is better for the estimation of blur kernel, so the blur kernel is estimated in the gradient domain. This problem can be quickly implemented in the frequency domain by fast Fast Fourier Transform. In addition, in order to improve the effectiveness of the algorithm, we have added a multi-scale iterative optimization method. This paper proposes the blind deconvolution method based on L1/L2 regularization priors in the gradient space can obtain the unique and stable solution in the process of image restoration, which not only keeps the edges and details of the image, but also ensures the accuracy of the results.

The uneven settlement of high-speed railway (HSR) brings about great threat to the safe operation of trains. Therefore, the subsidence monitoring and prediction of HSR has important significance. In this paper, an improved multitemporal InSAR method combing PS-InSAR and SBAS-InSAR, Multiple-master Coherent Target Small-Baseline InSAR (MCTSB-InSAR), is used to monitor the subsidence of partial section of the Beijing-Tianjin HSR (BTHSR) and the Beijing-Shanghai HSR (BSHSR) in Beijing area. Thirty-one TerraSAR-X images from June 2011 to December 2016 are processed with the MCTSB-InSAR, and the subsidence information of the region covering 56km*32km in Beijing is dug out. Moreover, the monitoring results is validated by the leveling measurements in this area, with the accuracy of 4.4 mm/year. On the basis of above work, we extract the subsidence information of partial section of BTHSR and BSHSR in the research area. Finally, we adopt the idea of timing analysis, and employ the back-propagation (BP) neural network to simulate the relationship between former settlement and current settlement. Training data sets and test data sets are constructed respectively based on the monitoring results. The experimental results show that the prediction model has good prediction accuracy and applicability.

Monitoring total nitrogen content (TNC) in the soil of cultivated land quantitively and mastering its spatial distribution are helpful for crop growing, soil fertility adjustment and sustainable development of agriculture. The study aimed to develop a universal method to map total nitrogen content in soil of cultivated land by HSI image at county scale. Several mathematical transformations were used to improve the expression ability of HSI image. The correlations between soil TNC and the reflectivity and its mathematical transformations were analyzed. Then the susceptible bands and its transformations were screened to develop the optimizing model of map soil TNC in the Anping County based on the method of multiple linear regression. Results showed that the bands of 14th, 16th, 19th, 37th and 60th with different mathematical transformations were screened as susceptible bands. Differential transformation was helpful for reducing the noise interference to the diagnosis ability of the target spectrum. The determination coefficient of the first order differential of logarithmic transformation was biggest (0.505), while the RMSE was lowest. The study confirmed the first order differential of logarithm transformation as the optimal inversion model for soil TNC, which was used to map soil TNC of cultivated land in the study area.

A fusion algorithm of infrared and visible images based on visual saliency map (VSM) and nonsubsampled contourlet transform (NSCT) was proposed. Usually, the visual salient region of infrared image is directed towards the targets which interpret the most important information in the image. For the given registered infrared and visible images, firstly, the frequency-tuned (FT) saliency detection algorithm is used to calculate the visual saliency map of infrared and visible images. Then the size of each salient region is obtained by maximizing entropy. In order to capture the details of the infrared and visible images, the low and high frequency fusion coefficients of nonsubsampled contourlet transform (NSCT) are selected based on region saliency, region energy (RE) and region sharpness (RS). Four different data sets from TNO, Human Factors are employed, and experimental results indicate that the proposed method can achieve a superior performance compared with other fusion methods in both subjective and objective assessments.

The spectral signature of vegetation in the image is easily affected by background soil reflectance and spectral variability of vegetation reflectance, spectral variability is one of the major error sources of unmixing. The traditional algorithms do not solve spectral variability problem from the mechanism. In this paper, we take advantage of radiative transfer model, in order to describe the spectral variability of endmember. As a result, the spectral variability can be quantitatively described. The experimental results show that the PROSAIL Model Spectral Unmixing (PMSU) algorithm has higher unmixing precision than the other algorithms.

In the hyperspectral data, the phenomenon of stripe deletion often occurs, which seriously affects the efficiency and accuracy of data analysis and application. Narrow band deletion can be directly repaired by interpolation, and this method is not ideal for wide band deletion repair. In this paper, an adaptive spectral wide band missing restoration method based on panchromatic information is proposed, and the effectiveness of the algorithm is verified by experiments.

