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The nascent graph representation learning has shown superiority for resolving graph data. Compared to conventional convolutional neural networks, graph-based deep learning has the advantages of illustrating class boundaries and modeling feature relationships. Faced with hyperspectral image (HSI) classification, the priority problem might be how to convert hyperspectral data into irregular domains from regular grids. In this regard, we present a novel method that performs the localized graph convolutional filtering on HSIs based on spectral graph theory. First, we conducted principal component analysis (PCA) preprocessing to create localized hyperspectral data cubes with unsupervised feature reduction. These feature cubes combined with localized adjacent matrices were fed into the popular graph convolution network in a standard supervised learning paradigm. Finally, we succeeded in analyzing diversified land covers by considering local graph structure with graph convolutional filtering. Experiments on real hyperspectral datasets demonstrated that the presented method offers promising classification performance compared with other popular competitors.

Gain redshift in high peak power Ti:sapphire laser systems can result in narrowband spectral output and hence lengthen the compressed pulse duration. In order to realize broadband spectral output in 10 PW-class Ti:sapphire lasers, the influence on gain redshift induced by spectral pre-shaping, gain distribution of cascaded amplifiers and Extraction During Pumping (EDP) technique have been investigated. The theoretical and experimental results show that the redshift of output spectrum is sensitive to the spectral pre-shaping and the gain distribution of cascaded amplifiers, while insensitive to the pumping scheme with or without EDP. Moreover, the output spectrum from our future 10 PW Ti:sapphire laser is theoretically analyzed based on the investigations above, which indicates that a Fourier-transform limited (FTL) pulse duration of 21 fs can be achieved just by optimizing the spectral pre-shaping and gain distribution in 10 PW-class Ti:sapphire lasers.

Interest on anomaly detection for hyperspectral images has increasingly grown during the last decades due to the diversity of applications that benefit from this technique. However, the high computational cost inherent to this detection procedure seriously limits its processing efficiency, especially for onboard application scenarios. In this paper, a novel spectral and spatial approximate computing approach, named SSAC is proposed for onboard anomaly detection from hyperspectral images. To efficiently design the proposed approach, two preliminary aspects have been deeply analyzed in this work. First, data correlation in hyperspectral images in both spectral and spatial dimensions has been analyzed. The high data correlation in both spectral and spatial dimensions is considered to be one of the cornerstones of the SSAC approach. Second, the error resilience of a popular hyperspectral anomaly detection algorithm in both data level and algorithm level has been analyzed, which is considered to be another cornerstone of the SSAC approach. Based on the outcomes of this analysis, the processing of spectrally and spatially degraded images has been employed for reducing computation complexity in onboard hyperspectral anomaly detection scenarios in this work. Performance assessment tools such as ROC curves, Cost curves, and computing times have been used for evaluating the computing accuracy and efficiency of our proposal. The results obtained with a nonlinear anomaly detector for hyperspectral imagery, such as the well-known kernel RX-algorithm, show that the proposed SSAC approach greatly improves anomaly detection efficiency compared to the traditional method with negligible degeneration in accuracy. This is an important achievement to meet the restrictions of onboard hyperspectral anomaly detection scenarios.

Target detection is an important technique in hyperspectral image analysis. The high dimensionality of hyperspectral data provides the possibility of deeply mining the information hiding in spectra, and many targets that cannot be visualized by inspection can be detected. But this also brings some problems such as unknown background interferences at the same time. In this way, extracting and taking advantage of the background information in the region of interest becomes a task of great significance. In this paper, we present an unsupervised background extraction-based target detection method, which is called UBETD for short. The proposed UBETD takes advantage of the method of endmember extraction in hyperspectral unmixing, another important technique that can extract representative material signatures from the images. These endmembers represent most of the image information, so they can be reasonably seen as the combination of targets and background signatures. Since the background information is known, algorithm like target-constrained interference-minimized filter could then be introduced to detect the targets while inhibiting the interferences. To meet the rapidly rising demand of real-time processing capabilities, the proposed algorithm is further simplified in computation and implemented on a FPGA board. Experiments with synthetic and real hyperspectral images have been conducted comparing with constrained energy minimization, adaptive coherence/cosine estimator and adaptive matched filter to evaluate the detection and computational performance of our proposed method. The results indicate that UBETD and its hardware implementation RT-UBETD can achieve better performance and are particularly prominent in inhibiting interferences in the background. On the other hand, the hardware implementation of RT-UBETD can complete the target detection processing in far less time than the data acquisition time of hyperspectral sensor like HyMap, which confirms strict real-time processing capability of the proposed system.

