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Tank, as a vital ground weapon, plays an irreplaceable role in the war. The article did the research of infrared image of the tank. Firstly, the 3D model of tank was established. And then the infrared radiation model of the target was constructed by analysing the infrared characteristics of the tank’s different parts.. Finally the infrared radiation value of the tank under different states was calculated and the simulation of infrared characteristics of the tank under different states was done, which will provide reference for the research on infrared characteristics of the army's battlefield target.

The rapid development of the satellite observation technology provides a very rich source of data for sea reconnaissance and ships surveillance. In the face of such a vast sea remote sensing data, it is urgent need to realize the automatic ship detection in optical remote sensing images, but the optical remote sensing images are easily affected by meteorological conditions, such as clouds, waves, which results in larger false alarm; and the weak contrast between optical remote sensing image target and background is easy to cause missing alarm. In this paper, a novel algorithm based on wavelet transform for ship target detection in optical remote sensing images is proposed, which can effectively remove these noise and interference. The segmentation of sea and land background is first applied to the image preprocessing to achieve more accurate detection results, and then discrete wavelet transform is used to deal with the part of sea background. The results show that almost all of the offshore ships can be detected, and through the comparison of the results of four different wavelet basis functions, the accuracy of ship detection is further improved.

Remote sensing system fitted on Unmanned Aerial Vehicle (UAV) can obtain clear images and high-resolution aerial photographs. It has advantages of strong real-time, flexibility and convenience, free from influence of external environment, low cost, low-flying under clouds and ability to work full-time. When an earthquake happened, it could go deep into the places safely and reliably which human staff can hardly approach, such as secondary geological disasters hit areas. The system can be timely precise in response to secondary geological disasters monitoring by a way of obtaining first-hand information as quickly as possible, producing a unique emergency response capacity to provide a scientific basis for overall decision-making processes. It can greatly enhance the capability of on-site disaster emergency working team in data collection and transmission. The great advantages of UAV remote sensing system played an irreplaceable role in monitoring secondary geological disaster dynamics and influences. Taking the landslides and barrier lakes for example, the paper explored the basic application and process of UAV remote sensing in the disaster emergency relief. UAV high-resolution remote sensing images had been exploited to estimate the situation of disaster-hit areas and monitor secondary geological disasters rapidly, systematically and continuously. Furthermore, a rapid quantitative assessment on the distribution and size of landslides and barrier lakes was carried out. Monitoring results could support relevant government departments and rescue teams, providing detailed and reliable scientific evidence for disaster relief and decision-making.

For remote sensing images with rich content of features, this paper presents a remote sensing image classification algorithm based on twin support vector machine (TWSVM) and multi-feature optimization. Firstly, we extract color feature and shape feature of remote sensing image and introduce the local angular phase (LAP) histogram, which has high texture description ability, as the texture feature. Due to these three kinds of feature represent different emphases of remote sensing image; the reasonable combination of them can be more comprehensive description of the contents of remote sensing image. Secondly, we use kernel principal component analysis (KPCA) to reduce the dimension of every kind of feature, and construct reasonable feature space based on different weights obtained by the distribution of feature space. Finally, the remote sensing image samples classification and test is completed in the TWSVM model that has better classification performance. Experimental results on the USGS test-set show that, the average classification accuracy of the proposed algorithm is reached to 93.7% compared with three popular methods. Compared with the highest classification accuracy of single feature classification algorithm, the average classification accuracy of the proposed algorithm has been improved by 16%, 14.5% and 9.2%.

Land surface temperature is playing a very important role in ground-atmosphere interaction. It is a key parameter in the global change studies. So it becomes one of important tasks in quantitative remote sensing research to make use of satellite remote sensing to retrieve land surface temperature at present. Land surface temperature is a crucial parameter of thermal energy distribution in region, the most efficient way to acquire it is through thermal infrared remote sensing images. On the basis of studies on the land surface temperature. With the support of remote sensing and GIS technology, using the single window algorithm to quantitative inversion of thermal infrared remote sensing image, obtain the spatial distribution of surface temperature change in the study area

In military fields, it is essential to monitor the dynamic of aircrafts through remote sensing images. Due to lack of automated assistive analysis methods of recent works, we propose a novel method for automatically monitoring the dynamic of aircrafts through remote sensing images in this paper. The method consists of two phases: (i) establish a priori model of aircrafts in airports and learn a Convolutional Neural Networks (CNN) classifier that identifies the state of aircrafts, and (ii) predict the states of aircrafts in the new images. The proposed method was tested on the remote sensing images of two typical airports. Experimental results show that the method is able to monitor the dynamic of aircrafts with high accuracy. We conclude that the method can report the states of aircrafts in airports correctly.

