The main objective of identifying the influence of system parameters on material discriminability in polarimetric images is accomplished by quantifying the material discriminability and then analyzing the measured discriminability at various imaging configurations. In general, statistical classification techniques that exploit dissimilarity in the polarimetric response of the materials can be used to quantify the material discriminability. However, the quantification results will also depend on the statistical framework of the technique. In order to have a ‘generalized’ quantification of material discriminability, the Euclidean distance metric given in (4.3) was used to measure the polarimetric dissimilarity for each target-background pair within the scene. This distance metric is designed to measure the distinctness of a target from its background, assuming that higher distance values indicate easily detectable targets. Analyzing this direct indicator of discriminability at varying imaging configurations, the optimal phenomenology related parameters to achieve maximum material discriminability can be identified.
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Current events clearly demonstrate that chemical and biological threats against the public are very real. Automated detection of chemical threats is a necessary component of a system that provides early warning of an attack. Plant biologists are currently developing genetically engineered plants that de-green in the presence of explosives (i.e. TNT) in their environment. The objectives of this thesis are to study the spectral reflectance phenomenology of the plant sensors and to propose requirements for an operational monitoring system using spectral imaging technol- ogy. Hyperspectral data were collected under laboratory conditions to determine the key spectral regions in the reflectance spectra associated with the de-greening phenomenon. The collected reflectance spectra were then entered into simulated imagery created using the Rochester Institute of Technology’s Digital Imaging and Remote Sensing Image Generation (DIRSIG) model. System performance was stud- ied as a function of pixel size, radiometric noise, spectral waveband dependence and spectral resolution. It was found that a framing array sensor with 40nm wide bands centered at 645 nm, 690 nm, 875 nm, a ground sample distance of 11cm or smaller, and an signal to noise ratio of 250 or better would be sufficient for monitoring bio-sensors deployed under conditions similar to those simulated for this work.
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In-scene glint greatly affect the usability of maritime imagery and several glint re- moval algorithms have been developed that work well in some situations. However, glint removal algorithms produces several unique artifacts when applied to very high resolution systems, particularly those with temporally offset bands. The optimal so- lution to avoid these artifacts is to avoid imaging in areas of high glint. The glint avoidance tool (GAT) was developed to avoid glint conditions and provide a mea- sure of parameter detectability. This work recreates the glint avoidance tool using Hydrolight, as a validation of a fast GAT using an in-water radiative transfer model which neglects in-water scattering. Because avoiding glint is not always possible, this research concentrates on the impact of glint and residual artifacts using RIT’s Digital Imaging and Remote Sensing Image Generation (DIRSIG) dynamic wave model and Hydrolight back-end to create accurate case 1 synthetic imagery. The synthetic imagery was used to analyze the impact of glint on automated anomaly detection, glint removal, and development of a new glint compensation technique for sensors with temporally offset bands.
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one is the detachable array CCD digital back installed in large and medium format camera to replace the rear film digital imaging; the other is the CCD imaging module directly integrated with the body, it is our common 135 small format digital camera. The former usually has high resolution for professional photography, while the latter emphasizes convenience for home and general photography. Unmanned aerial vehicle remote sensing platform requires high resolution and small size of camera, so it adopts the scheme of large area CCD digital back with 120 medium format camera. We require higher image quality, so we must reduce the impact of the image on the image, requiring less than 0.5 pixels. If we choose the large surface array CCD back the pixel size of 9 m * 9 m, the UAV speed 33m/s, height 500m, the camera focal length is about 50mm, can calculate the camera exposure time is 1/733s, so we use the highest shutter camera should be more than 1/1000s. The main parameters of the camera lens are the focal length f, the focal length f and the FOV angle theta, and the imaging surface width L are closely related. The relation between the focal length f and the field of view theta and the size of the imaging surface L is as follows: TG (theta /2) = (L/2) /f, the correlation calculation of the focal length can be carried out according to this relation. The master computer completes the control of the camera, the transmission and storage of the image. PC/104+ embedded computer better meets this requirement, and has the advantages of short development cycle. PC/104+ embedded computer can complete the functions of exposure control, image acquisition, transmission, storage, GPS decoding calculation, remote instruction execution and status reporting in two instruments.