Onboard processing systems are becoming very important in remote sensing data processing. However, a main problem with specialized hardware architectures used for onboard processing is their high power consumption, which limits their exploitation in earth observation missions. In this paper, a novel strategy for approximate computing is proposed for reducing energy consumption in remotely sensed onboard processing tasks. As a case study, the implementation of support vector machine (SVM) hyperspectral image classification is considered by using the proposed approximate computing framework. Experimental results show that the proposed approximate computing scheme achieves up to 70% power savings in the kernel accumulation computation procedure with negligible degradation of classification accuracy as compared to the traditional ripple carry adder (RCA) precise computation. This is an important achievement to meet the restrictions of onboard processing scenarios.

A framework based on color feature extraction of social media photos and correlation analysis with air quality parameters is proposed to monitor environmental change. More specifically, photos of the Beijing Olympic Park from Panoramio website have been analyzed as a case study. The aerosol optical depth data at 500 nm wavelength (AOD 500) obtained from sun-photometer observation network station has been used as reference. Results show a proof of concept that social media photos have an interesting potential for air pollution estimate and remote sensing parameter validation with a low cost.

Hyperspectral image unmixing is an important part of hyperspectral data analysis. The mixed pixel decomposition consists of two steps, endmember (the unique signatures of pure ground components) extraction and abundance (the proportion of each endmember in each pixel) estimation. Recently, a Discrete Particle Swarm Optimization algorithm (DPSO) was proposed for accurately extract endmembers with high optimal performance. However, the DPSO algorithm shows very high computational complexity, which makes the endmember extraction procedure very time consuming for hyperspectral image unmixing. Thus, in this paper, the DPSO endmember extraction algorithm was parallelized, implemented on the CUDA (GPU K20) platform, and evaluated by real hyperspectral remote sensing data. The experimental results show that with increasing the number of particles the parallelized version obtained much higher computing efficiency while maintain the same endmember exaction accuracy.

Linear spectral unmixing is a very important technique in hyperspectral image analysis. It contains two main steps. First, it finds spectrally unique signatures of pure ground components (called endmembers); second, it estimates their corresponding fractional abundances in each pixel. Recently, a discrete particle swarm optimization (DPSO) algorithm was introduced to accurately extract endmembers with high optimal performance. However, because of its limited feasible solution space, DPSO necessarily needs a small amount of candidate endmembers before extraction. Consequently, how to provide a suitable candidate endmember set, which has not been analyzed yet, is a critical issue in using DPSO for unmixing problem. In this study, three representative pure pixel-based methods, pixel purity index, vertex component analysis (VCA), and N-FINDR, are quantitatively compared to provide candidate endmembers for DPSO. The experiments with synthetic and real hyperspectral images indicate that VCA is the most reliable preprocessing implementation for DPSO. Further, it can be concluded that DPSO with the proposed preprocessing implementations given in this paper is robust for endmember extraction.

We have theoretically and experimentally investigated the evolution of the temporal contrast in a 10-PW-level Ti:sapphire laser in the Shanghai Superintense Ultrafast Laser Facility (SULF). The effects induced by the grism pair, spectral shaping filter, and increase in gain on the temporal contrast were investigated. First, it was found that the energy loss of clean seed pulses in the grism pair is a major factor in contrast degradation. Because of the low transmission efficiency of the grism pair (~10%), the temporal contrast is degraded by one order of magnitude. Second, the spectral shaping filter in the regenerative amplifier degrades the temporal contrast by increasing the intracavity loss. Finally, as the amplified spontaneous emission pedestal experiences gain more than the main pulse in Ti:sapphire amplifiers, particularly during saturated amplification, the temporal contrast will further deteriorate as the gain increases in multi-stage Ti:sapphire amplifiers. In addition, the effect on the temporal contrast induced by the extraction during pumping technique in large-aperture Ti:sapphire amplifiers has been considered. According to the investigations described above, the design of the SULF can be further improved. It is predicted that a temporal contrast of over 10−11 can be achieved at a peak power of 10 PW following the improvements. The investigations conducted in this study can provide guidelines for improving the temporal contrast in ultrahigh-peak-power Ti:sapphire lasers.