Landscape structures and process on different scale show different characteristics. In the study of specific target landmarks, the most appropriate scale for images can be attained by scale conversion, which improves the accuracy and efficiency of feature identification and classification. In this paper, the authors carried out experiments on multi-scale classification by taking the Shangri-la area in the north-western Yunnan province as the research area and the images from SPOT5 HRG and GF-1 Satellite as date sources. Firstly, the authors upscaled the two images by cubic convolution, and calculated the optimal scale for different objects on the earth shown in images by variation functions. Then the authors conducted multi-scale superposition classification on it by Maximum Likelyhood, and evaluated the classification accuracy. The results indicates that: (1) for most of the object on the earth, the optimal scale appears in the bigger scale instead of the original one. To be specific, water has the biggest optimal scale, i.e. around 25-30m; farmland, grassland, brushwood, roads, settlement places and woodland follows with 20-24m. The optimal scale for shades and flood land is basically as the same as the original one, i.e. 8m and 10m respectively. (2) Regarding the classification of the multi-scale superposed images, the overall accuracy of the ones from SPOT5 HRG and GF-1 Satellite is 12.84% and 14.76% higher than that of the original multi-spectral images, respectively, and Kappa coefficient is 0.1306 and 0.1419 higher, respectively. Hence, the multi-scale superposition classification which was applied in the research area can enhance the classification accuracy of remote sensing images .

In this contribution, a novel metric learning framework by jointly optimizing the feature space structural coherence manifested by the Cosine similarity measure and the error contribution induced by the Minkowski metric is presented with a loss function involving Mahalanobis distance measure governing the outlier robustness for maximal inter-sample and minimal intra-sample separation of the feature space vectors. The outlier’s robustness and scale variation sensitivity of the proposed measure by exploiting the prior statistical entropy of the correlated feature components in weighing the different feature dimensions according to their degree of cohesion within the data clusters and the conceptual architecture for the optimality criterion in terms of the optimal Minkowski exponent, ‘poptimal’ through semi-definite convex optimization with its lower and upper bounds of the proposed distance function have been discussed. Classification results involving special cases of the proposed distance measure on publicly available datasets validates the adequacy of the proposed methodology in remote sensing problems.

Using appropriately designed spectral filters allows to optically determine material abundances. While an infinite number of possibilities exist for determining spectral filters, we take advantage of using neural networks to derive spectral filters leading to precise estimations. To overcome some drawbacks that regularly influence the determination of material abundances using hyperspectral data, we incorporate the spectral variability of the raw materials into the training of the considered neural networks. As a main result, we successfully classify quantized material abundances optically. Thus, the main part of the high computational load, which belongs to the use of neural networks, is avoided. In addition, the derived material abundances become invariant against spatially varying illumination intensity as a remarkable benefit in comparison with spectral filters based on the Moore-Penrose pseudoinverse, for instance.

We introduce the opto-mechanical architecture of a high precision, full Stokes vector, dual-channel polarimeter for the European Extremely Large Telescope's High Resolution spectrograph (E-ELT HIRES). It is foreseen to feed two spectrograph modules simultaneously through the standard Front End subunit located on the Nasmyth platform via two fiber bundles; one optimized for the optical (BVRI), the other optimized for the infrared (zYJH) bands. The polarimeter is located below M4 in the f/4.4 intermediate focus, representing the only rotationally symmetric focus available, and is retractable. We illustrate the strategy of repositioning and aligning the instrument, provided that it has to withstand wind and earthquake loads and that the PSF is varying in width and position due to the active compensation by the co-phasing corrections. Preliminary results of its expected polarimetric sensitivity and accuracy are also analyzed for several configurations of M1 segments and suggest a stunning performance in the intermediate focus with cross talks of the order of 10(-7) but 10(-2) if it were located in the Nasmyth focus.

Lidar ratio (LR) is an important parameter to invert the lidar equation to subsequently get information from the lidar signals. Therefore, it is the objective of this study to assess the LR values for each day to implement into the inversion method. An algorithm has been generated to estimate the lidar ratios in Penang for the Raymetrics ground-based lidar. Daily average humidity and visibility parameters was obtained and the lidar ratios for each day in year 2014 and year 2015 were assessed. It is found that the LR values in the year 2014 and 2015 generally lie in the range from 55 sr to 85 sr. Maximum LR values in the year 2014 and 2015 is 141 sr and 177 sr respectively. Both years has the same minimum LR value of 46 sr. Extreme values are found in both years during the haze events that occurred in Penang. The LR values estimated are valuable as they represent the atmospheric conditions in Penang and plays an utmost important role in the lidar inversion method.

Within the framework of the project, substances are indicators. It is these substances that are the main constituents of a watery suspension found on the surface of Mars. According to the conducted researches, the spectral region for the study of indicator substances was chosen. The method of remote sensing of the surface and the lidar construction scheme are chosen. The results of the preliminary calculation of the system are presented.

The airport is one of the most crucial traffic facilities in military and civil fields. Automatic airport extraction in high spatial resolution remote sensing images has many applications such as regional planning and military reconnaissance. Traditional airport extraction strategies usually base on prior knowledge and locate the airport target by template matching and classification, which will cause high computation complexity and large costs of computing resources for high spatial resolution remote sensing images. In this paper, we propose a novel automatic airport extraction model based on saliency region detection, airport runway extraction and adaptive threshold segmentation. In saliency region detection, we choose frequency-tuned (FT) model for computing airport saliency using low level features of color and luminance that is easy and fast to implement and can provide full-resolution saliency maps. In airport runway extraction, Hough transform is adopted to count the number of parallel line segments. In adaptive threshold segmentation, the Otsu threshold segmentation algorithm is proposed to obtain more accurate airport regions. The experimental results demonstrate that the proposed model outperforms existing saliency analysis models and shows good performance in the extraction of the airport.

