Low-altitude Flight Research Articles

Tip Vortex Effects on Rotor Aeroacoustics via Hybrid Blade Element Momentum Theory–Free Vortex Wake Prediction

During rotor operation, wakes and tip vortices form and convect downstream, generating unsteady velocities on the blade surface, which act as significant noise sources. High-fidelity numerical simulations account for these effects but are computationally expensive, while the rapid predictions based on blade element momentum theory (BEMT) alone cannot capture the fluid mechanisms associated with tip vortex wakes. This paper presents a hybrid BEMT and free vortex wake (FVW) method to compute mean and unsteady flow variables on blade sections. The BEMT solver accommodates arbitrary flow directions, accounting for variations with the rotational phase angle, while the FVW models tip vortex dynamics with initial strength from BEMT computations. Unsteady airfoil theory is applied to compute time-varying loadings in inflight conditions, serving as noise sources for acoustic calculations. The method is validated with a benchmark rotor in hover and axial flow conditions, where wake correction alters thrust but has negligible noise impact. In inflight conditions, the FVW significantly improves prediction accuracy for blade passing frequency harmonics compared to BEMT alone. Near-field velocity and vortex structures also match high-fidelity simulations well. Application to a multirotor vehicle demonstrates the method’s potential for rapid and efficient noise estimation in low-altitude flight scenarios.

Correction of systematic image misalignment in direct georeferencing of UAV multispectral imagery

ABSTRACT Mosaicking of Unmanned Aerial Vehicles (UAV) imagery over featureless water bodies has been known to be challenging, and poses a significant impediment to water monitoring applications. Techniques such as Structure-from-motion typically fail under such conditions due to the lack of distinctive features in the scene, and direct georeferencing is currently the only practical solution, albeit lower georeferencing accuracy is expected. However, hardware issues, particularly the typical time delay between the GPS unit and the image capture, can lead to systematic image misalignment and further reducing the accuracy. The systematic image misalignment arises as the recording of the geographical coordinates by the GPS unit may not precisely correspond to the exact moment of image exposure, and the image exposure may not always occur at the mid-exposure time. Hardware solutions can mitigate this issue but require technical expertise and resources. Alternatively, software solutions can address the problem without necessitating any hardware modifications. This study introduces an open-source solution for the correction of the systematic image alignment by accounting for the time delay and distance discrepancy between the measurements of the GPS coordinates and the image capture. The method was validated with field UAV surveys conducted in this study under various flight configurations (different flight altitudes and overlap ratios), and effective image alignment was obtained using the proposed open-source solution which reduced the georeferencing error by around 67.7%. Specifically, a georeferencing error of RMSE = 1.409 m and σ = 0.6356 m was achieved without the use of any ground control points (GCPs). Finally, as demonstrated in this study, low flight altitudes (e.g. 15 m) should be discouraged for such conditions as georeferencing errors could amplify due to the limited accuracy of the GPS, resulting in visual artefacts.

Forced Landing Flight Test of Compound-Wing UAVs Based on Proportional Guidance

With the development of the low-altitude economy, the demand for low-altitude flight missions has steadily increased. However, such flights often encounter special circumstances requiring emergency landings. To address this, a forced landing guidance law is designed in this paper, using a small compound-wing aircraft as the research object and based on proportional guidance principles. The guidance law calculates lateral and longitudinal acceleration commands using azimuth and elevation angles provided by a seeker, which are then input into the inner-loop control law to generate control surface commands. These commands guide the aircraft toward a nearby landing point. Hardware-in-the-loop simulations demonstrate the effectiveness and robustness of the proposed guidance law, and flight tests further validate its practical applicability.