A need for SDR is thus justified. There is also tremendous technological growth in embedded system realization. Multiprocessor technology utilizing DSP, and FPGA helps a lot in this regards. The sub system in the form of SDR is commercially available. The programmability of the SDR is also simplified utilizing very high level languages like MATLAB and SIMULINK. With additional daughter board the SDR sub system can be best and efficiently utilized for the development of the system. In essence, the development of the digital radar is almost ready for its deployment at the vehicle for collision avoidance and remote sensing in ITS system .
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The digital images generated by remote sensors often contain noises that are inherent in the process of imaging and transmission. The application of digital processing techniques greatly enhances the ability to extract information on sur- face targets from remote sensing data. When digital images are used with high spatial resolution, one of the problems emerging the high variability of targets presents in such images. From the computational point of view, the use of partial differential equations is favored by the large number of numerical methods showed in the literature. Many of the models are considered non-complex both from the mathematical and computational standpoints, due to the characteristics of explicit equations. This work uses techniques of the partial differential equations (PDE) and mathematical morphology to extract cartographic features in digital images of the remote sensing. The selected study area corresponds to an image containing part of the Mário Covas Ring Road, located in the metropolitan region of São Paulo (SP), Brazil. The results are promising and show the high potential of using mathematical morphology in the field of cartography.
Image compression plays a key role in much application such as image data base, image communication, digital movie, televideo conferencing, remote sensing ,document and medical imaging, fax and in camera. It speed the processing due to reduce the size of image. Web page image and high resolution digital camera photos also are compressed routinely to save storage space and reduce transmission time. The residential internet connections deliver data at speed ranging from 56 Kbps via conventional phone line and more than 12 Mbps for broadband. The time required to transmit a small 128X128X8 bit full color image over this range of speed is from 7.0 to 0.03 seconds. Compression can reduce the transmitting time by the factor of 2 to 10 or more. Number of work has been done on this area.  has described the compression using DCT. has A new Lossless method of image compression and decompression using Huffman coding technique. has explained about data compression. Singh has described about Edge preserving compression technique using feed forward neural network. In this paper I described the model of compression using Huffman code technique which is based on Coding redundancy method and also designed a small algorithm for compressing and decompression of an image.
Although PCA has been widely used for feature extraction and data reduction, it suffers from three main drawbacks: high computational cost, large memory requirement and low efficacy in processing large datasets such as HSI. This column analysed two variations of PCA, namely SPCA and Seg-PCA. Seg-PCA can further improve classification accuracy whilst significantly reducing the computational cost and memory requirement, without requiring prior knowledge. There is potential to apply similar feature extraction and data reduction techniques in application areas beyond HSI when analysis of large dimensional datasets is required such as magnetic resonance imaging (MRI) and digital video processing.
8. Precision farming: In conventional cropping practice a similar dose of fertiliser, water and other inputs are applied to the whole farm. However the input requirement may not be the same in the total area. The goal of precision farming is to gather and analyse the information about the spatial variability of soil and crop condition in order to optimise the efficiency of crop input based on site specific needs within a farm. Thus precision farming can be considered as site specific farming, which has a potential to reduce cost through more efficient and effective application of agronomic inputs. This also reduce environmental impacts, since inputs are applied where they are needed and at the required quantity. To realise variable rate of input, we should first have an application map, which gives the location and spatial extend and level of input to be used. Precision farming requires an integration of various technologies such as remote sensing, GPS, GIS, and field equipments for variable rate of application. With the next generation satellites providing high resolution multi spectral data, it is now possible to give information at field/ within field level.