In recent years, Unmanned Aerial Vehicles (UAVs) have increasingly been used in many civil applications. However, they also pose a significant threat in restricted zones. Radar can be used to detect and discriminate UAVs. Due to the low flying altitude of the UAVs, it is found that the radar signals also include some unwanted echoes, reflected by building, ground, trees and grasses etc. Consequently, it has not been possible to get the clean UAVs characteristics for further classification. In this paper, the MTI filter is applied to cancel the ground clutter and based this, an improved MTI filter is further proposed. Compared with the traditional MTI filter, the improved one significantly enhances ground clutter rejection capability while maintaining most of the target power. As the result, the cleaner UAVs classification characteristics can be obtained. The effectiveness of the proposed method has been verified by an experimental CW radar dataset, collected from a helicopter UAV. Accepted version

The ripening of fruits is associated with changes, in some cases subtle, in the color of the fruit. Traditionally spectroscopy used to measure these subtle changes and infer the ripeness of fruits. Spectrometers provides high-resolution but only measure a small area of the fruit. That might not be a good indicator of the overall ripeness. In this paper, we propose a compact tunable LED based hyper spectral imaging system that scans through a set of wavelengths and images, the reflectance from the whole fruit. Based on the type of fruit, only specific wavelengths need to be scanned. Following a validation using a Rubik’s cube, an example banana going through its ripening cycles is used to demonstrate the system.

In the framework of the project, substances are indicators, which are the main constituents of a watery suspension found on the surface of Mars. According to the conducted researches, the spectral region for the study of indicator substances was chosen. The method of remote sensing of the surface and the lidar construction scheme are chosen. The results of the preliminary calculation of the system are presented.

ARKA and KORTES are two upcoming solar space missions in extreme ultraviolet and X-ray wavebands. KORTES is a sun-oriented mission designed for the Russian segment of International Space Station. KORTES consists of several imaging and spectroscopic instruments that will observe the solar corona in a number of wavebands, covering EUV and X-ray ranges. The surveillance strategy of KORTES is to cover a wide range of observations including simultaneous imaging, spectroscopic and polarization measurements. ARKA is a small satellite solar mission intended to take highresolution images of the Sun at the extreme ultraviolet wavelengths. ARKA will be equipped with two high-resolution EUV telescopes designed to collect images of the Sun with approximately 150 km spatial resolution in the field of view of about 10'×10'. The scientific results of the mission may have a significant impact on the theory of coronal heating and may help to clarify the physics of small-scale solar structures and phenomena including oscillations of fine coronal structures and the physics of micro- and nanoflares.

Nonlinear radar has proven to be a viable means of detecting devices that contain electrical nonlinearities. Electrical nonlinearities are present in dissimilar metals, metal to oxide junctions, semiconductors and more. This paper presents a linear and nonlinear synthetic aperture radar (SAR) system capable of imaging linear and nonlinear targets. The system creates images using data collected from a fixed 16 channel receiver with a single transmitter. A custom 16:1 switching network was developed to collect the SAR data from a 16 antenna receive array. SAR images presented show a nonlinear target placed directly on the ground and imaged in multiple range and cross-range locations. Data is also presented showing the clutter rejection properties of nonlinear radar. Images show that the harmonic radar is able to ignore the strong linear response from a corner reflector, while retaining the nonlinear response from a target.

This study presents a remote sensing application of using time series Landsat satellite images for monitoring the solid waste disposal site (WDS). We propose a method of detecting high-rise buildings landfills, such as municipal dumps and solid waste, according to a radar image (the height of the ground level). For disposal site detection a variety steps of image processing used (calculation image average level of the earth's surface; filtering thresholds spectral brightness coefficients, the size of the connected components, the nature of reducing the level of height with the distance of the maximum level). The spatial geometric features of waste disposal facilities are analytically expressed by linear and radial characteristics from other objects of the earth surface. As a result, the proposed method demonstrates good accuracy in detection the solid waste disposal site on real satellite images.

Degraded visual environments are a serious concern for modern sensing and surveillance systems. Fog is of interest due to the frequency of its formation along our coastlines disrupting border security and surveillance. Fog presents hurdles in intelligence and reconnaissance by preventing data collection with optical systems for extended periods. We will present recent results from our work in operating optical systems in our controlled fog experimental chamber. This facility is a 180-foot-long, 10-foot-wide, and 10-foot-tall structure that has over 60 spray nozzles to achieve uniform aerosol coverage with various particle size, distributions, and densities. We will discuss the physical formation of fog in nature and how our generated fog compares. In addition, we will discuss fog distributions and characterization techniques. We will investigate the biases of different methods and discuss the different techniques that are appropriate for realistic environments. Finally, we will compare the data obtained from our characterization studies against accepted models (e.g., MODTRAN) and validate the usage of this unique capability as a controlled experimental realization of natural fog formations. By proving the capability, we will enable the testing and validation of future fog penetrating optical systems and providing a platform for performing optical propagation experimentation in a known, stable, and controlled environment.

