This PDF file contains the front matter associated with SPIE Proceedings Volume 8895, including the Title Page, Copyright information, Table of Contents, Introduction (if any), and Conference Committee listing.

The basic observables of an imaging interferometer by aperture synthesis are the complex visibilities. Under some conditions, they can be simulated with reference to the Van Cittert-Zernike theorem. However, owing to the underlying assumptions some important effects that may alter them cannot be taken into account. This paper is devoted to the numerical simulation of complex visibilities with very few assumptions. The emission is modelled with random short wave trains. Each wave is transported to the antennae, transmitted to the receivers and the corresponding signals are cross-correlated. From emission to correlation, perturbating effects can be introduced. However, owing to the amount of calculations to be performed, massive parallel architectures like that found in GPU are required. To illustrate this modelling, numerical simulations are carried out in the L-band in reference to the SMOS-next project led by the french space agency. The results are discussed and compared with the estimates provided by the Van Cittert-Zernike theorem.

This paper proposes a blind multi-watermarking scheme based on designing two back-to-back encoders. The first encoder is implemented to embed a robust watermark into remote sensing imagery by applying a Discrete Cosine Transform (DCT) approach. Such watermark is used in many applications to protect the copyright of the image. However, the second encoder embeds a fragile watermark using ‘SHA-1’ hash function. The purpose behind embedding a fragile watermark is to prove the authenticity of the image (i.e. tamper-proof). Thus, the proposed technique was developed as a result of new challenges with piracy of remote sensing imagery ownership. This led researchers to look for different means to secure the ownership of satellite imagery and prevent the illegal use of these resources. Therefore, Emirates Institution for Advanced Science and Technology (EIAST) proposed utilizing existing data security concept by embedding a digital signature, “watermark”, into DubaiSat-1 satellite imagery. In this study, DubaiSat-1 images with 2.5 meter resolution are used as a cover and a colored EIAST logo is used as a watermark. In order to evaluate the robustness of the proposed technique, a couple of attacks are applied such as JPEG compression, rotation and synchronization attacks. Furthermore, tampering attacks are applied to prove image authenticity.

Despite the same purpose, each satellite product has different value because of its inescapable uncertainty. Also the satellite products have been calculated for a long time, and the kinds of the products are various and enormous. So the efforts for reducing the uncertainty and dealing with enormous data will be necessary. In this paper, we create an ensemble Sea Surface Temperature (SST) using MODIS Aqua, MODIS Terra and COMS (Communication Ocean and Meteorological Satellite). We used Bayesian Model Averaging (BMA) as ensemble method. The principle of the BMA is synthesizing the conditional probability density function (PDF) using posterior probability as weight. The posterior probability is estimated using EM algorithm. The BMA PDF is obtained by weighted average. As the result, the ensemble SST showed the lowest RMSE and MAE, which proves the applicability of BMA for satellite data ensemble. As future work, parallel processing techniques using Hadoop framework will be adopted for more efficient computation of very big satellite data.

The maximum simplex volume algorithm (MSVA) is an automatic endmember extraction method based on geometrical properties of simplex in high-dimensional feature space. By utilizing the relation of volume between a simplex and its corresponding parallelohedron in the high-dimensional space, the algorithm extracts endmembers directly from the initial hyperspectral image in a sequential manner without dimensionality reduction. It is thus considered to have overcome a major drawback of N-FINDR algorithm, which requires the data dimension reduced to one less than the number of the endmembers before endmembers extraction. But the MSVA suffers from excessive computation caused by massive determinant operation in practical application. An improved fast implementation method based on partitioned determinant operation is proposed in this paper to reduce computational complexity. Experimental results demonstrate that the proposed fast algorithm can greatly reduce computational complexity, while simultaneously their computational accuracyremains as good as its original algorithm.