In the traditional signal model, signal is assumed to be deterministic, and noise is assumed to be random, additive and uncorrelated to the signal component. A hyperspectral image has high spatial and spectral correlation, and a pixel can be well predicted using its spatial and/or spectral neighbors; any prediction error can be considered from noise. Using this concept, several algorithms have been developed for noise estimation for hyperspectral images. However, these algorithms have not been rigorously analyzed with a unified scheme. In this paper, we conduct a comparative study for such linear regression-based algorithms using simulated images with different signal-to-noise ratio (SNR) and real images with different land cover types. Based on experimental results, instructive guidance is concluded for their practical applications.

Urban morphology and morphology change and their impacts on urban transportation have been studied extensively in planar urban space. The essential feature of urban space, however, is its three-dimensionality (3D), and few studies have been conducted from a 3D perspective, overly limiting the accuracy of studies on the relationships between urban morphology and transportation. The aim of this paper is to simulate the impacts of 3D urban morphologies on urban transportation under the Digital Earth framework. On the basis of the principle that population distribution and movement are largely confined by 3D urban morphologies, which affect transportation, high spatial resolution remote sensing imagery and a thematic vector data-set were used to extract urban morphology and transportation-related variables. With a combination of three research methods – factor analysis, spatial regression analysis and Euclidean allocation – we provide an effective method to construct a simulation model. The paper indicates th...

One of the most important tasks in analyzing hyperspectral image data is the classification process[1]. In general, in order to enhance the classification accuracy, a data preprocessing step is usually adopted to remove the noise in the data before classification. But for the time-sensitive applications, we hope that even the data contains noise the classifier can still appear to execute correctly from the user’s perspective, such as risk prevention and response. As the most popular classifier, Support Vector Machine (SVM) has been widely used for hyperspectral image classification and proved to be a very promising technique in supervised classification[2]. In this paper, two experiments are performed to demonstrate that for the hyperspectral data with noise, if the noise of the data is within a certain range, SVM algorithm is still able to execute correctly from the user’s perspective.

Anomaly detection is one of the most important techniques for remotely sensed hyperspectral data interpretation. Developing fast processing techniques for anomaly detection has received considerable attention in recent years, especially in analysis scenarios with real-time constraints. In this paper, we develop an embedded graphics processing units based parallel computation for streaming background statistics anomaly detection algorithm. The streaming background statistics method can simulate real-time anomaly detection, which refer to that the processing can be performed at the same time as the data are collected. The algorithm is implemented on NVIDIA Jetson TK1 development kit. The experiment, conducted with real hyperspectral data, indicate the effectiveness of the proposed implementations. This work shows the embedded GPU gives a promising solution for high-performance with low power consumption hyperspectral image applications.

Background: Natural sulforaphane (SF) has been of increasing interest for nutraceutical and pharmaceutical industries due to its anti-cancer effect. The main objective of the present work was to optimize the production of SF from broccoli seed using response surface methodology. Results: Three major factors (hydrolysis time, water volume and ethyl acetate volume) were screened out through Plackett-Burman (PB) factorial design. The methods of steepest ascent combined with central composite design (CCD) were employed for optimization of the SF production process. The optimal extraction conditions for SF production were a hydrolysis time of 13 min, a hydrolysis volume/weight ratio of 2.9:1 (v/g) and an extraction volume/weight ratio of 17.5:1 (v/g). The maximum SF yield was 14.8 ± 0.1 mg/g, a value that was in perfect agreement with the actual experimental value (14.8 mg/g). Conclusions: These results suggested that PB design combined with CCD were proved effective in screening and optimization of the parameters of SF production.

In this paper, ant colony optimization (ACO) is applied to hyperspectral band selection. The objective is to select a small band subset such that classification accuracy can be maintained or even improved. The ACO-based band selection technique in this research is independent of any classifier, resulting in lower computational cost. Both supervised (i.e., Jeffries-Matusita distance) and unsupervised (i.e., simplex volume) selection criteria are investigated. The experimental results demonstrate that the classification accuracy on the selected bands is higher than using all bands, and ACO-based methods can outperform the widely used sequential forward selection (SFS) method.

This paper proposed a GPU-based implementation of radiometric normalization algorithms, which is used as a representative case study of on-board data processing techniques for hyperspectral image. Three algorithms of radiometric normalization based on the column average and standard deviation of raw image statistical characteristics were implemented and applied to real hyperspectral images for evaluating their performance. These algorithms have been implemented using the compute device unified architecture (CUDA), and tested on the NVidia Tesla C2075 architecture. The airborne Pushbroom Hyperspectral Imager (PHI) was flown to acquire the spectrally contiguous images as experimental datasets. The results show that MN worked best among the three methods and the speedups achieved by the GPU implementation over their CPU counterparts are outstanding.