In this research, we focus on vessel detection using the satellite imagery of day/night band (DNB) on Suomi NPP in order to monitor the change of vessel activity on the region of South China Sea. In this paper, we consider the relation between the temporal change of vessel activities and the events on maritime environment based on the vessel traffic density estimation using DNB. DNB is a moderate resolution (350-700m) satellite imagery but can detect the fishing light of fishery boats in night time for every day. The advantage of DNB is the continuous monitoring on wide area compared to another vessel detection and locating system. However, DNB gave strong influence of cloud and lunar refection. Therefore, we additionally used Brightness Temperature at 3.7μm(BT3.7) for cloud information. In our previous research, we construct an empirical vessel detection model that based on the DNB contrast and the estimation of cloud condition using BT3.7. Moreover, we proposed a vessel traffic density estimation method based on empirical model. In this paper, we construct the time temporal density estimation map on South China Sea and East China Sea in order to extract the knowledge from vessel activities change.

Adaptive filtering and channel estimation techniques are applied to laser based ranging systems that utilize wide-band intensity modulation to measure the range and reflectivity of underwater objects. The proposed method aims to iteratively learn the frequency dependent characteristics of the underwater environment using a frequency domain adaptive filter, which results in an estimate for the channels optical impulse response. This work presents the application of the frequency domain adaptive filter to simulated and experimental data, and shows it is possible to iteratively learn the underwater optical channel impulse response while using Hybrid Lidar/Radar techniques.

This work presents a new statistical signal processing approach to reduce the effects of forward scatter on range accuracy for an underwater modulated pulse lidar. Lidar sensors offer the potential for high-resolution, high-accuracy ranging in the underwater environment. For the modulated pulse lidar rangefinder, performance is limited in turbid waters primarily due to forward scatter, which causes decreased range resolution and accuracy. This work presents simulated and experimental results demonstrating the ability of statistical signal processing to reduce range error for systems operating in these turbid conditions. Experimental results demonstrated 60% reduction in range error compared to a baseline approach.

Compressive Line Sensing (CLS) imaging system is a compressive sensing (CS) based imaging system with the goal of developing a compact and resource efficient imaging system for the degraded visual environment. In the CLS system, each line segment is sensed independently; however, the correlation among the adjacent lines (sources) is exploited via the joint sparsity in the distributed compressing sensing model during signal reconstruction. Several different CLS prototypes have been developed. This paper discusses the development of a pulsed CLS system. Initial experimental results using this system in a turbid water environment are presented.

Hyperspectral imaging (HSI) has been used for over two decades in laboratory research, academic, environmental and defense applications. In more recent time, HSI has started to be adopted for commercial applications in machine vision, conservation, resource exploration, and precision agriculture, to name just a few of the economically viable uses for the technology. Corning Incorporated (Corning) has been developing and manufacturing HSI sensors, sensor systems, and sensor optical engines, as well as HSI sensor components such as gratings and slits for over a decade and a half. This depth of experience and technological breadth has allowed Corning to design and develop unique HSI spectrometers with an unprecedented combination of high performance, low cost and low Size, Weight, and Power (SWaP). These sensors and sensor systems are offered with wavelength coverage ranges from the visible to the Long Wave Infrared (LWIR). The extremely low SWaP of Corning’s HSI sensors and sensor systems enables their deployment using limited payload platforms such as small unmanned aerial vehicles (UAVs). This paper discusses use of the Corning patented monolithic design Offner spectrometer, the microHSI™, to build a highly compact 400-1000 nm HSI sensor in combination with a small Inertial Navigation System (INS) and micro-computer to make a complete turn-key airborne remote sensing payload. This Selectable Hyperspectral Airborne Remote sensing Kit (SHARK) has industry leading SWaP (1.5 lbs) at a disruptively low price due, in large part, to Corning’s ability to manufacture the monolithic spectrometer out of polymers (i.e. plastic) and therefore reduce manufacturing costs considerably. The other factor in lowering costs is Corning’s well established in house manufacturing capability in optical components and sensors that further enable cost-effective fabrication. The competitive SWaP and low cost of the microHSI™ sensor is approaching, and in some cases less than the price point of Multi Spectral Imaging (MSI) sensors. Specific designs of the Corning microHSI™ SHARK visNIR turn-key system are presented along with salient performance characteristics. Initial focus market areas include precision agriculture and historic and recent microHSI™ SHARK prototype test results are presented.