Discriminating Seagrasses from Green Macroalgae in European Intertidal Areas Using High-Resolution Multispectral Drone Imagery

Coastal areas support seagrass meadows, which offer crucial ecosystem services, including erosion control and carbon sequestration. However, these areas are increasingly impacted by human activities, leading to habitat fragmentation and seagrass decline. In situ surveys, traditionally performed to monitor these ecosystems, face limitations on temporal and spatial coverage, particularly in intertidal zones, prompting the addition of satellite data within monitoring programs. Yet, satellite remote sensing can be limited by too coarse spatial and/or spectral resolutions, making it difficult to discriminate seagrass from other macrophytes in highly heterogeneous meadows. Drone (unmanned aerial vehicle—UAV) images at a very high spatial resolution offer a promising solution to address challenges related to spatial heterogeneity and the intrapixel mixture. This study focuses on using drone acquisitions with a ten spectral band sensor similar to that onboard Sentinel-2 for mapping intertidal macrophytes at low tide (i.e., during a period of emersion) and effectively discriminating between seagrass and green macroalgae. Nine drone flights were conducted at two different altitudes (12 m and 120 m) across heterogeneous intertidal European habitats in France and Portugal, providing multispectral reflectance observation at very high spatial resolution (8 mm and 80 mm, respectively). Taking advantage of their extremely high spatial resolution, the low altitude flights were used to train a Neural Network classifier to discriminate five taxonomic classes of intertidal vegetation: Magnoliopsida (Seagrass), Chlorophyceae (Green macroalgae), Phaeophyceae (Brown algae), Rhodophyceae (Red macroalgae), and benthic Bacillariophyceae (Benthic diatoms), and validated using concomitant field measurements. Classification of drone imagery resulted in an overall accuracy of 94% across all sites and images, covering a total area of 467,000 m2. The model exhibited an accuracy of 96.4% in identifying seagrass. In particular, seagrass and green algae can be discriminated. The very high spatial resolution of the drone data made it possible to assess the influence of spatial resolution on the classification outputs, showing a limited loss in seagrass detection up to about 10 m. Altogether, our findings suggest that the MultiSpectral Instrument (MSI) onboard Sentinel-2 offers a relevant trade-off between its spatial and spectral resolution, thus offering promising perspectives for satellite remote sensing of intertidal biodiversity over larger scales.

Prediction method for 4D trajectory of large fixed wing UAV under multi-layer convolution and bidirectional gating model

Abstract With the increase of low-altitude flight tasks, the importance of research on low-altitude flight safety and efficient operation has gradually become prominent. Therefore, it is necessary to try to establish a low-altitude airspace flight path pre-planning method based on 4D trajectory prediction, which can effectively support the above research. In order to improve the integrity, accuracy and reliability of the long-range trajectory prediction results of large fixed wing UAV (LFW UAV), based on the standard neural network as the basic concept to form a fusion model of improved convolutional neural network (CNN) and bidirectional gated recurrent neural network (BIGRU). At the same time, the flight trajectory fitting preprocessing is added to build the completed multilayer cyclic convolutional improvement model (MCCI). The experimental results show that the MCCI model has obvious advantages in terms of prediction accuracy, deviation range and predictable duration when dealing with the LFW UAV 4D trajectory prediction problem, and better reflects the characteristics of 3D position and spatiotemporal elements. The comparative analysis shows that the error values of the MCCI model are smaller than those of the comparison prediction models in any dimension, and the prediction results have better fit and model performance.

Enhancing Winter Wheat Soil-Plant Analysis Development Value Prediction through Evaluating Unmanned Aerial Vehicle Flight Altitudes, Predictor Variable Combinations, and Machine Learning Algorithms.

Monitoring winter wheat Soil-Plant Analysis Development (SPAD) values using Unmanned Aerial Vehicles (UAVs) is an effective and non-destructive method. However, predicting SPAD values during the booting stage is less accurate than other growth stages. Existing research on UAV-based SPAD value prediction has mainly focused on low-altitude flights of 10-30 m, neglecting the potential benefits of higher-altitude flights. The study evaluates predictions of winter wheat SPAD values during the booting stage using Vegetation Indices (VIs) from UAV images at five different altitudes (i.e., 20, 40, 60, 80, 100, and 120 m, respectively, using a DJI P4-Multispectral UAV as an example, with a resolution from 1.06 to 6.35 cm/pixel). Additionally, we compare the predictive performance using various predictor variables (VIs, Texture Indices (TIs), Discrete Wavelet Transform (DWT)) individually and in combination. Four machine learning algorithms (Ridge, Random Forest, Support Vector Regression, and Back Propagation Neural Network) are employed. The results demonstrate a comparable prediction performance between using UAV images at 120 m (with a resolution of 6.35 cm/pixel) and using the images at 20 m (with a resolution of 1.06 cm/pixel). This finding significantly improves the efficiency of UAV monitoring since flying UAVs at higher altitudes results in greater coverage, thus reducing the time needed for scouting when using the same heading overlap and side overlap rates. The overall trend in prediction accuracy is as follows: VIs + TIs + DWT > VIs + TIs > VIs + DWT > TIs + DWT > TIs > VIs > DWT. The VIs + TIs + DWT set obtains frequency information (DWT), compensating for the limitations of the VIs + TIs set. This study enhances the effectiveness of using UAVs in agricultural research and practices.