Availability of advanced sensor technology such as NASA‟s Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) has resulted in the collection of spectral data over terrestrial regions at high, medium and low altitudes through adjacent multi-band channels, enabling its utilization in a multitude of applications ranging from environmental planning and assessment, monitoring of oil spills, geological research, and target detection in military applications . Hyperspectral imaging can be defined as the simultaneous acquisition of an image in many narrow contiguous spectral bands . The advantage of this technique is that, considering that every element (water, tree, soil, etc) is defined by a spectrum (spectral signature). The overall objective of image classification procedures is to automatically categorize all pixels in image into land cover classes . Based on pixel information, Images can be classified as Per-pixel, Sub pixel, Per-field, Knowledge based, Contextual and multiple classifiers. Per-pixel classifiers may be parametric or non-parametric. Based on the use of training samples, images can be classified as Supervised and Unsupervised Classification. The unsupervised classification is the identification of natural groups or structures. The supervised classification is the process of using samples of known identity to classify (i.e.) to assign unclassified pixels to one of several informational classes.
A lineament is usually defined as a straight or curve lin- ear feature to be seen on the ground surface. Lineaments can be manmade structures such as roads and canals or geological structures such as faults/fractures, folds, and unconformities, differences in vegetation and soil mois- ture, or drainage networks (rivers). Lineaments can be mapped during a field survey, or by using air photos and remote sensing data either manually or by means of pat- tern recognition algorithms . On contrary lineaments can be traced from remote sensed data or digital terrain models by mathematical algorithms.
On the other hand, ITC (Individual Tree Crown) method was used for tree tops, tree-crown delineations and species classifictiion of delineated tree-crown. ITC method is a new method of digital remote sensing data analysis with the purpose of extracting individual tree crown based forestry parameters as automatically as possible, such as individual tree species and canopy area, crown and tree top extraction, stem density, crown closure and gap distribution. GeoEye-1panchromatic image was used to delineate forest area and non-forest area based on THR (thresholding Image to bitmap) method. This method creates the boundary of each segment based on the spectral values which was specified range of non-forest area. It was important to separate non-forest regions in the image, such as roads and gap of crown. On this basis, the valley- following method from the NIR band of GeoEye-1 multi-color image was used to delineate tree crown (Gougeon 1995). This method compares the spectral values of each pixel, and then it treats the spectral values of the imagery as topography with shaded and darker areas representing valleys and bright pixels of the tree crowns. It produces a bitmap of segments of valley and crown materials. A rule-based system follows the boundary of each segment of crown material to create isolations, which are taken to represent tree crowns. The ITC image was classified into species using a supervised classification process of multi bands based on comparing crown signatures (Katoh 2009). Tree top detection method was used to detect tree tops of canopy tree and count their numbers automatically. Bitmap of tree tops was created based on the highest spectral values of pixels within delineated crown. It confirmed the number of canopy trees. Canopy area can be used to calculate the DBH using the result of image analysis and crown size-DBH regression equation. Then, projection results of DBH can be used to calculate the height based on height-diameter equations. Finally, the volume and biomass can be calculated based on volume formula and expanding coefficient method respectively.
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Physical-science based and computing technologies present many opportunities for researchers in whole-organism biology, both in the field and the laboratory. The rapid advancement of these technologies, especially with regard to miniaturisation and increasing capability at reduced cost, continually extends the limits of what researchers can achieve with established methods and it also allows novel ap- proaches to be developed. The field of insect movement is one in which the use of electronics, various remote-sensing and imaging techniques and computing is well established and for which some ingenious solutions to the particular challenge of working with small organisms have been invented. A 1-day symposium on Tech- nologies for Movement and Migration Research at the 21st International Congress of Entomology (ICE), held at Iguassu Falls, Brazil, in August 2000 provided an opportunity for a dozen or so such researchers to make contact — in several cases for the first time — and to describe the techniques they have developed. Because the great majority of the research in this field is driven by the imperative of improving the management of agricultural pests, the symposium papers fall directly within the aegis of ‘Computers and Electronics in Agriculture’ and they have therefore been re-worked and brought together for this Special Issue.