Preliminary results of previous work had shown a Neural Network (NN) technique developed by us as effective in detecting Karenia brevis Harmful Algal Blooms (KB HABs) plaguing West Florida Shelf (WFS) from VIIRS satellite observations. We extend comparisons of NN retrievals against a data set of near simultaneous in-situ measurements in the WFS spanning the 2012-2016 period for which there was available VIIRS data. Specifically we looked for match ups where the overlap time windows between satellite observations and in-situ measurements were 15 minutes and 100 minutes. We then compare the accuracy of the NN retrievals against the in-situ measurements, with the accuracies achieved with similar of retrievals using OC3, GIOP, QAA and RGCI algorithms. The NN technique exhibited the best retrieval accuracy statistics. The retrievals for all the algorithms very clearly showed the impact of temporal variations of the KB HABS on retrieval accuracies. Thus, retrievals using a 15 minutes overlap window between satellite observations and in-situ measurements yielded much higher accuracies than those with the 100 minutes overlap window. Temporal variabilities were also studied, using consecutive overlapping VIIRS images. These variabilities, as well as the patchiness of KB blooms were also confirmed by a set of in-situ measurements near Sarasota, FL.

This effort develops and tests algorithms and a user-portable optical system designed to autonomously optimize the laser communication wavelength in open and coastal oceans. In situ optical meteorology and oceanography (METOC) data gathered and analyzed as part of the auto-selection process can be stored and forwarded. The system performs closedloop optimization of three visible-band lasers within one minute by probing the water column via passive retroreflector and polarization optics, selecting the ideal wavelength, and enabling high-speed communication. Backscattered and stray light is selectively blocked by employing polarizers and wave plates, thus increasing the signal-to-noise ratio. As an advancement in instrumentation, we present autonomy software and portable hardware, and demonstrate this new system in two environments: ocean bay seawater and outdoor test pool freshwater. The next generation design is also presented. Once fully miniaturized, the optical payload and software will be ready for deployment on manned and unmanned platforms such as buoys and vehicles. Gathering timely and accurate ocean sensing data in situ will dramatically increase the knowledge base and capabilities for environmental sensing, defense, and industrial applications. Furthermore, communicating on the optimal channel increases transfer rates, propagation range, and mission length, all while reducing power consumption in undersea platforms.

Our group has developed a custom, multi-modal sensor suite and data analysis pipeline to phenotype crops in the field using unpiloted aircraft systems (UAS). This approach to high-throughput field phenotyping is part of a research initiative intending to markedly accelerate the breeding process for refined energy sorghum varieties. To date, single rotor and multirotor helicopters, roughly 14 kg in total weight, are being employed to provide sensor coverage over multiple hectaresized fields in tens of minutes. The quick, autonomous operations allow for complete field coverage at consistent plant and lighting conditions, with low operating costs. The sensor suite collects data simultaneously from six sensors and registers it for fusion and analysis. High resolution color imagery targets color and geometric phenotypes, along with lidar measurements. Long-wave infrared imagery targets temperature phenomena and plant stress. Hyperspectral visible and near-infrared imagery targets phenotypes such as biomass and chlorophyll content, as well as novel, predictive spectral signatures. Onboard spectrometers and careful laboratory and in-field calibration techniques aim to increase the physical validity of the sensor data throughout and across growing seasons. Off-line processing of data creates basic products such as image maps and digital elevation models. Derived data products include phenotype charts, statistics, and trends. The outcome of this work is a set of commercially available phenotyping technologies, including sensor suites, a fully integrated phenotyping UAS, and data analysis software. Effort is also underway to transition these technologies to farm management users by way of streamlined, lower cost sensor packages and intuitive software interfaces.

Though sharing with many commonalities, one of the major differences between conventional high-altitude airborne remote sensing and low-altitude unmanned aerial system (UAS) based remote sensing is that the latter one has much smaller ground footprint for each image shot. To cover the same area on the ground, it requires the low-altitude UASbased platform to take many highly-overlapped images to produce a good mosaic, instead of just one or a few image shots by the high-altitude aerial platform. Such an UAS flight usually takes 10 to 30 minutes or even longer to complete; environmental lighting change during this time span cannot be ignored especially when spectral variations of various parts of a field are of interests. In this case study, we compared the visible reflectance of two aerial imagery – one generated from mosaicked UAS images, the other generated from a single image taken by a manned aircraft – over the same agricultural field to quantitatively evaluate their spectral variations caused by the different data acquisition strategies. Specifically, we (1) developed our customized ground calibration points (GCPs) and an associated radiometric calibration method for UAS data processing based on camera’s sensitivity characteristics; (2) developed a basic comparison method for radiometrically calibrated data from the two aerial platforms based on regions of interests. We see this study as a starting point for a series of following studies to understand the environmental influence on UAS data and investigate the solutions to minimize such influence to ensure data quality.

This paper examines the measurement of MTF of slant edge targets from airborne imagery. The MTF is calculated by extracting the edge spread function from the slant edge, deriving the line spread function, the performing an FFT to get the MTF. Because characteristics of airborne imagery are not controlled, using edge targets to get the system level MTF present challenges. A method to calculate the MTF from edge targets in airborne imagery is proposed by normalizing the scan lines in the edge spread function and low pass filtering it. An example using air borne imagery is shown and compared with analytical results and laboratory measurements. The paper also examines extracting the effects on the MTF due to image blur from jitter common with air borne imagery.