Military target detection is an important application of hyperspectral remote sensing. It highly demands real-time or near real-time processing. However, the massive amount of hyperspectral image data seriously limits the processing speed. Real-time image processing based on hardware platform, such as digital signal processor (DSP), is one of recent developments in hyperspectral target detection. In hyperspectral target detection algorithms, correlation matrix or covariance matrix calculation is always used to whiten data, which is a very time-consuming process. In this paper, a strategy named spatial-spectral information extraction (SSIE) is presented to accelerate the speed of hyperspectral image processing. The strategy is composed of bands selection and sample covariance matrix estimation. Bands selection fully utilizes the high-spectral correlation in spectral image, while sample covariance matrix estimation fully utilizes the high-spatial correlation in remote sensing image. Meanwhile, this strategy is implemented on the hardware platform of DSP. The hardware implementation of constrained energy minimization (CEM) algorithm is composed of hardware architecture and software architecture. The hardware architecture contains chips and peripheral interfaces, and software architecture establishes a data transferring model to accomplish the communication between DSP and PC. In experiments, the performance on software of ENVI with that on hardware of DSP is compared. Results show that the processing speed and recognition result on DSP are better than those on ENVI. Detection results demonstrate that the strategy implemented by DSP is sufficient to enable near real-time supervised target detection.

HJ1A HSI is an interferometric imaging spectrometer (Hyper-spectral Imager, sensor ID: HSI) of HJ-1 small satellite. The hyper-spectral image data are organized and stored in hierarchical data format version 5 (HDF5) files. This paper presents the data model, file structure, library and programming model of HDF5 file format. The adapter design pattern is used for translating hdf5 interface into a compatible interface. Then, we give a detailed analysis of HJ1A hyper-spectral image data. The HJ1A hyper-spectral image data model includes five groups: 'GlobalAttributes', 'ImageAttributes', 'ImageData', 'MapInformation', and 'ProductParameters'' under the root group. The 'ImageData' group includes three datasets: 'BandData', 'CalibrationCoefficient', and 'WaveLength'. Based on the relationships between the models and implementations, we give a flow chart of extraction HJ1A hyper-spectral image data from hdf5 files. The level2 product of HJ1A hyper-spectral image data is used for experiment. We present the RGB color composite image and 3D cube of the extracted data. Tests show that the data extraction is correct and rapid with this approach. This work provides a solid foundation for quality evaluation and application of HJ1A hyper-spectral image.

This paper creates an operational swift solution for automatic analyzing water quality in case II waters. The HJ-1 satellites of China was successfully launched, this provides a good opportunity for case II water (inland and coastal water) environment monitoring. However, there is no operational monitoring system in China and abroad. The traditional processing methods were developed for expert users, which including many complex operations and user interaction, not suitable for business operation. To overcome this problem, an automatic monitoring procedure was developed by us. With this approach, one can get monitoring reports from original HJ-1 satellites images or MODIS images with “one-click”. That means, you do not have to concern the complex data processing procedure, and what you need to do is specify the water quality monitoring area, the specific space-borne sensor and the remote sensing images. According to the study area, sensor type and image file, the operational procedure will automatically get parameters from the integrated XML files and database indexed by area and sensor type. The processing procedure does not need any user intervention, which greatly improve the processing efficiency. This paper gives a complete flow chart of data processing in the automatic monitoring mode using XML files and databases. Moreover, in order to verify the ability of the operational monitoring approach, experiments were conducted in Tai'hu, Chao'hu, and Dian'chi lakes using MODIS QKM images and the HJ-1 multi-spectral images. Tests show both the efficiency and accuracy of this approach is relatively high.

In late June 2008, Enteromorpha prolifera (E.P) bloomed in Qingdao marine, including Qingdao Olympic Sailing Center. The large scale and the fast extending speed of the E.P bloom were rare in history. The E.P bloomed just before Beijing 2008 Olympics, so it attracted the most attention around the world. Monitoring E.P from remote sensing data has the advantages of rapidness, wide coverage, low cost. But commonly used optical remote sensing is easily affected by cloud, which limits the monitoring timelines. In this paper, the theory of E.P monitoring from optical remote sensing and SAR remote sensing were analyzed. Then, the technical routes of E.P monitoring by exploiting the synergy of active and passive remote sensing data were presented. Because the SAR images are not affected by cloud, the timelines can be increased exploiting the by synergy of active and passive remote sensing data. MODIS and RADARSAT were used to monitor E.P. The monitoring results were used to analyze the beginning and spread of E.P. The monitoring results from MODIS and RADARSAT were compared.

