CCD数字相机的电源系统设计

分析了CCD相机外部单路供电,并为实现多路DC/DC转换功能,提出了一种开关型稳压电源与线性稳压电源相结合的系统设计方案。分析了具有BOOST/BUCK功能的开关电源和线性稳压电源的工作特性,并结合CCD相机的整体设计要求,实现了相机电源的功耗低、发热量小、EMI干扰轻的设计目标,从而提升了CCD相机的整体性能。     

Urban heat island intensity during winter over metropolitan cities of India using remote-sensing techniques: impact of urbanization

ABSTRACT

The present study aims to quantify spatial relationship of land-use and land-cover (LULC) changes and land surface temperature (LST) using remote-sensing and geographical information system techniques over 10 major metropolitan cities of India. For this purpose, Landsat 7 Enhanced Thematic Mapper Plus images of these cities during winter period from 2001 to 2013 are used. Statistical analysis of the LULC classification has shown overall accuracy ranging between 85% and 88%. The LULC classification and estimated LST using the satellite imageries reveals the presence of multiple urban heat islands (UHIs) and their increase in number in all cities. Significant increase in built-up/urban areas are noticed at the expense of vegetated lands and barren lands over Lucknow, Nagpur, and Jaipur, whereas in Hyderabad and Bengaluru the built-up area and the dry/barren lands are observed to be increasing, at the expense of crop/grass lands. Higher UHI intensities in the range of 8.9–10.3°C are noticed over Mumbai, Nagpur, and Hyderabad compared to the other cities. Higher temperature zones (hotspots) are found to be increasing in the built-up area as well as in barren lands. Varying increase in UHI intensity among the cities is noticed which may have implications in the regional climate over the cities.     

Marsobot: design and performance characterization of a low-cost,ground-based multispectral camera on an open source rover

Open source, low cost sensors, and robotic systems have developed to the point of being able to produce meaningful, repeatable results in real-life applications. We developed a low-cost, open source multispectral camera mounted on a small custom-built robotic rover. We compared the performance of our camera with a commercial multispectral camera and a laboratory spectrometer using minerals commonly found on Mars that exhibited different reflectance values in visible and near-infrared wavelengths. Our camera performed favourably when compared to the commercial instruments. It is a very cost effective solution for operating in extreme situations, where damage to instruments is possible. Our total system of rover and sensor would, therefore, be very useful for operating in delicate and inaccessible environments where damage to the area under investigation and to human observers is of concern.     

Remote monitoring of buildings using a ground-based SAR: Application to cultural heritage survey

In this Letter, the authors describe an innovative application of radar interferometric techniques aimed to monitor structural deformations of buildings. The proposed application is based on the use of ground-based instrumentation able to operate as Interferometric Synthetic Aperture Radar (InSAR). Preliminary experimental results on a model of an historical building encourage the development of this technique for use in architectural heritage survey.     

The impact of assimilation of satellite derived wind observations for the prediction of a monsoon depression over India using a mesoscale model

The Penn State/NCAR mesoscale model (MM5) has been used in this study to ingest and assimilate the INSAT‐CMV (Indian National Satellite System‐Cloud Motion Vector) wind observations using analysis nudging (four‐dimensional data assimilation, FDDA) to improve the prediction of a monsoon depression which occurred over the Bay of Bengal, India during 28 July 2005 to 31 July 2005. To determine the impact of assimilation of INSAT‐CMV winds on the prediction of a monsoon depression, three sets of numerical experiments (NOFDDA, FDDA and FDDA CMV) were designed. While the FDDA CMV run assimilated satellite derived winds only, the FDDA run assimilated both satellite and conventional observations. The NOFDDA run used neither satellite nor conventional observations. The results of the study indicate that the simulated sea level pressure field from the FDDA run is more consistent with the sea level pressure field from NCEP‐FNL compared to the FDDA CMV and NOFDDA runs. The highest correlation and lowest rms error of the sea level pressure field are associated with the FDDA run, and this provides a quantitative verification of the improvement due to the assimilation of satellite derived winds and the conventional upper air observations for the prediction of monsoon depression. All the three model simulated winds are in good agreement with the analysis winds at 850 hPa, 500 hPa and 200 hPa levels. The simulated structure of the spatial precipitation pattern for the assimilation experiments (FDDA and FDDA CMV) are closer to the TRMM observations with more rainfall simulated over the east coast regions in the assimilation experiments. The rms errors of the wind speed for the FDDA run show lower values at 500 hPa for all the three model runs, with a reduction in all three levels of up to 0.8–1.4 m s^?1 for the FDDA run and 0.5–1.9 m s^?1 for the FDDA CMV run with respect to the NOFDDA run. The statistical significance of the sea level pressure and the precipitation differences between the FDDA and the NOFDDA as well as the differences between the FDDA CMV and the NOFDDA have been calculated using the two‐tailed Student's t‐test and were found to be statistically significant. The influence of varying the nudging coefficients in the FDDA experiment has been studied.     