Water vapor and aerosol are two key atmospheric factors effecting the remote sensing image quality. As water vapor is responsible for most of the solar radiation absorption occurring in the cloudless atmosphere, accurate measurement of water content is important to not only atmospheric correction of remote sensing images, but also many other applications such as the study of energy balance and global climate change, land surface temperature retrieval in thermal remote sensing. A multi-spectral, single-angular, polarized radiometer called Polarized Scanning Atmospheric Corrector (PSAC) were developed in China, which are designed to mount on the same satellite platform with the principle payload and provide essential parameters for principle payload image atmospheric correction. PSAC detect water vapor content via measuring atmosphere reflectance at water vapor absorbing channels (i.e. 0.91 μm) and nearby atmospheric window channel (i.e. 0.865μm). A near-IR channel ratio method was implemented to retrieve column water vapor (CWV) amount from PSAC measurements. Field experiments were performed at Yantai, in Shandong province of China, PSAC aircraft observations were acquired. The comparison between PSAC retrievals and ground-based Sun-sky radiometer measurements of CWV during the experimental flights illustrates that this method retrieves CWV with relative deviations ranging from 4% ~ 13%. This method retrieve CWV more accurate over land than over ocean, as the water reflectance is low.

Effective spectral and spatial pixel description plays a significant role for the classification of high resolution remote sensing images. Current approaches of pixel-based feature extraction are of two main kinds: one includes the widelyused principal component analysis (PCA) and gray level co-occurrence matrix (GLCM) as the representative of the shallow spectral and shape features, and the other refers to the deep learning-based methods which employ deep neural networks and have made great promotion on classification accuracy. However, the former traditional features are insufficient to depict complex distribution of high resolution images, while the deep features demand plenty of samples to train the network otherwise over fitting easily occurs if only limited samples are involved in the training. In view of the above, we propose a GLCM-based convolution neural network (CNN) approach to extract features and implement classification for high resolution remote sensing images. The employment of GLCM is able to represent the original images and eliminate redundant information and undesired noises. Meanwhile, taking shallow features as the input of deep network will contribute to a better guidance and interpretability. In consideration of the amount of samples, some strategies such as L2 regularization and dropout methods are used to prevent over-fitting. The fine-tuning strategy is also used in our study to reduce training time and further enhance the generalization performance of the network. Experiments with popular data sets such as PaviaU data validate that our proposed method leads to a performance improvement compared to individual involved approaches.

This paper analyzes the importance of asphalt pavement rutting detection in pavement maintenance and pavement administration in today’s society, the shortcomings of the existing rutting detection methods are presented and a new rutting line-laser extraction method based on peak intensity characteristic and peak continuity is proposed. The intensity of peak characteristic is enhanced by a designed transverse mean filter, and an intensity map of peak characteristic based on peak intensity calculation for the whole road image is obtained to determine the seed point of the rutting laser line. Regarding the seed point as the starting point, the light-points of a rutting line-laser are extracted based on the features of peak continuity, which providing exact basic data for subsequent calculation of pavement rutting depths.

The condition detection of rails in high-speed railway is one of the important means to ensure the safety of railway transportation. In order to replace the traditional manual inspection, save manpower and material resources, and improve the detection speed and accuracy, it is of great significance to develop a machine vision system for locating and identifying defects on rails automatically. Rail defects exhibit different properties and are divided into various categories related to the type and position of flaws on the rail. Several kinds of interrelated factors cause rail defects such as type of rail, construction conditions, and speed and/or frequency of trains using the rail. Rail corrugation is a particular kind of defects that produce an undulatory deformation on the rail heads. In high speed train, the corrugation induces harmful vibrations on wheels and its components and reduces the lifetime of rails. This type of defects should be detected to avoid rail fractures. In this paper, a novel method for fast rail corrugation detection based on texture filtering was proposed.

Aimed to the testing requirement of the transient high temperature in the bore of barrel weapon, which has the problems of high temperature, high pressure, high overload and narrow adverse environment, the photoelectric pyrometer was researched based on the temperature measurement technology of double line of atomic emission spectrum and storage measurement technology, which used silicon photomultiplier. Al I 690.6nm and 708.5nm were selected as the temperature measurement element spectral lines, spectral line intensity was converted into a voltage value by silicon photomultiplier, the temperature was obtained through the ratio of two spectrum lines. The temperature is measured by the photoelectric thermometer and the infrared thermometer when heating aluminum by oxyhydrogen flame, and the relative error was 1.75%. Results show the temperature dependence of the two methods is better, and prove the feasibility of the method.