Modular Approach for Online Vertical Obstacle Detection

Poles, towers, and other vertical structures are a significant hazard to low-altitude flight operations. Three-dimensional sensors have the ability to perceive these objects; however, detection is hindered by the overwhelming preponderance of returns from other surfaces. This paper first evaluates existing approaches to finding vertical structures. Then the paper proposes a fast, modular process that efficiently removes extraneous points and automatically distills remaining data to find significant vertical structures with application to dense and sparse datasets. We apply our approach to the analysis of 200 million LiDAR points from a variety of real-world scenes. With our algorithm, the average density of identified pole returns for communication towers increases from 0.1% to over 60%. Finding other vertical structures is more difficult, but prevalence generally increases for these objects also.

Experiments with Combining LiDAR and Camera Data Acquired by UAS and Smartphone to Support Mapping

Abstract. Aerial mapping using Unmanned Aerial Systems (UAS), such as the DJI Mavic 3 Enterprise, has revolutionized photogrammetry, enabling efficient data capture for small-scale projects. The typical nadir perspective of UAS mapping, however, imposes limitations on capturing critical details of features due to its predominantly vertical viewpoint. Overcoming this challenge often requires manual, low-altitude flights by experienced UAS pilots to achieve high-angle oblique perspectives, unless gimbled camera mount is used. This study explores the integration of high oblique angle perspectives using the iPhone 15 Pro, which boasts advanced camera capabilities and an integrated LiDAR sensor, to complement UAS imagery. The iPhone 15 Pro's camera sensors provide a Ground Sampled Distance (GSD) comparable to UAS cameras, while its LiDAR sensor, with about five meters of range, enhances mapping capabilities by delivering accurate depth measurements in close range. By utilizing various georeferencing options for the imagery and LiDAR data from the iPhone 15 Pro with UAS nadir imagery, we can achieve a more comprehensive object space reconstruction, significantly improving the accuracy of geospatial mapping. Both the Mavic 3 Enterprise and the iPhone 15 Pro, though operating independently on their respective platforms, support Real-Time Kinematic (RTK) corrections, facilitating precise positioning for the entire system trajectory. Strategic placement and utilization of Ground Control Points (GCPs) aid in the georeferencing of the complete dataset, enhancing its overall accuracy. To validate the accuracy of the acquired data, checkpoints are established on-site. The positions derived from the integrated UAS and iPhone 15 Pro data are compared against these checkpoints to quantify the accuracy and reliability of the data. Additionally, Post-Processed Kinematic (PPK) techniques are employed to validate the trajectories of all data collection systems, ensuring the reliability of the acquired data, especially in instances where RTK corrections may be lacking. In summary, this research showcases comprehensive, multi-dimensional geospatial datasets by conducting validation studies that assess the accuracy and reliability of georeferenced datasets against known ground truth checkpoints. Such validation studies are crucial for identifying gaps in current methodologies and suggesting areas for improvement, thereby aiming to enhance the quality and accuracy of geospatial mapping applications. Through the integration of UAS and smartphone mapping, complemented by rigorous validation efforts, we aspire to achieve improved geospatial mapping accuracy.