Mapping of rice-cropping pattern and cultural type using remote-sensing and ancillary data: a case study for South and Southeast Asian countries

A geospatial database on the spatial distribution of rice areas and rice cultural types of major rice-producing countries of South and Southeast Asia has been developed in this study using remote-sensing and ancillary data sets. Multitemporal SPOT VGT normalized difference vegetation index (NDVI) data for the period 2009–2010 were used for the analysis. The classification was performed adopting ISODATA clustering to build a non-agricultural area mask followed by rice area mapping. The derived rice area was stratified by logical modelling of ancillary data sets into five rice cultural types: irrigated wet, upland, flood-prone, drought-prone, and deep-water. The uniqueness of this study is a synergistic approach based solely on single-source, high-temporal remote-sensing data coupled with ancillary data, which demonstrate the application of SPOT VGT NDVI data in building a geospatial database for rice crops over a wide spatial extent. This approach was adopted for cost effectivity as the study extent was vast and thus lacking ground truth information. Comparison of the derived rice area against the reported literature values for validation yielded a good correlation (linear coefficient of determination, R² = 0.95–0.99). The high-temporal resolution NDVI data enabled effective characterization of vegetation phenology. The derived spatial outputs can be used in various studies associated with the assessment of greenhouse gas emissions from paddy fields, change detection, and inputs to crop simulation models, which are significantly related to different rice cultural types.     

HiTempo: a platform for time-series analysis of remote-sensing satellite data in a high-performance computing environment

Course resolution earth observation satellites offer large data sets with daily observations at global scales. These data sets represent a rich resource that, because of the high acquisition rate, allows the application of time-series analysis methods. To research the application of these time-series analysis methods to large data sets, it is necessary to turn to high-performance computing (HPC) resources and software designs. This article presents an overview of the development of the HiTempo platform, which was designed to facilitate research into time-series analysis of hyper-temporal sequences of satellite image data. The platform is designed to facilitate the exhaustive evaluation and comparison of algorithms, while ensuring that experiments are reproducible. Early results obtained using applications built within the platform are presented. A sample model-based change detection algorithm based on the extended Kalman filter has been shown to achieve a 97% detection success rate on simulated data sets constructed from MODIS time series. This algorithm has also been parallelized to illustrate that an entire sequence of MODIS tiles (415 tiles over 9 years) can be processed in under 19 minutes using 32 processors.     

The integration of digital camera derived images with a computer based diabetes register for use in retinal screening

Exeter district provides a retinal screening service based on a mobile non-mydriatic camera operated by a dedicated retinal screener visiting general practices on a 2-yearly cycle. Digital attachments to eye cameras can now provide a cost effective alternative to the use of film in population based eye screening programmes. Whilst the manufacturers of digital cameras provide a database for the storage of pictures, the images do not as yet interface readily with the rest of the patient's computer held data or allow for a sophisticated grading, reporting and administration system. The system described is a development of the Exeter diabetes register (EXSYST) which can import digitally derived pictures from either Ris-Lite TM and Imagenet TM camera systems or scanned Polaroids Pictures can be reported by the screener, checked by a consultant ophthalmologist via the hospital network, and a report, consisting of colour pictures, map of relevant pathology and referral recommendations produced. This concise report can be hard copied inexpensively on a high resolution ink-jet printer to be returned to the patient's general practitioner. Eye images remain available within the hospital diabetes centre computer network to facilitate shared care. This integrated system would form an ideal platform for the addition of computer based pathology recognition and total paperless transmission when suitable links to GP surgeries become available.     

A global NDVI and EVI reference data set for land-surface phenology using 13 years of daily SPOT-VEGETATION observations

Time series of vegetation indices (VIs) obtained by remote sensing are widely used to study phenology on regional and global scales. The aim of the study is to design a method and to produce a reference data set describing the seasonal and inter-annual variability of the land-surface phenology on a global scale. Specific constraints are inherent in the design of such a global reference data set: (1) the high diversity of vegetation types and the heterogeneous conditions of observation, (2) a near-daily resolution is needed to follow the rapid changes in phenology, (3) the time series used to depict the baseline vegetation cycle must be long enough to be representative of the current vegetation dynamic and encompass anomalies, and (4) a spatial resolution consistent with a land-cover-specific analysis should be privileged. This study focuses on the SPOT (Satellite Pour l’Observation de la Terre)-VEGETATION sensor and its 13-year time series of reflectance values. Five steps addressing the noise and the missing data in the reflectance time series were selected to process the daily multispectral reflectance observations. The final product provides, for every pixel, three profiles for 52 × 7-day periods: a mean, a median, and a standard deviation profile. The mean and median profiles represent the reference seasonal pattern for variation of the vegetation at a specific location whereas the standard deviation profile expresses the inter-annual variability of VIs. A quality flag at the pixel level demonstrated that the reference data set can be considered as a reliable representation of the vegetation phenology in most parts of the Earth.     