AGERpoint is defining a new technology space for the growers’ industry by introducing novel applications for sensor technology and data analysis to growers of permanent crops. Serving data to a state-of-the-art analytics engine from a cutting edge sensor platform, a new paradigm in precision agriculture is being developed that allows growers to understand the unique needs of each tree, bush or vine in their operation. Autonomous aerial and terrestrial vehicles equipped with multiple varieties of remote sensing technologies give AGERpoint the ability to measure key morphological and spectral features of permanent crops. This work demonstrates how such phenotypic measurements combined with machine learning algorithms can be used to determine the variety of crops (e.g., almond and pecan trees). This phenotypic and varietal information represents the first step in enabling growers with the ability to tailor their management practices to individual plants and maximize their economic productivity.

Thanks to the development of camera technologies, small unmanned aerial systems (sUAS), it is possible to collect aerial images of field with more flexible visit, higher resolution and much lower cost. Furthermore, the performance of objection detection based on deeply trained convolutional neural networks (CNNs) has been improved significantly. In this study, we applied these technologies in the melon production, where high-resolution aerial images were used to count melons in the field and predict the yield. CNN-based object detection framework-Faster R-CNN is applied in the melon classification. Our results showed that sUAS plus CNNs were able to detect melons accurately in the late harvest season.

Variety selection and seeding rate are two important choice that a peanut grower must make. High yielding varieties can increase profit with no additional input costs, while seeding rate often determines input cost a grower will incur from seed costs. The overall purpose of this study was to examine the effect that seeding rate has on different peanut varieties. With the advent of new UAV technology, we now have the possibility to use indices collected with the UAV to measure emergence, seeding rate, growth rate, and perhaps make yield predictions. This information could enable growers to make management decisions early in the season based on low plant populations due to poor emergence, and could be a useful tool for growers to use to estimate plant population and growth rate in order to help achieve desired crop stands. Red-Green-Blue (RGB) and near-infrared (NIR) images were collected from a UAV platform starting two weeks after planting and continued weekly for the next six weeks. Ground NDVI was also collected each time aerial images were collected. Vegetation indices were derived from both the RGB and NIR images. Greener area (GGA- the proportion of green pixels with a hue angle from 80° to 120°) and a* (the average red/green color of the image) were derived from the RGB images while Normalized Differential Vegetative Index (NDVI) was derived from NIR images. Aerial indices were successful in distinguishing seeding rates and determining emergence during the first few weeks after planting, but not later in the season. Meanwhile, these aerial indices are not an adequate predictor of yield in peanut at this point.

Because unmanned air vehicles (UAVs) are emerging as an indispensable image acquisition platform in precision agriculture, it is vitally important that researchers understand how to optimize UAV camera payloads for analysis of surveyed areas. In this study, imagery captured by a Nikon RGB camera attached to a Precision Hawk Lancaster was used to survey an agricultural field from six different altitudes ranging from 45.72 m (150 ft.) to 121.92 m (400 ft.). After collecting imagery, two different software packages (MeshLab and AgiSoft) were used to measure predetermined reference objects within six three-dimensional (3-D) point clouds (one per altitude scenario). In-silico measurements were then compared to actual reference object measurements, as recorded with a tape measure. Deviations of in-silico measurements from actual measurements were recorded as Δx, Δy, and Δz. The average measurement deviation in each coordinate direction was then calculated for each of the six flight scenarios. Results from MeshLab vs. AgiSoft offered insight into the effectiveness of GPS-defined point cloud scaling in comparison to user-defined point cloud scaling. In three of the six flight scenarios flown, MeshLab's 3D imaging software (user-defined scale) was able to measure object dimensions from 50.8 to 76.2 cm (20-30 inches) with greater than 93% accuracy. The largest average deviation in any flight scenario from actual measurements was 14.77 cm (5.82 in.). Analysis of the point clouds in AgiSoft (GPS-defined scale) yielded even smaller Δx, Δy, and Δz than the MeshLab measurements in over 75% of the flight scenarios. The precisions of these results are satisfactory in a wide variety of precision agriculture applications focused on differentiating and identifying objects using remote imagery.

Farmers can benefit from growing drought tolerant peanut (Arachis hypogaea L.) cultivars with improved yield when rainfall is sporadic. In the Virginia-Carolina (VC) region, drought is magnified by hot summers and usually occurs in July and Aug when pod and seed growth are intense. At these growth stages, weekly supply of 50 to 75 mm of water is needed to ensure profitability. Irrigation can supplement crop water needs, but only 10% of the peanut farms are irrigated. In this frame, drought tolerant varieties can be profitable, but breeding for cultivars with improved drought tolerance requires fast yet accurate phenotyping. Our objective was to evaluate the potential of UAV remote sensing technologies for drought tolerance selection in peanut. In this study, we examined the effect of drought on leaf wilting, pod yield, grading characteristics, and crop value of 23 peanut cultivars (Virginia, Runner, and Valencia type). These varieties were arranged in a factorial design, with four replications drought stressed and two replications well-watered. Drought was imposed by covering the drought stressed plots with rainout shelters on July 19; they remained covered until August 29 and only received 38 mm irrigation in mid Aug. The well-watered plots continued to receive rain and supplemental irrigation as needed. During this time, Canopy Temperature Depression (CT) and Normalized Differential Vegetative Index (NDVI) were collected from the ground on all plots at weekly intervals. After the shelters were removed, these measurements were collected daily for approximately 2 weeks. At the same time, Red-Green-Blue (RGB), near-infrared (NIR), and infrared (IR) images taken from an UAV platform were also collected. Vegetation indices derived from the ground and aerial data were compared with leaf wilting, pod yield and crop value. Wilting, which is a common water stress symptom, was best estimated by NDVI and RGB, and least by CT; but CT was best in estimating yield, SMK and crop value in particular when taken on the ground at 15 days water stress imposition. Interestingly, CT predicted well plant wilting even before it occurred, i.e., correlation coefficients were negative and over 0.750 when CT was measured on July 19 and 20 even though wilting was visible only after two weeks. The data, yet preliminary, show promising potential for remote sensing technologies, at the ground and aerial, for peanut variety selection for improved drought tolerance.