Present study is an attempt to investigate land resources using Remote Sensing (RS) and Geographic Information System (GIS) for proper management and sustainability of agricultural lands selecting a development block of Gauriganj , Amethi district, Uttar Pradesh, India. The multispectral Landsat 7 ETM+ images of the year 2015 were processed in ERDAS Imagine 9.1 and Arc GIS 9.3 software for land use/ land cover investigation and degraded land mapping. The study was mainly based on standard visual image processing technique. Some digital image processing techniques such as image enhancement, classification and band ratioing etc., were also applied for generating ad-on-data for visual image processing. The ground truth and training data collected from field survey were also incorporated in the image processing. In the study area seven land use / land cover classes i.e., built-up area, fallow lands, Rabi crops, Kharif crops, double cropped area, lake/ponds, salt affected lands and waterlogged area were identified and mapped through processing of Rabi (February) and Kharif (September) season’s satellite images of the year 2015. In the study area four types of degraded lands i.e. salt affected lands (strong), salt affected lands (moderate), waterlogged area (permanent), waterlogged area (seasonal) were also mapped . The study proves effectiveness of satellite data, RS and GIS techniques in land resource inventory and mapping. This study may helpful in proper management of land resources for agricultural sustainability in the study area.
Remote sensing (RS) has been extensively used to in- vestigate and identify wetland resources. RS brings about huge technical support to relevant research owing to its time/cost saving, provision of abundant information, multi-platform operation, and multi-temporal advantage . Though RS largely facilitates wetland research, the changeable meteorological conditions make cloud inter- ference one of the major barriers against the acquisition of clear RS images, identification of wetlands, and information extraction. Especially, the cloudy conditions at the growing season (June to September) certainly complicate the extraction of wetland-related information (in particular, marsh wetlands). Though there are some cloud processing techniques, they are limited by image distortion and information loss . In the field of wetland monitoring, the most used and mature RS source is Landsat Thematic Mapper (TM). Despite its
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To collect well calibrated image data a calibrated thermal camera system was required. A Kodak KIR-310 MWIR camera, shown in Figure 5.2, was chosen for the data collection. This camera was developed by Kodak Research Laboratories around 1991. The program was canceled making the camera rare, but still useful for applications such as this. The focal plane array is a PtSi array grown on top of an interlined CCD. It has a format of 640 x 486 pixels making it, for the time of manufacture, one of the largest infrared camera arrays available. The array is divided up into four vertical bar shaped section with a separate amplifier for each. A flat field image showing the effects of four amplifiers can be seen in Figure 5.1. Kodak Research Labs had the foresight to give the camera both a National Television System Committee (NTSC) format analog and an RS-422 12-bit digital output. The analog output was used for displaying the video onto a CRT monitor while the digital output was used to collect the data. The camera and two 4-position filter wheels are cooled with liquid nitrogen for the purpose of lowering the dark noise.
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For a validation of the phase identification technique described above, collocated measurements of solar spec- tral radiation reflected by the clouds and concurrent in situ measurements of the cloud microphysics are ideal. Here two approaches are introduced: helicopterborne (Werner et al. 2013, 2014) and aircraftborne (Frey et al. 2009; Finger et al. 2016). Figure 10-12 illustrates both ap- proaches. For low clouds a slow-flying helicopter is used as an instrument carrier for Spectral Modular Airborne Radiation measurement system (SMART-HELIOS) and Airborne Cloud Turbulence Observation System (ACTOS) payloads. SMART-HELIOS takes spectral cloud reflectivity measurements from above the cloud to remotely sense the cloud ice; ACTOS does the micro- physical cloud sampling to indicate the cloud ice with in situ measurements inside the cloud. For high ice clouds, a fast-flying jet aircraft is used in combination with a towed measurement platform [Airborne Towed Sensor Shuttle (AIRTOSS)]. In this case the remote sensing of the ice in the clouds is done by spectral re- flectivity measurements on board the aircraft; the in situ verification is done by the AIRTOSS, which, by the way, contains not only a cloud microphysical in situ probe but also upward- and downward-looking solar spectral radiometers.