Balancing predictability and flexibility through operation volume-constrained visual flight rule operations in low altitude airspaces

The advancement in uncrewed aircraft systems such as small drones and advanced air mobility vehicles such as Electric Vertical Take Off and Landing aircraft (eVTOL) has called for airspace integration at low altitudes of both traditional aircraft, such as helicopters and new entrants, such as drones and eVTOL. Currently, the trajectories and necessary buffers around them of flights operating under visual flight rules are not possible to predict. This research proposes the use of flight mission characteristics to model the trajectory and evaluates temporal, lateral and vertical deviations to define the safety buffers which can be used to generate operation volumes, geo-fencing such low-altitude flights and separating them from other traffic in a safe and efficient manner. Real flight test data obtained for the purposes of this study and pilots’ interviews assure high fidelity and practicability of the proposal.

Enhancing sustainable urban air transportation: Low-noise UAS flight planning using noise assessment simulator

Low-altitude flights of unmanned aircraft systems (UASs), such as drones, can cause substantial noise pollution in urban areas. This study aims to tackle the crucial issue of noise in UAS flight planning and analyze the feasibility of implementing low-noise urban flights at a micro level. We employ a virtual flight simulator to accurately model the UAS noise impact in realistic flights and optimize the flight path via a heuristic algorithm accordingly. The flight noise assessment incorporates a sound source modeling technique for practical UAS models and an efficient Gaussian beam tracing method for outdoor noise propagation in realistic environments. Case studies for flight simulations are conducted in a representative resident community and metropolitan area to evaluate the noise impact resulting from different flight paths. By comparing our approach with benchmark shortest-distance paths, we validate its effectiveness in significantly reducing en-route noise exposure, encompassing both instantaneous and continuous noise levels. This method holds great potential for contributing to the planning and management of urban air transport systems. It effectively mitigates noise impact, promoting quieter and more environmentally friendly UAS operations and sustainable urban transportation.

Health Effects Associated with Occupational Exposure to Gamma Radiation in Aircrew: A Case Study on Mehrabad Airport in 2021

Introduction: Aircrew is exposed to harmful levels of gamma radiation with unknown effects. This study aims to investigate occupational exposure to cosmic gamma radiation and its associated health effects among the aircrew members of Iran Airlines.
 Methods: This analytical cross-sectional study was carried out on the crew from four internal flights departing from Mehrabad Airport in 2021. The participants were divided into two groups of 100 aircrew members flying on low-altitude and high-altitude routes, and the history of their illnesses in the past thres years was extracted from medical records. The average annual effective dose (ED) of gamma radiation for the aircrew was measured by dosimeter (CEM DT-9501), and data analysis was done using SPSS16 software.
 Results: This study found that the average annual ED of gamma rays was approximately 1.5 millisieverts higher in flight crews on high-altitude flights compared to the low-altitude ones. Moreover, a significant relationship was observed between exposure to gamma and occupational disease in the studied subjects (P < 0.05). Therefore, the risk of gastrointestinal, skin, and cardiovascular diseases was 3.55, 3.63, and 12.4 times higher for the crew on high-altitude flights compared with those on low-altitude flights.
 Conclusion: High-altitude flights are associated with increased exposure to gamma radiation, leading to a threefold higher risk of occupational diseases such as gastrointestinal, skin, and cardiovascular conditions among aircrew members. These findings highlight the importance of reducing health risks of exposure to gamma rays in aviation industry and emphasize the need for preventive measures to protect the well-being of aircrew personnel.

Contrail formation on ambient aerosol particles for aircraft with hydrogen combustion: a box model trajectory study