Hyperspectral images can be used to identify the unique materials present in an area.Due to the limited spatial resolution, each pixel of the image is considered as a mixture of several different pure substances or endmembers. Several spectral unmixing methods have been developed for endmember extraction in an image. Among them, the vertex component analysis (VCA) algorithm is a popular one for its superior performance. As there are a lot of matrix/vector operations involved in the VCA algorithm, this work aims to apply the highly parallel computing power of recent GPUs which are reported to have good success in acceleration of many compute intensive applications. In the experiment, the compute unified device architecture (CUDA) which provide more convenient programming model is used. The speedup is measured with respect to standard C code on a single core CPU for evaluation.Our experiments are performed on a typical case where the number of extracted endmembers is 30 from the 188-band Cuprite hyperspectral dataset.The results show that a speedup of 42x can be achieved on a pure GPU implementation using CULA and CUBLAS libraries. As VCA involves Singular Value Decomposition (SVD) operation and SVD is faster on CPU than GPU for small data sizes as is our case, a speedup of 58x can be achieved on a hybrid implementation when SVD is carried out on CPU.

With the development of high resolution remote sensing satellite in recent years, the research of typical objects is connecting more and more closely with remote sensing applications. In the TDI CCD camera on-orbit imaging process, great changes will happen on solar angles at different time, causing a certain change of BRDF of most earth’s surface objects, and finally affect the remote sensing radiances, even imaging quality. In order to solve this problem, optimization of in-orbit parameters based on the ground objects BRDF is necessary. A detailed investigation about the global imaging area of ground objects characteristics is given in this paper. We inverse BRDF of different time based on Kernel-Driven BRDF model, establish database of ground objects BRDF, make a classification of ground objects characteristics, simulate imaging effect with radiative transfer model and degradation model of remote sensor, and then optimize imaging parameters according to the imaging quality requirement. The simulation results show that the contrast, definition and dynamic range of image have improved, the proposed method in this paper can set imaging parameters reasonably of different imaging conditions, improve the imaging quality of high resolution remote sensing satellites.

Parallel hyperspectral unmixing problem is considered in this paper. A semisupervised approach is developed under the linear mixture model, where the abundance’s physical constraints are taken into account. The proposed approach relies on the increasing availability of spectral libraries of materials measured on the ground instead of resorting to endmember extraction methods.

Since Libraries are potentially very large and hyperspectral datasets are of high dimensionality a parallel implementation in a pixel-by-pixel fashion is derived to properly exploits the graphics processing units (GPU) architecture at low level, thus taking full advantage of the computational power of GPUs. Experimental results obtained for real hyperspectral datasets reveal significant speedup factors, up to 164 times, with regards to optimized serial implementation.

In this paper, the graphic processor unit (GPU) implementation of the finite-difference time domain (FDTD) algorithm is presented to investigate the electromagnetic (EM) scattering from one dimensional (1-D) Gaussian rough soil surface. The FDTD lattices are truncated by uniaxial perfectly matched layer (UPML), in which the finite-difference equations are carried out for the total computation domain. Using Compute Unified Device Architecture (CUDA) technology, significant speedup ratios are achieved for different incident frequencies, which demonstrates the efficiency of GPU accelerated the FDTD method. The validation of our method is verified by comparing the numerical results with these obtained by CPU, which shows favorable agreements.