Reliable pose estimation of underwater dock using single camera: a scene invariant approach

It is well known that docking of Autonomous Underwater Vehicle (AUV) provides scope to perform long duration deep-sea exploration. A large amount of literature is available on vision-based docking which exploit mechanical design, colored markers to estimate the pose of a docking station. In this work, we propose a method to estimate the relative pose of a circular-shaped docking station (arranged with LED lights on periphery) up to five degrees of freedom (5-DOF, neglecting roll effect). Generally, extraction of light markers from underwater images is based on fixed/adaptive choice of threshold, followed by mass moment-based computation of individual markers as well as center of the dock. Novelty of our work is the proposed highly effective scene invariant histogram-based adaptive thresholding scheme (HATS) which reliably extracts positions of light sources seen in active marker images. As the perspective projection of a circle features a family of ellipses, we then fit an appropriate ellipse for the markers and subsequently use the ellipse parameters to estimate the pose of a circular docking station with the help of a well-known method in Safaee-Rad et al. (IEEE Trans Robot Autom 8(5):624–640, 1992). We analyze the effectiveness of HATS as well as proposed approach through simulations and experimentation. We also compare performance of targeted curvature-based pose estimation with a non-iterative efficient perspective-n-point (EPnP) method. The paper ends with a few interesting remarks on vantages with ellipse fitting for markers and utility of proposed method in case of non-detection of all the light markers.     

Visual measurement of pile movements for the foundation work using a high-speed line-scan camera

When a construction company builds a high structure, many piles should be driven into the ground by a hammer whose weight is typically 7000 kg in order to make the ground under the structure safe and strong. It is essential to determine whether a pile is penetrated into the ground enough to support the weight of the structure since ground characteristics at different locations are different from each other. In order to measure the pile movements, a specially designed mark is designed and a high-speed line-scan camera is used in this paper. A mark, in which black and white right-angled triangles are stacked, is used for the measurements of movement information for vertical distances, horizontal distances and rotational angles simultaneously. The measurement performance of dynamic characteristics of the pile at impact instant is improved dramatically compared with the equipment using speckle laser sensors. The developed visual measurement system is successfully applied for a real penetration measurement system for building construction.     

Bang-bang strategies using interrupted observations for steering a random motion of a point

Consider the random motion in the plane of a point M, whose velocity u = (u₁, u₂) is perturbed by an R²-valued Gaussian white noise. The point is equipped with an observation mechanism which provides interrupted noisy observations of a given target. The interruptions are characterized by a jump Markov process taking on the values 0 or 1, and using these observations the point M wishes to steer itself into the target. Sufficient conditions on optimal velocity laws (strategies) u are derived, and suboptimal strategies computed. The numerical results obtained show that the suboptimal strategies obey the separation principle of optimal stochastic control theory.     

Localization of a door handle of unknown geometry using a single camera for door-opening with a mobile manipulator

Door-opening by a robot is a challenging task. Current approaches to localization of door handles are only applicable to certain types of handles or require the use of learning algorithms. In this paper, a method for localizing a door handle of unknown geometry is presented. The localization is performed by fitting a bounding box around the door handle. The proposed method extracts a three-dimensional (3D) point cloud using the optical flow calculated from images taken with a single CCD camera. The point cloud is then segmented to separate the door and the handle points. A?bounding box is fitted to the door handle. The box is oriented along the principal components of the handle points after they are projected into the plane of the door, and the normal to the door. The box performs the function of both localizing the handle and estimating its pose. Experiments performed on a set of various door handles demonstrated the effectiveness of the proposed method.     