Abstract. Future air traffic using (green) hydrogen (H2) promises zero carbon emissions, but the effects of contrails from this new technology have hardly been investigated. We study contrail formation behind aircraft with H2 combustion by means of the particle-based Lagrangian Cloud Module (LCM) box model. Assuming the absence of soot and ultrafine volatile particle formation, contrail ice crystals form solely on atmospheric background particles mixed into the plume. While a recent study extended the original LCM with regard to the contrail formation on soot particles, we further advance the LCM to cover the contrail formation on ambient particles. For each simulation, we perform an ensemble of box model runs using the dilution along 1000 different plume trajectories. The formation threshold temperature of H2 contrails is around 10 K higher than for conventional contrails (which form behind aircraft with kerosene combustion). Then, contrail formation becomes primarily limited by the homogeneous freezing temperature of the water droplets such that contrails can form at temperatures down to around 234 K. The number of ice crystals formed varies strongly with ambient temperature even far away from the contrail formation threshold. The contrail ice crystal number clearly increases with ambient aerosol number concentration and decreases significantly for ambient particles with mean dry radii ⪅ 10 nm due to the Kelvin effect. Besides simulations with one aerosol particle ensemble, we analyze contrail formation scenarios with two co-existing aerosol particle ensembles with different mean dry sizes or hygroscopicity parameters. We compare them to scenarios with a single ensemble that is the average of the two aerosol ensembles. We find that the total ice crystal number can differ significantly between the two cases, in particular if nucleation-mode particles are involved. Due to the absence of soot particle emissions, the ice crystal number in H2 contrails is typically reduced by more than 80 %–90 % compared to conventional contrails. The contrail optical thickness is significantly reduced, and H2 contrails either become visible later than kerosene contrails or are not visible at all for low ambient particle number concentrations. On the other hand, H2 contrails can form at lower flight altitudes where conventional contrails would not form.

Modeling advanced air mobility aircraft in data-driven reduced order realistic urban winds

The concept of Advanced Air Mobility involves utilizing cutting-edge transportation platforms to transport passengers and cargo efficiently over short distances in urban and suburban areas. However, using simplified atmospheric models for aircraft simulations can prove insufficient for modeling large disturbances impacting low-altitude flight regimes. Due to the complexities of operating in urban environments, realistic wind modeling is necessary to ensure trajectory planning and control design can maintain high levels of safety. In this study, we simulate the dynamic response of a representative advanced air mobility platform operating in wing-borne flight through an urban wind field generated using Large Eddy Simulations (LES) and a wind field created using reduced-order models based on full-order computational solutions. Our findings show that the longitudinal response of the aircraft was not greatly affected by the fidelity of the LES models or if the spatial variation was considered while evaluating the full-order wind model. This is encouraging as it indicates that the full LES generation of the wind field may not be necessary, which decreases the complexity and time needed in this analysis. Differences are present when comparing the lateral response, owing to the differences in the asymmetric loading of the planform in the full and reduced order models. These differences seen in the lateral responses are expected to increase for planforms with smaller wing loadings, which could pose challenges. Additionally, the response of the aircraft to the mean wind field, the temporal average of the full order model, was misrepresentative in the longitudinal response and greatly under-predicted control surface activity, particularly in the lateral response.

Power Inspection UAV Task Assignment Matrix Reversal Genetic Algorithm

Traditional manual power inspections are characterized by low efficiency, lengthy processes, and high costs. Existing research on UAV-based power inspections has often overlooked critical factors such as the risk levels of target tasks, the duration of tasks executed by UAVs, and the utility per unit task. To address these gaps, this paper proposes a task allocation method for UAV power inspections based on the Time Window Matrix Reversal Genetic Algorithm (TMGA). Firstly, the proposed cost model accounts for the risk levels of inspection tasks and the impact of low-altitude flight on energy consumption. Secondly, an inspection task allocation model is constructed with the goal of maximizing UAV inspection unit utility. The model is then optimized using two-point crossover and single-point reversal mutation operations, which enhance the UAV unit utility and generate an optimal allocation matrix. The performance of TMGA is evaluated through simulation experiments in three different scenarios, comparing it with existing algorithms. The results show that TMGA outperforms these algorithms in terms of average task time, task completion rate, and unit utility. Specifically, TMGA reduces the average task time by 37% compared to the Cluster Grouping Consensus-base Bundle Algorithm and improves task unit utility by 56.91% compared to the Genetic Algorithm.

Modeling and detection of low-altitude flight conflict network based on SVM

With the continuous increase in low altitude flight density, the issue of low altitude navigation safety has attracted widespread attention. Due to the complex low altitude environment, low altitude flight is more susceptible to ground obstacles and weather effects than commercial aviation. In order to ensure the flight safety of helicopters in low altitude airspace, this paper proposes an improved support vector machine based flight conflict detection model. By modeling the conflict network in low altitude flight areas and utilizing Support Vector Machine (SVM) classification features, the safety discrimination of low altitude flight was achieved, ultimately achieving the safety of aircraft in low altitude flight. This article adopts a protected area model that considers the shape of the aircraft as a conflict zone. In order to reduce the complexity of the conflict detection model, an improved ID3 decision tree algorithm and random forest are used to reduce the complexity of the classifier. The study solved the saturation problem of S-type functions in conflict detection models by using more sensitive functions for probability mapping. And use intelligent optimization algorithms to pre train key parameters, achieving efficient conflict detection suitable for low altitude flight.

