CCD感光图像质量分析研究

介绍了感光图像曲线回归和求解被测物体尺寸的数学表达式，并分析了焦距、光圈、温度对感光图像质量的影响。

大展宏图的挠性板——走出自我,进入刚性板市场

很多年以来人们就在说:“挠性电路的时代要到来了。”挠性研究者肯格雷欧博士也这样说过。他认为凭着非粘结的挠性基材的发展及其在军事和汽车工业上的应用,挠性板应该很快的发展起来。然而,事实并非如此。非粘结挠性基材没有被广泛接受,军事上的应用急剧下降,汽车上的应用也没有得到迅猛的发展。

Numerical Simulation and Analysis of Bipolar Junction Photogate Transistor for CMOS Image Sensor

A new photodetector--bipolar junction photogate transistor is presented for CMOS image sensor and its analytical model is also established.With the technical parameter of the 0.6μm CMOS process,the bipolar junction photogate transistor is analyzed and simulated.The simulated results illustrate that the bipolar junction photogate transistor has the similar characteristics of the traditional photogate transistor.The photocurrent density of the bipolar junction photogate transistor increases exponentially with the incidence light power due to introducing the injection p+n junction.Its characteristic of blue response is rather improved compared to the traditional photogate transistor that benefits to increase the color photograph made up of the red,the green,and the blue.

Comparison Between Four Methods for Data Fusion of ETM+ Multispectral and Pan Images

Four data fusion methods, principle component transform (PCT), brovey transform (BT), smoothing filter-based intensity modulation(SFIM), and hue, saturation, intensity (HSI), are used to merge Landsat-7 ETM+ multispectral bands with ETM+ panchromatic band. Each of them improves the spatial resolution effectively but distorts the original spectral signatures to some extent. SFIM model can produce optimal fusion data with respect to preservation of spectral integrity. However, it results the most blurred and noisy image if the coregistration between the multispectral and pan images is not accurate enough. The spectral integrity for all methods is preserved better if the original multispectral images are within the spectral range of ETM+ pan image.

Quantitative Relationship Between Multi-Angle Polarized Reflectance and BRDF of Rock

The traditional remote sensing mainly detects the ground vertically to obtain the 2D information but it is hard to get adequate parameters for the quantitative remote sensing to invert land features. The multi-angle observation can get more detailed and reliable 3D structural parameters of targets, so it makes the quantitative remote sensing applicable. During the process of reflecting, scattering and transmitting the electromagnetic wave, minerals and rocks could reveal the polarized features related to the nature of themselves. Therefore, it has become a new approach of quantitative remote sensing to detect multi-angle polarized information of minerals and rocks. In respect that the polarized reflectance always goes with the bidirectional one, we can obtain the 3D spatial distribution of targets by a polarized means together with detecting its bi-directional reflectance. From the perspective of multi-angle polarized remote sensing mechanism, the quantitative relationship between multi-angle polarized reflectance and the BRDF is studied in this paper. And it is testified that the bi-directional reflectance, polarized reflectance of 45° and the mean value of polarized reflectance are equal to that of the corresponding azimuth angle, zenith angle, detection angle and detection channels in 27t space by experiment.

Speed-up for N-FINDR algorithm

N-FINDR is a very popular algorithm of endmember (EM) extraction for its automated property and high efficiency. Unfortunately, innumerable volume calculation, initial random selection of EMs and blind searching for EMs lead to low speed of the algorithm and limit the applications of the algorithm. So in this paper two measures are proposed to speed up the algorithm. One of the measures is substituting distance calculation for volume calculation. Thus the avoidance of volume calculation greatly decreases the computational cost. The other measure is resorting dataset in terms of pixel purity likelihood based on pixel purity index (PPI) concept. Then, initial EMs can be selected well-founded and a fast searching for EMs is achieved. Numerical experiments show that the two measures speed up the original algorithm hundreds of times as the number of EMs is more than ten.