The impervious surface has become an important index to evaluate the urban environmental quality and measure the development level of urbanization. At present, the use of remote sensing technology to extract impervious surface has become the main way. In this paper, a method to extract impervious surface based on rule algorithm is proposed. The main ideas of the method is to use the rule-based algorithm to extract impermeable surface based on the characteristics and the difference which is between the impervious surface and the other three types of objects (water, soil and vegetation) in the seven original bands, NDWI and NDVI. The steps can be divided into three steps: 1) Firstly, the vegetation is extracted according to the principle that the vegetation is higher in the near-infrared band than the other bands; 2) Then, the water is extracted according to the characteristic of the water with the highest NDWI and the lowest NDVI; 3) Finally, the impermeable surface is extracted based on the fact that the impervious surface has a higher NDWI value and the lowest NDVI value than the soil.In order to test the accuracy of the rule algorithm, this paper uses the linear spectral mixed decomposition algorithm, the CART algorithm, the NDII index algorithm for extracting the impervious surface based on six remote sensing image of the Dianchi Lake Basin from 1999 to 2014. Then, the accuracy of the above three methods is compared with the accuracy of the rule algorithm by using the overall classification accuracy method. It is found that the extraction method based on the rule algorithm is obviously higher than the above three methods.

A medical endoscope system combined with the narrow-band imaging (NBI), has been shown to be a superior diagnostic tool for early cancer detection. The NBI can reveal the morphologic changes of microvessels in the superficial cancer. In order to improve the conspicuousness of microvessel texture, we propose an enhanced NBI method to improve the conspicuousness of endoscopic images. To obtain the more conspicuous narrow-band images, we use the edge operator to extract the edge information of the narrow-band blue and green images, and give a weight to the extracted edges. Then, the weighted edges are fused with the narrow-band blue and green images. Finally, the displayed endoscopic images are reconstructed with the enhanced narrow-band images. In addition, we evaluate the performance of enhanced narrow-band images with different edge operators. Experimental results indicate that the Sobel and Canny operators achieve the best performance of all. Compared with traditional NBI method of Olympus company, our proposed method has more conspicuous texture of microvessel.

In this work, remote sensing reflectance (Rrs) spectra of the Zhejiang coastal water in the East China Sea (ECS) were simulated by using the Hydrolight software with field data as input parameters. The seawater along the Zhejiang coast is typical Case II water with complex optical properties. A field observation was conducted in the Zhejiang coastal region in late May of 2016, and the concentration of ocean color constituents (pigment, SPM and CDOM), IOPs (absorption and backscattering coefficients) and Rrs were measured at 24 stations of 3 sections covering the turbid to clear inshore coastal waters. Referring to these ocean color field data, an ocean color model suitable for the Zhejiang coastal water was setup and applied in the Hydrolight. A set of 11 remote sensing reflectance spectra above water surface were modeled and calculated. Then, the simulated spectra were compared with the filed measurements. Finally, the spectral shape and characteristics of the remote sensing reflectance spectra were analyzed and discussed.

A new method for the lossless compression of the interferometer hyperspectral instrument Large Aperture Static Imaging Spectrometer (LASIS) data is presented in this paper. Differs from traditional hyperspectral instrument, the image captured by the two dimensional CCD detector of LASIS is no longer a normal image, but the two spatial information of the scene superimposed with interference fringes of equal thickness. There is a translation motion of the spatial information among each frame of LASIS data cube. Based on these unique data characteristics of LASIS and the recently presented CCSDS-123 lossless multispectral & Hyperspectral image compression standard, an improved predictor is designed for the prediction of LASIS data while using the standard. We perform several experiments on real data acquired by LASIS to investigate the performance of the proposed predictor. Experimental results show that the proposed predictor gives about 27.5% higher compression ratio than the default predictor of CCSDS-123 for lossless compression of LASIS data. In addition, the appropriate choice of several parameters of the proposed predictor are presented according to the experimental results.

The satellite images with very high spatial resolution have been recently widely used in image classification topic as it has become challenging task in remote sensing field. Due to a number of limitations such as the redundancy of features and the high dimensionality of the data, different classification methods have been proposed for remote sensing images classification particularly the methods using feature extraction techniques. This paper propose a simple efficient method exploiting the capability of extended multi-attribute profiles (EMAP) with sparse autoencoder (SAE) for remote sensing image classification. The proposed method is used to classify various remote sensing datasets including hyperspectral and multispectral images by extracting spatial and spectral features based on the combination of EMAP and SAE by linking them to kernel support vector machine (SVM) for classification. Experiments on new hyperspectral image "Huston data" and multispectral image "Washington DC data" shows that this new scheme can achieve better performance of feature learning than the primitive features, traditional classifiers and ordinary autoencoder and has huge potential to achieve higher accuracy for classification in short running time.

This paper expounds method of the average weighted fusion, image pyramid fusion, the wavelet transform and apply these methods on the fusion of multiple exposures nighttime images. Through calculating information entropy and cross entropy of fusion images, we can evaluate the effect of different fusion. Experiments showed that Laplacian pyramid image fusion algorithm is suitable for processing nighttime images fusion, it can reduce the halo while preserving image details.