Assessment of forest damage caused by an ice storm using multi-temporal remote-sensing images: a case study from Guangdong Province

In early 2008, forest ecosystems in southern China suffered damage due to a severe ice storm disaster. The area and degree of forest damage caused by the ice storm was assessed using Satellite Pour l’Observation de la Terre (SPOT)-Vegetation images for Guangdong Province acquired between 1999 and 2008. By using the maximum value composition method and image thresholding techniques, the forest vegetation loss, expressed as the change in net primary productivity (NPP) and two indicators (I₁, I₂), was estimated. The damage threshold was determined by comparing the standard deviation of pixels of the undamaged areas in 2008 and other years without any disaster, which was 10%. The area of damaged forest vegetation was 47,670 km², with the northern Guangdong Province most seriously affected. The total loss of NPP for forest vegetation was 50,578,055 t (DW) year^?1, with 52 counties (43.7%) suffering forest vegetation damage. Evergreen coniferous forest was most widely affected, but evergreen broad-leaved forest was the most severely damaged vegetation type. Terrain topography influenced the damage to forest vegetation, which was found to increase with increasing elevation and slope gradient. The range and degree of damaged forest determined by remote-sensing data is consistent with the extent of the ice storm, indicating that this study provides a new approach for rapid assessment of forest disasters at a regional scale.     

Risk assessment for wildfire occurrence in high-voltage power line corridors by using remote-sensing techniques: a case study in Hubei Province,China

Wildfires are a significant problem for power line maintenance that can lead to widespread power outages and economic loss in China. Remote sensing (RS) and geographic information system (GIS) technologies and their derived applications are effective tools for power line surveillance and disaster prevention. Using multi-source data and remote-sensing techniques, a risk assessment of wildfire occurrence in high-voltage power line corridors in Hubei Province, China, is presented in this study. Both natural and human causes are considered in the assessment, including a traditional Chinese spring custom. Historical ignition points along power line corridors in Hubei Province that occurred from 2009 to 2014 were collected as training data to create a quantitative analysis. Next, a logistic regression model was applied, and ignition probability maps were produced for power lines. Several wildfire accidents that occurred in 2015 were used as validation data to evaluate the performance of the proposed method. The experimental results indicated that the drought conditions significantly influenced wildfires as natural cause, and human activities play important roles in causing wildfires during specific time periods. The effectiveness and robustness of our method is thereby demonstrated, and our method can be used to provide valuable suggestions for wildfire management for the electrical department in Hubei Province.     

Stacked Autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping

Land-cover mapping is an important research topic with broad applicability in the remote-sensing domain. Machine learning algorithms such as Maximum Likelihood Classifier (MLC), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest (RF) have been playing an important role in this field for many years, although deep neural networks are experiencing a resurgence of interest. In this article, we demonstrate early efforts to apply deep learning-based classification methods to large-scale land-cover mapping. Based on the Stacked Autoencoder (SAE), one of the deep learning models, we built a classification framework for large-scale remote-sensing image processing. We adjusted and optimized the model parameters based on our test samples. We compared the performance of the SAE-based approach with traditional classification algorithms including RF, SVM, and ANN with multiple performance analytics. Results show that the SAE classifier trained with an entire set of African training samples achieves an overall classification accuracy of 78.99% when assessed by test samples collected independently of training samples, which is higher than the accuracies achieved by the other three classifiers (76.03%, 77.74%, and 77.86% of RF, SVM, and ANN, respectively) based on the same set of test samples. We also demonstrated the advantages of SAE in prediction time and land-cover mapping results in this study.     

A new approach for using time-series remote-sensing images to detect changes in vegetation cover and composition in drylands: a case study of eastern Kenya

Vegetation cover and composition are important aspects of the dryland environment because they provide livelihood to humans and also protect soil resources against erosion. Currently, scientists are advancing various techniques for detecting vegetation degradation in the drylands and the possibilities for its control. This study contributed through the testing of time-series mixed-effects modelling of the normalized difference vegetation index (NDVI) and rainfall relationship to trace the footprints of vegetation dynamics in the drylands. The approach aimed at providing guidelines for quick diagnosis of the changes in vegetation cover and composition to trigger necessary action. The mixed-effects technique used in this study is a novel regression approach for simultaneous modelling of the NDVI–rainfall relationship in different dominant vegetation types. Its time-series application with Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI images between 1982 and 2008 was tested in eastern Kenya. The results show how the original dominant vegetation types had been converted to cereal croplands, open grasslands, or reduced to bare ground in a span of 27 years. In some places, it shows how the changes in vegetation composition resulted in the overall loss of vegetation cover. Field validation positively confirmed these observations; thus, indicating that the method was a promising tool for tracing vegetation dynamics in the drylands. In spite of its success, the method was found to be only useful in detecting changes in large areas with dominant vegetation types. The technique can therefore be recommended for regional analysis, and can be used as a first approximation to guide more detailed subsequent analysis.     

Implementation of a 3 times zoom lens barrel system for a 5M camera module using a mobile phone

In this study, we developed a small 3 times zoom lens barrel for a 5M camera module to load a mobile phone set. Its dimensions are 16mm × 9mm × 28mm and is constructed with 8 sheets of lenses, two step motors, some gears, a cam plate and so on.