Analysis of Spectral Characteristics Based on Optical Remote Sensing and SAR Image Fusion

Because of cloudy and rainy weather in south China, optical remote sens-ing images often can＇t be obtained easily. With the regional trial results in Baoying, Jiangsu province, this paper explored the fusion model and effect of ENVISAT/SAR and HJ-1A satel ite multispectral remote sensing images. Based on the ARSIS strat-egy, using the wavelet transform and the Interaction between the Band Structure Model （IBSM）, the research progressed the ENVISAT satel ite SAR and the HJ-1A satel ite CCD images wavelet decomposition, and low/high frequency coefficient re-construction, and obtained the fusion images through the inverse wavelet transform. In the light of low and high-frequency images have different characteristics in differ-ent areas, different fusion rules which can enhance the integration process of self-adaptive were taken, with comparisons with the PCA transformation, IHS transfor-mation and other traditional methods by subjective and the corresponding quantita-tive evaluation. Furthermore, the research extracted the bands and NDVI values around the fusion with GPS samples, analyzed and explained the fusion effect. The results showed that the spectral distortion of wavelet fusion, IHS transform, PCA transform images was 0.101 6, 0.326 1 and 1.277 2, respectively and entropy was 14.701 5, 11.899 3 and 13.229 3, respectively, the wavelet fusion is the highest. The method of wavelet maintained good spectral capability, and visual effects while improved the spatial resolution, the information interpretation effect was much better than other two methods.

Human-inspired lighting control in robot systems with the intention of glare avoidance

This paper presents some human-inspired strategies for lighting control in a robot system for best scene interpretation,where the main intention is to avoid possible glares or highlights occurring in images. It firstly compares the characteristics of human eyes and robot eyes. Then some evaluation criteria are addressed to assess the lighting conditions. A bio-inspired method is adopted to avoid the visual glare which is caused by either direct illumination from large light sources or indirect illumination reflected by smooth surfaces. Appropriate methods are proposed to optimize the pose and optical parameters of the light source and the vision camera.

Fabrication of PdSe2/GaAs Heterojunction for Sensitive Near-Infrared Photovoltaic Detector and Image Sensor Application

In this study,we have developed a high-sensitivity,near-infrared photodetector based on PdSe2/GaAs heterojunction,which was made by transferring a multilayered PdSe2 film onto a planar GaAs.The as-fabricated PdSe2/GaAs heterojunction device exhibited obvious photovoltaic behavior to 808 nm illumination,indicating that the near-infrared photodetector can be used as a self-driven device without external power supply.Further device analysis showed that the hybrid heterojunction exhibited a high on/off ratio of 1.16×10^5 measured at 808 nm under zero bias voltage.The responsivity and specific detectivity of photodetector were estimated to be 171.34 mA/W and 2.36×10^11 Jones,respectively.Moreover,the device showed excellent stability and reliable repeatability.After 2 months,the photoelectric characteristics of the near-infrared photodetector hardly degrade in air,attributable to the good stability of the PdSe2.Finally,the PdSe2/GaAs-based heterojunction device can also function as a near-infrared light sensor.

Classification of hyperspectral remote sensing images based on simulated annealing genetic algorithm and multiple instance learning

A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algonthrn and multiple instance learning （MIL）. The band selection method was proposed from subspace decomposition, which combines the simulated annealing algorithm with the genetic algorithm in choosing different cross-over and mutation probabilities, as well as mutation individuals. Then MIL was combined with image segmentation, clustering and support vector machine algorithms to classify hyperspectral image. The experimental results show that this proposed method can get high classification accuracy of 93.13% at small training samples and the weaknesses of the conventional methods are overcome.

Spectrum Feature Retrieval and Comparison of Remote Sensing Images Using Improved ISODATA Algorithm

Due to the large quantities of data and high relativity of the spectra of remote sensing images, K-L transformation is used to eliminate the relativity. An improved ISODATA(Interative Self-Organizing Data Analysis Technique A) algorithm is used to extract the spectrum features of the images. The computation is greatly reduced and the dynamic arguments are realized. The comparison of features between two images is carried out, and good results are achieved in simulation.

320×240 Pixels CMOS Digital Image Sensor with Wide Dynamic Range

A 320×240 CMOS image sensor is demonstrated,which is implemented by a standard 0.6 μm 2P2M CMOS process.For reducing the chip area,each 2×2-pixel block shares a sample/hold circuit,analog-to-digital converter and 1-b memory.The 2×2 pixel pitch has an area of 40 μm×40 μm and the fill factor is about 16%.While operating at a low frame rate,the sensor dissipates a very low power by power-management circuit making pixel-level comparators in an idle state.A digital correlated double sampling,which eliminates fixed pattern noise,improves SNR of the sensor, and multiple sampling operations make the sensor have a wide dynamic range.