FIRING EFFICIENCY OF ANTI-AIRCRAFT MISSILE COMPLEX “STRILA-10” IN TARGET DETECTION THROUGH THE OPTICAL VISOR AND AUTOMATED TARGET POINTER

When preparing for and conducting combat operations of the “SA-13 Gopher” anti-aircraft missile system, commanders and combat personnel of the system must know the missile firing efficiency indicators [1–4]. As an indicator of missile firing efficiency, the probability of hitting a target with one missile is usually used. Massive homing is carried out via the contrast channel or the infrared channel of the missile homing head. Under various conditions of use, it is necessary to evaluate the detection ranges that are realised and the probability of timely target detection, launch range and missile launch probability. It is also necessary to imagine the possible values of the inclined distances to the far edge of the kill zone of the complex. The probability of hitting a target with a single missile is affected by the geometric dimensions of the targets, their colour and contrast in the infrared wavelength range, the presence of target designation, meteorological range of visibility etc.
 The task of determining the firing efficiency of the “SA-13 Gopher” anti-aircraft missile system when detecting targets through the optical sight and automated targeting is therefore relevant and important.
 Research presented in the published [1–4; 12–13] do not allow determining the value of the probability of hitting different air targets in various conditions of the complex’s use. This literature provides only general approaches to solving this problem for typical conditions of use of an anti-aircraft missile system. A numerical modeling technique for determining the firing efficiency indicator in the form of values of the probability of hitting targets by a missile has been developed. The modeling took into account the speed of the targets, their plane in the shooting picture, their radiating capabilities and meteorological visibility range, the quality of the combat work of the operators of the combat vehicle, the sun exposure and the closing angles of the combat vehicle. Found possible detection ranges and detection probabilities of a typical target and a small unmanned aerial vehicle (UAV). A high probability of the operator of a combat vehicle missing a typical target at low and medium flight altitudes was noted. Considered launch ranges of missiles that are implemented and the probability of launching a missile when targets are captured in the contrast and infrared channels of the homing head. The largest inclined ranges to the far boundary of the zone of damage of the complex were calculated. The values of the probability of hitting a typical target and UAV in various firing conditions are determined. Sufficient firing efficiency has been proven when capturing and launching a missile in the contrast guidance channel and firing at a typical target. A comparison of the contrast and infrared channels of the homing missile head when firing at UAVs was carried out. Analytical expressions for the calculation of missile firing efficiency indicators and corresponding graphic material are presented.

Noise-aware UAS flight path planning based on virtual flight simulation

This paper proposes a noise-aware flight path planning method to reduce the noise pollution caused by low-altitude flights of unmanned aerial systems (UAS) in urban areas. A noise assessment platform is used to obtain noise maps at discretized airspace blocks, considering sound reflection and absorption by obstacles, as well as diffraction and attenuation by the atmosphere. An improved cost-based A* algorithm is employed to search for the optimal path with the minimum sound exposure level. Virtual flight simulations demonstrate the effectiveness of the proposed method in reducing en-route noise exposure from flying drones. This cost-effective and efficient approach has the potential to benefit air traffic planning and management.

Review of Key Technologies of Rotary-Wing Mars UAVs for Mars Exploration

The sparse atmosphere on the surface of Mars provides the necessary flight conditions for Mars unmanned aerial vehicles (UAVs) to perform low-altitude flights. This work presents a comprehensive overview of key technologies in the development of Mars UAVs, with a specific focus on rotary-wing Mars UAVs. It summarizes prototypes of rotary-wing Mars UAVs developed by various global research institutions. It reviews essential technologies in rotary-wing Mars UAV research, including the Mars near-surface atmospheric environment, aerodynamic characteristics, and principles of low-pressure flight control. This work also summarizes various experimental setups and ground test results for rotary-wing Mars UAVs. Furthermore, it discusses the future development trends of rotary-wing Mars UAVs.