Texas A and M University has been operating a large-scale, UAV-based, agricultural remote-sensing research project since 2015. To use UAV-based images in agricultural production, many high-resolution images must be mosaicked together to create an image of an agricultural field. Two key difficulties to science-based utilization of such mosaics are geographic registration and radiometric calibration. In our current research project, image files are taken to the computer laboratory after the flight, and semi-manual pre-processing is implemented on the raw image data, including ortho-mosaicking and radiometric calibration. Ground control points (GCPs) are critical for high-quality geographic registration of images during mosaicking. Applications requiring accurate reflectance data also require radiometric-calibration references so that reflectance values of image objects can be calculated. We have developed a method for automated geographic registration and radiometric correction with targets that are installed semi-permanently at distributed locations around fields. The targets are a combination of black (≈5% reflectance), dark gray (≈20% reflectance), and light gray (≈40% reflectance) sections that provide for a transformation of pixel-value to reflectance in the dynamic range of crop fields. The exact spectral reflectance of each target is known, having been measured with a spectrophotometer. At the time of installation, each target is measured for position with a real-time kinematic GPS receiver to give its precise latitude and longitude. Automated location of the reference targets in the images is required for precise, automated, geographic registration; and automated calculation of the digital-number to reflectance transformation is required for automated radiometric calibration. To validate the system for radiometric calibration, a calibrated UAV-based image mosaic of a field was compared to a calibrated single image from a manned aircraft. Reflectance values in selected zones of each image were strongly linearly related, and the average error of UAV-mosaic reflectances was 3.4% in the red band, 1.9% in the green band, and 1.5% in the blue band. Based on these results, the proposed physical system and automated software for calibrating UAV mosaics show excellent promise.

The compact single photon lidar demonstrator dedicated for asteroid rendezvous missions has been designed and realized in our laboratory two years ago. The instrument provides crucial data on altitude and terrain profile for altitudes exceeding 5 km with a precision of less than 10 cm fulfilling the Rayleigh criterion. One of the calibration procedure of demonstrator is the positioning of receiver and transmitter optics related to detector and laser and the aligning of transmitter and receiver optical common paths. To improve this particular indoor calibration procedure the new simulator of single photon energy budget during short range operation has been created. The comparison of simulated and experimental data will be presented and discussed.

The U.S. Army Research Laboratory has developed the Spectrally Agile Frequency-Incrementing Reconfigurable (SAFIRE) radar, which is capable of imaging concealed/buried targets using forward- and side-looking configurations. The SAFIRE radar is vehicle-mounted and operates from 300 MHz–2 GHz; the step size can be adjusted in multiples of 1 MHz. It is also spectrally agile and capable of excising frequency bands, which makes it ideal for operation in congested and/or contested radio frequency (RF) environments. Furthermore, the SAFIRE radar receiver has a super-heterodyne architecture, which was designed so that intermodulation products caused by interfering signals could be easily filtered from the desired received signal. The SAFIRE system also includes electro-optical (EO) and infrared (IR) cameras, which can be fused with radar data and displayed in a stereoscopic augmented reality user interface. In this paper, recent upgrades to the SAFIRE system are discussed and results from the SAFIRE’s initial field tests are presented.

This paper presents the work being conducted at Cal Poly Pomona on the collaboration between unmanned aerial and ground vehicles for precision agriculture. The unmanned aerial vehicles (UAVs), equipped with multispectral/hyperspectral cameras and RGB cameras, take images of the crops while flying autonomously. The images are post processed or can be processed onboard. The processed images are used in the detection of unhealthy plants. Aerial data can be used by the UAVs and unmanned ground vehicles (UGVs) for various purposes including care of crops, harvest estimation, etc. The images can also be useful for optimized harvesting by isolating low yielding plants. These vehicles can be operated autonomously with limited or no human intervention, thereby reducing cost and limiting human exposure to agricultural chemicals. The paper discuss the autonomous UAV and UGV platforms used for the research, sensor integration, and experimental testing. Methods for ground truthing the results obtained from the UAVs will be used. The paper will also discuss equipping the UGV with a robotic arm for removing the unhealthy plants and/or weeds.