Assessment of Soil Water Content and Remote Sensing Techniques--Case Study of Kiwi Orchard （Portugal）

Soil water management plays an important role in the response of kiwi plants （Actinidia deliciosa A. Chev.）. In GuimarSes district soil moisture content is monitored in kiwifi＇uit orchard as a routine parameter. Drip irrigation system is the method used. This crop tends to have high water requirements and extends over a wide area in Portugal, requiring innovative solutions to achieve better benefits. A method that correlates soil and crop conditions with the parameters of remote sensing was established in this study. To assess the level of accuracy of soil moisture measurements from satellites, it is important to compare satellite image with ground real data （namely the frequency domain reflectometry （FDR）, Diviner 2000）. The combination of multispectral satellite images produces an image representative of vegetation vigour, density and health. In this study, Landsat satellite images （2011 and 2013） are used and vegetation indexes are calculated for different periods of time, using the software Idrisi Taiga. The information of vegetation indexes is crossed with data of soil moisture, in situ, to establish a correlation between both of them. Thus, it allows to improve the soil water content monitoring, in particular for the soil water balance optimization and its effect on kiwi biornass production.

Fusion and Classification of Beijing-1 Small Satellite Remote Sensing Image for Land Cover Monitoring in Mining Area

In order to promote the application of Beijing-1 small satellite(BJ-1) remote sensing data,the multispectral and panchromatic images captured by BJ-1 were used for land cover classification in Pangzhuang Coal Mining.An improved Intensity-Hue-Saturation(IHS) fusion algorithm is proposed to fuse panchromatic and multispectral images,in which intensity component and panchromatic image are combined using the weights determined by edge pixels in the panchromatic image identified by grey absolute correlation degree.This improved IHS fusion algorithm outper-forms traditional IHS fusion method to a certain extent,evidenced by its ability in preserving spectral information and enhancing spatial details.Dempster-Shafer(D-S) evidence theory was adopted to combine the outputs of three member classifiers to generate the final classification map with higher accuracy than that by any individual classifier.Based on this study,we conclude that Beijing-1 small satellite remote sensing images are useful to monitor and analyze land cover change and ecological environment degradation in mining areas,and the proposed fusion algorithms at data and decision levels can integrate the advantages of multi-resolution images and multiple classifiers,improve the overall accuracy and produce a more reliable land cover map.

Exploration of computerized image processing in underexposed and overexposed X-rays of bones and joints

Objective: To study the effective computerized image processing of underexposed and overexposed X-rays of bones and joints. Methods: Ninety-nine conventional X-ray images (82 were overexposed and 17 were underexposed),scanned by an UMAX Astra 4000U Scanner, were converted into digital images on the basis of their analog images. A computerized imaging processing program consisting of five functional modules such as Contrast Stretch, Fast Flourier Transform (FFT), Image Smoothing Modules, Inverse Fast Flourier Transform (IFFT) and Nonlinear Transform performed image contrast stretch and smoothing. Three senior doctors from hospital image sections made their evaluation of all the processed images. Results: Of 82 overexposed films, 71 met the clinical requirements after image processing, and 11 were unable to be applied to clinical diagnosis, accounting for 87% and 13% respectively. Of the other 17 underexposed X-ray images, 11 met the clinical requirements while 6 were not, making a percentage of 64 and 35. Conclusion: Image contrast stretch and smoothing processing are significantly effective on conventional X-ray images which were inappropriately exposed, and can avoid more X-ray radiation caused by handling of radiological photograph again. This method can decrease hospital cost and provide acute and effective X-ray examinations for the treatment and cure for critical patients.

Land cover classification of remote sensing imagery based on interval-valued data fuzzy c-means algorithm

There is a certain degree of ambiguity associated with remote sensing as a means of performing earth observations.Using interval-valued data to describe clustering prototype features may be more suitable for handling the fuzzy nature of remote sensing data,which is caused by the uncertainty and heterogeneity in the surface spectral reflectance of ground objects.After constructing a multi-spectral interval-valued model of source data and defining a distance measure to achieve the maximum dissimilarity between intervals,an interval-valued fuzzy c-means(FCM)clustering algorithm that considers both the functional characteristics of fuzzy clustering algorithms and the interregional features of ground object spectral reflectance was applied in this study.Such a process can significantly improve the clustering effect;specifically,the process can reduce the synonym spectrum phenomenon and the misclassification caused by the overlap of spectral features between classes of clustering results.Clustering analysis experiments aimed at land cover classification using remote sensing imagery from the SPOT-5 satellite sensor for the Pearl River Delta region,China,and the TM sensor for Yushu,Qinghai,China,were conducted,as well as experiments involving the conventional FCM algorithm,the results of which were used for comparative analysis.Next,a supervised classification method was used to validate the clustering results.The final results indicate that the proposed interval-valued FCM clustering is more effective than the conventional FCM clustering method for land cover classification using multi-spectral remote sensing imagery.