The aerosol optical depth (AOD) measured by sun photometer is obtained after subtracting the Rayleigh optical depth, ozone optical depth and other absorption gas optical depth from total optical depth. Wherein, it was according to Beer's attenuation law. However, the band filter function of sun photometers should be considered when retrieving AODs. In this paper, it was systemically analyzed the influence of sun photometer filter function on retrieving aerosol optical depth. Numerous study showed that the band filter function has a significant impact on retrieving AODs: 1)The uncertainty of AODs may become large when the filter function is not well desgined; 2) Under large zenith observation condition, it may introduce non-neglected errors if Rayleigh scattering optical depth is calculated directly at central wavelength without filter function considered; 3) The band weighted absorption coefficients of O 3 and NO 2 almost hold constant when gas amounts increase, except for questionable designed band filter; 4) These weak absorption optical depths can not be ignored such as H 2 O absorption optical depths in 1020nm &1640nm band and CH 4 , CO 2 absorption optical depths in 1640nm band.

Enterprise computing often takes the form of automation islands, and lots of business operations must be built on these islands. Then reusing existing components and creating integrated, flexible, reliable applications which can be combined for maximum productivity have become the imperative for an enterprise to survive and thrive in the new competitive and dynamic business environment. In the company with SAP as its ERP software, it is an obvious choice for the enterprise to target SAP R/3 Enterprise as its data source. But the question is how to achieve the integration when the company used Java EE as its platform for developing enterprise applications? JCO (Java Connector) is a new and economical solution that is introduced in this paper for the integration.

We propose a commodity graphics processing units (GPUs)–based massively parallel efficient computation for spectral-spatial classification of hyperspectral images. The spectral-spatial classification framework is based on the marginal probability distribution which uses all of the information in the hyperspectral data. In this framework, first, the posterior class probability is modeled with discriminative random field in which the association potential is linked with a multinomial logistic regression (MLR) classifier and the interaction potential modeling the spatial information is linked to a Markov random field multilevel logistic (MLL) prior. Second, the maximizers of the posterior marginals are computed via the loopy belief propagation (LBP) method. In addition, the regressors of the multinominal logistic regression classifier are inferred by the logistic regression via variable splitting and augmented Lagrangian (LORSAL) algorithm. Although the spectral-spatial classification framework exhibited state-of-the-art accuracy performance with regard to similar approaches, its computational complexity is very high. We take advantage of the massively parallel computing capability of NVIDIA Tesla C2075 with the compute unified device architecture including a set of GPU-accelerated linear algebra libraries (CULA) to dramatically improve the computation speed of this hyperspectral image classification framework. The shared memory and the asynchronous transfer techniques are also used for further computationally efficient optimization. Real hyperspectral data sets collected by the National Aeronautics and Space Administration’s airborne visible infrared imaging spectrometer and the reflective optics system imaging spectrometer system are used for effectiveness evaluation. The results show that we achieved a speedup of 92-fold on LORSAL, 69-fold on MLR, 127-fold on MLL, 160-fold on LBP, and 73-fold on the whole spectral-spatial classification framework as compared with its single-core central processing unit counterpart, respectively.

We present a parallel implementation of the optimized maximum noise fraction (G-OMNF) transform algorithm for feature extraction of hyperspectral images on commodity graphics processing units (GPUs). The proposed approach explored the algorithm data-level concurrency and optimized the computing flow. We first defined a three-dimensional grid, in which each thread calculates a sub-block data to easily facilitate the spatial and spectral neighborhood data searches in noise estimation, which is one of the most important steps involved in OMNF. Then, we optimized the processing flow and computed the noise covariance matrix before computing the image covariance matrix to reduce the original hyperspectral image data transmission. These optimization strategies can greatly improve the computing efficiency and can be applied to other feature extraction algorithms. The proposed parallel feature extraction algorithm was implemented on an Nvidia Tesla GPU using the compute unified device architecture and basic linear algebra subroutines library. Through the experiments on several real hyperspectral images, our GPU parallel implementation provides a significant speedup of the algorithm compared with the CPU implementation, especially for highly data parallelizable and arithmetically intensive algorithm parts, such as noise estimation. In order to further evaluate the effectiveness of G-OMNF, we used two different applications: spectral unmixing and classification for evaluation. Considering the sensor scanning rate and the data acquisition time, the proposed parallel implementation met the on-board real-time feature extraction.