基于ATmega32A单片机的图像采集系统设计

为了解决图像感光传感器采集和处理的问题,设计一种用最精简的硬件资源作为采集平台,通过软件的方法实现图像数据处理、显示、保存的方案。在此基础上,选择ATmega32A单片机和OV9650图像感光传感器实现了图像采集系统,为图像感光传感器的学习,应用和开发提供了可操作的流程。

一种基于区域最大值的LED显示屏亮度特征数据提取方法

LED显示屏亮度信息的采集是屏幕显示质量评估的前提,现有的亮度分析仪采集方法不仅速度慢而且对采集到的数据评估不准确,缺乏说服力。利用CCD图像传感器采集到的图像,不仅信息量大而且事实性好。分析了LED显示屏感光图像的特征,由LED亮度特征数据与感光单元灰度值之间的关系,提出了基于区域最大值的LED显示屏目标亮度数据提取算法。该方法利用数字图像处理技术,基于最大类间方差的阈值设置法分离出目标与背景,通过确定目标区域最大值坐标,并结合灰度模板卷积算法实现了对目标亮度的快速准确提取。实践证明该方法较已有的LED显示屏亮度特征数据提取算法有了一定的改善。

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.

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.

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.

怎样挑选称心如意的DV

随着时代的发展，往日遥不可及的DV（数码摄像机）早已走入寻常百姓家。DV不仅可以留下旅游中的欢乐，可以记录亲朋好友相聚的场面，还可以摄下生命的精彩瞬间。但是，面对琳琅满目的DV产品，我们该如何挑选呢？

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.

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.

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.

Atmospheric correction of remote sensing imagery based on the surface spectrum’s vector space

Due to the atmosphere effect,the qualities of images decrease conspicuously,practically in the visible bands,in the processing of earth observation by the satellite-borne sensors.Thus,removing the atmosphere effects has become a key step to improve the qualities of images and to retrieve the actual reflectivity of surface features.An atmospheric correction approach,called ACVSS(Atmospheric Correction based Vector Space of Spectrum),is proposed here based on the vector space of the features' spectrum.The reflectance image of each band is retrieved first according to the radiative transfer equation,then the spectrum's vector space is constructed using the infrared bands,and finally the residual errors of the reflectance images in the visible bands are corrected based on the pixel position in the spectrum's vector space.The proposed methodology is verified through atmospheric correction on Landsat-7 ETM+ imagery.The experimental results show that our method is more accurate and the corrected image is more distinct,compared with those offered by current popular atmospheric correction software.

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.

Ship detection in optical remote sensing image based on visual saliency and AdaBoost classifier

In this paper, firstly, target candidate regions are extracted by combining maximum symmetric surround saliency detection algorithm with a cellular automata dynamic evolution model. Secondly, an eigenvector independent of the ship target size is constructed by combining the shape feature with ship histogram of oriented gradient(S-HOG) feature, and the target can be recognized by Ada Boost classifier. As demonstrated in our experiments, the proposed method with the detection accuracy of over 96% outperforms the state-of-the-art method. efficiency switch and modulation.