An Atmospheric radiative transfer model calculates radiative transfer of electromagnetic radiation through a planetary atmosphere. Both shortwave radiance and longwave radiance parameterizations in an atmospheric model calculate radiation fluxes and heating rates in the earth-atmospheric system. One radiative transfer model is the rapid radiative transfer model (RRTM), which calculates of longwave and shortwave atmospheric radiative fluxes and heating rates. Longwave broadband radiative transfer code for general circulation model (GCM) applications, RRTMG, is based on the single-column reference code, RRTM. The RRTMG is a validated, correlated k-distribution band model for the calculation of longwave and shortwave atmospheric radiative fluxes and heating rates. The focus of this paper is on the RRTMG long wave (RRTMG_LW) model. In order to improve computational efficiency, RRTMG_LW incorporates several modifications compared to RRTM. In RRTM_LW there are 16 g points in each of the spectral bands for a total of 256 g points. In RRTMG_LW, the number of g points in each spectral band varies from 2 to 16 depending on the absorption in each band. RRTMG_LW employs a computationally efficient correlated-k method for radiative transfer calculations. It contains 16 spectral bands with various number of quadrature points (g points) in each of the bands. In total, there are 140 g points. The radiative effects of all significant atmospheric gases are included in RRTMG_LW. Active gas absorbers include H₂O, O₃, CO₂, CH₄, N₂O, O₂ and four types of halocarbons: CFC-11, CFC-12, CFC-22, and CCL4. RRTMG_LW also treats the absorption and scattering from liquid and ice clouds and aerosols. For cloudysky radiative transfer, a maximum-random cloud overlapping scheme is used. Small scale cloud variability, such as cloud fraction and the vertical overlap of clouds can be represented using a statistical technique in RRTMG_LW. Due to its accuracy, RRTMG_LW has been implemented operationally in many weather forecast and climate models. RRTMG_LW is in operational use in ECMWF weather forecast system, the NCEP global forecast system, the ECHAM5 climate model, Community Earth System Model (CESM) and the weather and forecasting (WRF) model. RRTMG_LW has also been evaluated for use in GFDL climate model. In this paper, we examine the feasibility of using graphics processing units (GPUs) to accelerate the RRTMG_LW as used by the WRF. GPUs can provide a substantial improvement in RRTMG speed by supporting the parallel computation of large numbers of independent radiative calculations. Furthermore, using commodity GPUs for accelerating RRTMG_LW allows getting a much higher computational performance at lower price point than traditional CPUs. Furthermore, power and cooling costs are significantly reduced by using GPUs. A GPU-compatible version of RRTMG was implemented and thorough testing was performed to ensure that the original level of accuracy is retained. Our results show that GPUs can provide significant speedup over conventional CPUs. In particular, Nvidia’s GTX 680 GPU card can provide a speedup of 69x for the compared to its single-threaded Fortran counterpart running on Intel Xeon E5-2603 CPU.

As the cost of imaging systems have decreased, the quality and size has increased. This dynamic has made the practicality of many aerial imaging applications achievable such as cost line monitoring and vegetation indexing. Orthorectification is required for many of these applications; however, it is also expensive, computationally. The computational cost is due to oating point operations and divisions inherent in the orthorecti cation process. Two novel algorithm modi cations are proposed which signi cantly reduce the computational cost. The rst modi cation uses xed-point arithmetic in place of the oating point operations. The second replaces the division with a multiplication of the inverse. The result in an increase of 2x of the throughput while remaining within 15% of a pixel size in position.

In this study, an improved ray tracing propagation prediction model, which is based on creating a new virtual source tree, is used because of their high efficiency and reliable prediction accuracy. In addition, several acceleration techniques are also adopted to improve the efficiency of coverage prediction over large areas. However, in the process of employing the ray tracing method for coverage zone prediction, runtime is linearly proportional to the total number of prediction points, leading to large and sometimes prohibitive computation time requirements under complex geographical environments. In order to overcome this bottleneck, the compute unified device architecture (CUDA), which provides fine-grained data parallelism and thread parallelism, is implemented to accelerate the calculation. Taking full advantage of tens of thousands of threads in CUDA program, the decomposition of the coverage prediction problem is firstly conducted by partitioning the image tree and the visible prediction points to different sources. Then, we make every thread calculate the electromagnetic field of one propagation path and then collect these results. Comparing this parallel algorithm with the traditional sequential algorithm, it can be found that computational efficiency has been improved dramatically.

Radio wave propagation prediction is very important for the design of the mobile communication network. The raytracing algorithm is a commonly used computational method for site-specific prediction of the radio channel characteristics of wireless communication systems. However, it does not consider the diffuse scattering. Therefore, an indoor diffuse scattering model which based on diffuse scattering theory and FDTD is established. The diffuse scattering of indoor walls and ceiling and floor is calculated at a series of discrete time instance in this method. In recent years, the compute unified device architecture (CUDA) of NVIDIA takes advantage of the GPU for parallel computing, and greatly improve the speed of computation. Because there is a large number of data to deal with, in order to reduce the computation time, a GPU-based diffuse scattering model for indoor radio prediction is introduced in this paper, which fully utilizes the parallel processing capabilities of CUDA to further improve the computational efficiency. It can be found that good acceleration effect has been achieved.