High-resolution remote sensing mapping of global land water

Land water, one of the important components of land cover, is the indispensable and important basic information for climate change studies, ecological environment assessment, macro-control analysis, etc. This article describes the overall study on land water in the program of global land cover remote sensing mapping. Through collection and processing of Landsat TM/ETM+, China's HJ-1 satellite image, etc., the program achieves an effective overlay of global multi-spectral image of 30 m resolution for two base years, namely, 2000 and 2010, with the image rectification accuracy meeting the requirements of 1:200000 mapping and the error in registration of images for the two periods being controlled within 1 pixel. The indexes were designed and selected reasonably based on spectral features and geometric shapes of water on the scale of 30 m resolution, the water information was extracted in an elaborate way by combining a simple and easy operation through pixel-based classification method with a comprehensive utilization of various rules and knowledge through the object-oriented classification method, and finally the classification results were further optimized and improved by the human-computer interaction, thus realizing high-resolution remote sensing mapping of global water. The completed global land water data results, including Global Land 30-water 2000 and Global Land 30-water 2010, are the classification results featuring the highest resolution on a global scale, and the overall accuracy of self-assessment is 96%. These data are the important basic data for developing relevant studies, such as analyzing spatial distribution pattern of global land water, revealing regional difference, studying space-time fluctuation law, and diagnosing health of ecological environment.

Classification of hyperspectral remote sensing images using frequency spectrum similarity

An algorithm of hyperspectral remote sensing images classification is proposed based on the frequency spectrum of spectral signature.The spectral signature of each pixel in the hyperspectral image is taken as a discrete signal,and the frequency spectrum is obtained using discrete Fourier transform.The discrepancy of frequency spectrum between ground objects' spectral signatures is visible,thus the difference between frequency spectra of reference and target spectral signature is used to measure the spectral similarity.Canberra distance is introduced to increase the contribution from higher frequency components.Then,the number of harmonics involved in the proposed algorithm is determined after analyzing the frequency spectrum energy cumulative distribution function of ground object.In order to evaluate the performance of the proposed algorithm,two hyperspectral remote sensing images are adopted as experimental data.The proposed algorithm is compared with spectral angle mapper (SAM),spectral information divergence (SID) and Euclidean distance (ED) using the product accuracy,user accuracy,overall accuracy,average accuracy and Kappa coefficient.The results show that the proposed algorithm can be applied to hyperspectral image classification effectively.

A Fast Clonal Selection Algorithm for Feature Selection in Hyperspectral Imagery

Clonal selection feature selection algorithm （CSFS） based on clonal selection algorithm （CSA）, a new computational intelligence approach, has been proposed to perform the task of dimensionality reduction in high-dimensional images, and has better performance than traditional feature selection algorithms with more computational costs. In this paper, a fast clonal selection feature selection algorithm （FCSFS） for hyperspectral imagery is proposed to improve the convergence rate by using Cauchy mutation instead of non-uniform mutation as the primary immune operator. Two experiments are performed to evaluate the performance of the proposed algorithm in comparison with CSFS using hyperspectral remote sensing imagery acquired by the pushbroom hyperspectral imager （PHI） and the airborne visible/infrared imaging spectrometer （AVlRIS）, respectively. Experimental results demonstrate that the FCSFS converges faster than CSFS, hence providing an effective new option for dimensionality reduction of hyperspectral remote sensing imagery.

The spatial scaling effect of the discrete-canopy effective leaf area index retrieved by remote sensing

The leaf area index(LAI) is a critical biophysical variable that describes canopy geometric structures and growth conditions.It is also an important input parameter for climate,energy and carbon cycle models.The scaling effect of the LAI has always been of concern.Considering the effects of the clumping indices on the BRDF models of discrete canopies,an effective LAI is defined.The effective LAI has the same function of describing the leaf density as does the traditional LAI.Therefore,our study was based on the effective LAI.The spatial scaling effect of discrete canopies significantly differed from that of continuous canopies.Based on the directional second-derivative method of effective LAI retrieval,the mechanism responsible for the spatial scaling effect of the discrete-canopy LAI is discussed and a scaling transformation formula for the effective LAI is suggested in this paper.Theoretical analysis shows that the mean values of effective LAIs retrieved from high-resolution pixels were always equal to or larger than the effective LAIs retrieved from corresponding coarse-resolution pixels.Both the conclusions and the scaling transformation formula were validated with airborne hyperspectral remote sensing imagery obtained in Huailai County,Zhangjiakou,Hebei Province,China.The scaling transformation formula agreed well with the effective LAI retrieved from hyperspectral remote sensing imagery.