The application of unmanned autonomous vehicles (UAVs), or "drones", to generate data to support better decisions for agricultural management and farm operations is a relatively new technology that is now beginning to enter the market. This potentially disruptive technology is still in its infancy and must mature in ways that the current market cannot clearly foresee and probably does not fully understand. Major technical and regulatory hurdles must be overcome before the full potential of this remote sensing technology can be realized in agricultural applications. Further, and most importantly, buyers and sellers in today’s market must both gain a deeper understanding of the potential that this technology might achieve and the technical challenges that must be met before advances that will bring significant market value will be possible. A lack of understanding of some of the basic concepts of remote sensing can translate into poor decisions regarding the acquisition and deployment of UAVs in agriculture. This paper focuses on some of the details of remote sensing that few growers, and, indeed, few university researchers fully understand.

This paper reports a novel quantitative gas plume imaging tool, based on active near-infrared Backscatter Tunable Diode Laser Absorption Spectroscopy (b-TDLAS) technology, designed for upstream natural gas leak applications. The new tool integrates low-cost laser sensors with video cameras to create a highly sensitive gas plume imager that also quantifies emission rate, all in a lightweight handheld ergonomic package. It is intended to serve as a lower-cost, higherperformance, enhanced functionality replacement for traditional passive non-quantitative mid-infrared Optical Gas Imagers (OGI) which are utilized by industry to comply with natural gas infrastructure Leak Detection and Repair (LDAR) requirements. It addresses the need for reliable, robust, low-cost sensors to detect and image methane leaks, and to quantify leak emission rates, focusing on inspections of upstream oil and gas operations, such as well pads, compressors, and gas plants. It provides: 1) Colorized quantified images of path-integrated methane concentration. The images depict methane plumes (otherwise invisible to the eye) actively interrogated by the laser beam overlaid on a visible camera image of the background. The detection sensitivity exceeds passive OGI, thus simplifying the manual task of leak detection and location; and 2) Data and algorithms for using the quantitative information gathered by the active detection technique to deduce plume flux (i.e. methane emission rate). This key capability will enable operators to prioritize leak repairs and thereby minimize the value of lost product, as well as to quantify and minimize greenhouse gas emissions, using a tool that meets EPA LDAR imaging equipment requirements.

One of the main problems faced by the Space Situational Awareness community is the detection and characterization of faint objects around geosats. Independent of the origin of these objects, whether they are debris or controlled spacecraft, they can potentially harm these assets and contaminate the geobelt environment. The challenge of detecting these companion objects comes from their proximity and brightness ratio relative to geosats. Here we present a novel interferometric fringe nulling technique, aimed at solving these issues. This technique takes advantage of the fact that the presence of companions introduces large phase fluctuations in the fringe phase observed by an interferometer, when the interferometer is observing a target at spatial frequencies where the fringe amplitude is near zero. We describe the ongoing development of this technique at the Navy Precision Optical Interferometer, and the results of simulations of interferometric observations of satellites with companions. We also present the current state of the NPOI and related SSA work being done with this interferometer, as well as undergoing upgrades to the system.

This paper considers the mechanical design of a low-mass, low-cost spacecraft for use in a multi-satellite sensing constellation. For a multi-spacecraft mission, aggregated small mass and cost reductions can have significant impact. One approach to mass reduction is to make cuts into the structure, removing material. Stress analysis is used to determine the level of material reduction possible. Focus areas for this paper include determining areas to make cuts to ensure that a strong shape remains, while considering the comparative cost and skill level of each type of cut. Real-world results for a CubeSat and universally applicable analysis are presented.

The paper discusses recent results of flight tests performed with the Hensoldt (former Airbus DS Electronics and Border Security) LiDAR system in brownout. The SferiSense® LiDAR system was mounted on a Mi-2 test platform as part of the complete DVE system SFERION® to undergo the tests. To optimize brownout capabilities minor modifications were performed on the sensor firmware over the SferiSense® serial LiDAR system which is in operational use on the NH90 transport helicopter. Also dust echoes were filtered out by a real-time filtering algorithm. Numerous approaches into own ship generated dust (light to heavy) as well as fan generated dust clouds (chalk 2 scenarios) were performed. The paper discusses the results and shows under which conditions the LiDAR can still look through the dust cloud. Also the contribution of high resolution real-time 3D LiDAR data to the DVE system usage is discussed.

With the rapid development of space exploration and utilization, orbital debris increases dramatically, leading to great threat to human space activities and spacecraft security. In this paper, a new type of multi-functional space debris satellite system (MSDS) was put forward, which shared main optical system, and possessed functions of multidimensional information detection, polarized remote sensing and high rate transmission. The MSDS system can meet the requirements of detection and identification for the small orbital debris which is 1000km faraway, as well as the requirements of the data transmission by 50 Mbps to 2.5 Gbps@200-1000 km. At the same time, by the method of satellite orbital maneuver and attitude adjusting, the orbital debris information that is real-time, complex and refined, allweather can be acquired and transmitted by the new system. Such new type of multifunctional satellite system can provide important and effective technology for international orbital debris detection.