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The potential to use photogrammetry in the ma- rine industry has been investigated early on (Kenefick 1976) as a means of documenting ship- board conditions and expediting ship-check proce- dures for distributive systems such as piping, venti- lation and structural components (Sparacino 1991). Although the advantages from the use of photo- grammetry have been clear, the technological means available at the time, allowed only limited applica- tion of the technique (Mugnier 1997). Still, photo- grammetry has been mainly used for the scheduling of activities by naval shipyards, primarily due to the cost needed to achieve the required accuracy (In- gram 1991) and is recognized as an efficient tool for the shipyard planning process with significant bene- fits in monetary terms (Komoroski 2005). The pri- mary focus of the previous tasks has been the ship- checking prior to overhauling or maintenance activi- ties. In a similar sense, but with a focus rather on the extraction of hull geometrical characteristics, as in (Koelman 2010), the shape - reverse engineering of ships is discussed for shape retrieval for damage re- pairs, post-building verification, etc. In (Menna et al. 2011) the methodology for 3D modeling of floating objects is extracted using a hybrid scheme of terres- trial and underwater image acquisition. The evalua- tion of structural defects has been approached in Chen et al. 2011, by producing a 3D model of distor- tions in plated structures via photogrammetry. In Rodriguez-Martin et al. 2015 a novel method of weld-inspection is given which uses the reconstruc- tion of the welded-surface in a digital format via close-range photogrammetry in order to overcome the need for in-situ inspection.
Costs are decisive for the selection of the optimal design alternative, but are for the most part neglected in publications on inventory concepts for REDD. Reports on costs of different components of an inventory such as ground survey, analysis of remote sensing data, or data cost vary widely. As we did not want to optimize an inventory design for a specific application but illustrate the effect of cost implications on the design selection, we choose a range of realistic costs for field assessments and remote sensing data acquisition and interpretation. Fixed cost components such as administration, training or infrastructure were not included as they are not design dependent. Table 5 shows the costs used in the simula- tion study. For remote sensing imagery two alternative cost scenarios were utilized. Alternative 1 was chosen according to Asner et al. , who quantified the cost for the acquisition of LiDAR data with 0.16 US$/ha for
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ABSTRACT: To determine the suitable indices for vegetation cover and production assessment based on the remote sensing data, simultaneous digital data with field data belonging to the spring rangeland of the Semirom-Isfahan province were analyzed. During two years of monitoring the annual, grass, forb, and shrub vegetation cover and the total production data from 86 were collected. The Global Positioning System (GPS) was used to measure the coordinates of plots and transects. Geometric correction and histogram equalization were applied in image processing, and image digital numbers were converted to reflectance numbers. In the next stage, all vegetation indices were calculated from the Advanced Wide Field Sensor (AWiFS) image data and compared with the vegetation cover estimates, at monitoring points, made during field assessments. A linear regression model was used to select suitable vegetation indices. The results showed that there were significant relationships between the satellite data and the vegetative characteristics. Among the indices, the Normalized Difference Vegetation Index (NDVI) consistently showed significant relationships with the vegetation cover. The estimation of the vegetation cover with the NDVI vegetation index was more accurately predicted within rangeland systems. Using the produced model from the NDVI index vegetation crown cover, percentage maps were produced in three class percentages for each image. Generally introduced indices provided accurate quantitative estimation of the parameters. Therefore, it was possible to estimate cover and production as important factors for range monitoring using the AWiFS data. The Remote sensing data and the Geographic Information System are the most effective tools in natural resource management.
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