EO Sensor Planning for UAV Engineering Reconnaissance Based on NIIRS and GIQE

Bai, Jingbo; Sun, Yu; Chen, Liang; Feng, Yufang; Liu, Jianyong

doi:10.1155/2018/6837014

Cited by 12 publications

(13 citation statements)

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“…Detailed guidelines related to the quality of the recognition material that should be obtained were not also taken into account. The quality of the material is determined in the case of EO/IR systems on the The National Imagery Interpretability Rating Scale (NIIRS) (see in [ 12 ]), which can be adapted to SAR. From the point of view of optimization principles, therefore, no procedures were presented for solving the local task, i.e., planning the flight trajectory to recognize each of the targets in its location.…”

Section: Introductionmentioning

confidence: 99%

“…In the first part of the article, in Section 2.1 , the requirements related to the quality of recognition data that should be obtained by UAVs are discussed. In the case of optoelectronic systems, the data quality is described by the NIIRS [ 12 , 13 ]. In the case of SAR, the quality can be expressed in NIIRS or the resolution of the obtained scan [ 12 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

“…In the case of optoelectronic systems, the data quality is described by the NIIRS [ 12 , 13 ]. In the case of SAR, the quality can be expressed in NIIRS or the resolution of the obtained scan [ 12 , 13 , 14 ]. Principles of flight planning in the neighborhood of the target were discussed when the planner has a terrain map with an altitude grid DTED (for more information about DTED see in [ 3 ]).…”

Section: Introductionmentioning

confidence: 99%

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Determining UAV Flight Trajectory for Target Recognition Using EO/IR and SAR

Stecz

Gromada

2020

Sensors

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The paper presents the concept of planning the optimal trajectory of fixed-wing unmanned aerial vehicle (UAV) of a short-range tactical class, whose task is to recognize a set of ground objects as a part of a reconnaissance mission. Tasks carried out by such systems are mainly associated with an aerial reconnaissance using Electro-Optical/Infrared (EO/IR) systems and Synthetic Aperture Radars (SARs) to support military operations. Execution of a professional reconnaissance of the indicated objects requires determining the UAV flight trajectory in the close neighborhood of the target, in order to collect as much interesting information as possible. The paper describes the algorithm for determining UAV flight trajectories, which is tasked with identifying the indicated objectives using the sensors specified in the order. The presence of UAV threatening objects is taken into account. The task of determining the UAV flight trajectory for recognition of the target is a component of the planning process of the tactical class UAV mission, which is also presented in the article. The problem of determining the optimal UAV trajectory has been decomposed into several subproblems: determining the reconnaissance flight method in the vicinity of the currently recognized target depending on the sensor used and the required parameters of the recognition product (photo, film, or SAR scan), determining the initial possible flight trajectory that takes into account potential UAV threats, and planning detailed flight trajectory considering the parameters of the air platform based on the maneuver planning algorithm designed for tactical class platforms. UAV route planning algorithms with time constraints imposed on the implementation of individual tasks were used to solve the task of determining UAV flight trajectories. The problem was formulated in the form of a Mixed Integer Linear Problem (MILP) model. For determining the flight path in the neighborhood of the target, the optimal control algorithm was also presented in the form of a MILP model. The determined trajectory is then corrected based on the construction algorithm for determining real UAV flight segments based on Dubin curves.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Determining UAV Flight Trajectory for Target Recognition Using EO/IR and SAR

Stecz

Gromada

2020

Sensors

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“…A variety of image quality assessment methods have been developed to assess satellite image quality in terms of spatial and spectral consistency of data. Satellite image quality is a subject of considerable scientific interest because of its multifaceted nature and has been expressed using several technical parameters such as ground sampling distance (GSD), relative edge response (RER), edge extent (EE), line spread function (LSF), modulation transfer function (MTF) or point spread function (PSF), and signal to noise ratio (SNR) are used to quantitatively characterize the images in terms of sharpness, noise, nonlinearities, and artefacts [19,20]. The National Image Interpretation Rating Scale (NIIRS) is another well-established image quality metric that is used for evaluating image interpretability and defines objects that are discernible in an image using a rating scale of 0 to 9 [21].…”

Section: Introductionmentioning

confidence: 99%

“…JPEG compression imaging workflow (image credit: SCSAG).Aerospace 2020, 7,19 6 JPEG compression imaging workflow (image credit: SCSAG).…”

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confidence: 99%

Image Interpretability of nSight-1 Nanosatellite Imagery for Remote Sensing Applications

2020

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Nanosatellites are increasingly being used in space-related applications to demonstrate and test scientific capability and engineering ingenuity of space-borne instruments and for educational purposes due to their favourable low manufacturing costs, cheaper launch costs, and short development time. The use of CubeSat to demonstrate earth imaging capability has also grown in the last two decades. In 2017, a South African company known as Space Commercial Services launched a low-orbit nanosatellite named nSight-1. The demonstration nanosatellite has three payloads that include a modular designed SCS Gecko imaging payload, FIPEX atmospheric science instrument developed by the University of Dresden and a Radiation mitigation VHDL coding experiment supplied by Nelson Mandela University. The Gecko imager has a swath width of 64 km and captures 30 m spatial resolution images using the red, green, and blue (RGB) spectral bands. The objective of this study was to assess the interpretability of nSight-1 in the spatial dimension using Landsat 8 as a reference and to recommend potential earth observation applications for the mission. A blind image spatial quality evaluator known as Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) was used to compute the image quality for nSight-1 and Landsat 8 imagery in the spatial domain and the National Imagery Interpretability Rating Scale (NIIRS) method to quantify the interpretability of the images. A visual interpretation was used to propose some potential applications for the nSight1 images. The results indicate that Landsat 8 OLI images had significantly higher image quality scores and NIIRS results compared to nSight-1. Landsat 8 has a mean of 19.299 for the image quality score while nSight-1 achieved a mean of 25.873. Landsat 8 had NIIRS mean of 2.345 while nSight-1 had a mean of 1.622. The superior image quality and image interpretability of Landsat could be attributed for the mature optical design on the Landsat 8 satellite that is aimed for operational purposes. Landsat 8 has a GDS of 30-m compared to 32-m on nSight-1. The image degradation resulting from the lossy compression implemented on nSight-1 from 12-bit to 8-bit also has a negative impact on image visual quality and interpretability. Whereas it is evident that Landsat 8 has the better visual quality and NIIRS scores, the results also showed that nSight-1 are still very good if one considers that the categorical ratings consider that images to be of good to excellent quality and a NIIRS mean of 1.6 indicates that the images are interpretable. Our interpretation of the imagery shows that the data has considerable potential for use in geo-visualization and cartographic land use and land cover mapping applications. The image analysis also showed the capability of the nSight-1 sensor to capture features related to structural geology, geomorphology and topography quite prominently.

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A two-stage stochastic optimization model for integrated tram timetable and speed control with uncertain dwell times

Bai

Chen

et al. 2022

Energy

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EO Sensor Planning for UAV Engineering Reconnaissance Based on NIIRS and GIQE

Cited by 12 publications

References 11 publications

Determining UAV Flight Trajectory for Target Recognition Using EO/IR and SAR

Determining UAV Flight Trajectory for Target Recognition Using EO/IR and SAR

Image Interpretability of nSight-1 Nanosatellite Imagery for Remote Sensing Applications

A two-stage stochastic optimization model for integrated tram timetable and speed control with uncertain dwell times

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