Location decision of low-altitude service station for transfer flight based on modified immune algorithm

The location of Low-Altitude Flight Service Station (LAFSS) is a comprehensive decision work, and it is also a multi-objective optimization problem (MOOP) with constraints. As a swarm intelligence search algorithm for solving constrained MOOP, the Immune Algorithm (IA) retains the excellent characteristics of genetic algorithm. Using some characteristic information or knowledge of the problem selectively and purposefully, the degradation phenomenon in the optimization process can be suppressed and the global optimum can be achieved. However, due to the large range involved in the low-altitude transition flight, the geographical characteristics, economic level and service requirements among the candidate stations in the corridor are quite different, and the operational safety and service efficiency are interrelated and conflict with each other. And all objectives cannot be optimal. Therefore, this article proposes a Modified Immune Algorithm (MIA) with two-layer response to solve the constrained multi-objective location mathematical model of LAFSS. The first layer uses the demand track as the cell membrane positioning pattern recognition service response distance to trigger the innate immunity to achieve the basic requirements of security service coverage. In the second layer, the expansion and upgrading of adjacent candidate sites are compared to the pathogen’s effector, and the adaptive immunity is directly or indirectly triggered again through the cloning, mutation and reproduction between candidate sites to realize the multi-objective equilibrium of the scheme. Taking 486,000 km2 of Sichuan Province as an example, MIA for LAFSS is simulated by the MATLAB platform. Based on the Spring open source application framework of Java platform, the cesiumjs map data is called through easyui, and the visualization of site selection scheme is presented with the terrain data of Map World as the background. The experimental results show that, compared with dynamic programming and ordinary immunization, the immune trigger mode of double response and the improved algorithm of operation parameter combination designed by the Taguchi experiment, the total economic cost of location selection is reduced by 26.4%, the service response time is reduced by 25%, the repeat coverage rate is reduced by 29.5% and the effective service area is increased by 17.5%. The security risk, service efficiency and location cost are balanced. The present work is to provide an effective location method for the layout number and location of local transfer flight service stations. For complex scenes with larger scale of low-altitude flight supply and demand and larger terrain changes in the region, the above research methods can be used to effectively split and reduce the dimension.

Enhancing Feature Detection and Matching in Low-Pixel-Resolution Hyperspectral Images Using 3D Convolution-Based Siamese Networks.

Today, hyperspectral imaging plays an integral part in the remote sensing and precision agriculture field. Identifying the matching key points between hyperspectral images is an important step in tasks such as image registration, localization, object recognition, and object tracking. Low-pixel resolution hyperspectral imaging is a recent introduction to the field, bringing benefits such as lower cost and form factor compared to traditional systems. However, the use of limited pixel resolution challenges even state-of-the-art feature detection and matching methods, leading to difficulties in generating robust feature matches for images with repeated textures, low textures, low sharpness, and low contrast. Moreover, the use of narrower optics in these cameras adds to the challenges during the feature-matching stage, particularly for images captured during low-altitude flight missions. In order to enhance the robustness of feature detection and matching in low pixel resolution images, in this study we propose a novel approach utilizing 3D Convolution-based Siamese networks. Compared to state-of-the-art methods, this approach takes advantage of all the spectral information available in hyperspectral imaging in order to filter out incorrect matches and produce a robust set of matches. The proposed method initially generates feature matches through a combination of Phase Stretch Transformation-based edge detection and SIFT features. Subsequently, a 3D Convolution-based Siamese network is utilized to filter out inaccurate matches, producing a highly accurate set of feature matches. Evaluation of the proposed method demonstrates its superiority over state-of-the-art approaches in cases where they fail to produce feature matches. Additionally, it competes effectively with the other evaluated methods when generating feature matches in low-pixel resolution hyperspectral images. This research contributes to the advancement of low pixel resolution hyperspectral imaging techniques, and we believe it can specifically aid in mosaic generation of low pixel resolution hyperspectral images.