Remote sensing image encryption algorithm based on novel hyperchaos and an elliptic curve cryptosystem

Remote sensing images carry crucial ground information,often involving the spatial distribution and spatiotemporal changes of surface elements.To safeguard this sensitive data,image encryption technology is essential.In this paper,a novel Fibonacci sine exponential map is designed,the hyperchaotic performance of which is particularly suitable for image encryption algorithms.An encryption algorithm tailored for handling the multi-band attributes of remote sensing images is proposed.The algorithm combines a three-dimensional synchronized scrambled diffusion operation with chaos to efficiently encrypt multiple images.Moreover,the keys are processed using an elliptic curve cryptosystem,eliminating the need for an additional channel to transmit the keys,thus enhancing security.Experimental results and algorithm analysis demonstrate that the algorithm offers strong security and high efficiency,making it suitable for remote sensing image encryption tasks.

Computational Intelligence Driven Secure Unmanned Aerial Vehicle Image Classification in Smart City Environment

Computational intelligence(CI)is a group of nature-simulated computationalmodels and processes for addressing difficult real-life problems.The CI is useful in the UAV domain as it produces efficient,precise,and rapid solutions.Besides,unmanned aerial vehicles(UAV)developed a hot research topic in the smart city environment.Despite the benefits of UAVs,security remains a major challenging issue.In addition,deep learning(DL)enabled image classification is useful for several applications such as land cover classification,smart buildings,etc.This paper proposes novel meta-heuristics with a deep learning-driven secure UAV image classification(MDLS-UAVIC)model in a smart city environment.Themajor purpose of the MDLS-UAVIC algorithm is to securely encrypt the images and classify them into distinct class labels.The proposedMDLS-UAVIC model follows a two-stage process:encryption and image classification.The encryption technique for image encryption effectively encrypts the UAV images.Next,the image classification process involves anXception-based deep convolutional neural network for the feature extraction process.Finally,shuffled shepherd optimization(SSO)with a recurrent neural network(RNN)model is applied for UAV image classification,showing the novelty of the work.The experimental validation of the MDLS-UAVIC approach is tested utilizing a benchmark dataset,and the outcomes are examined in various measures.It achieved a high accuracy of 98%.

Rice Bacterial Infection Detection Using Ensemble Technique on Unmanned Aerial Vehicles Images

Establishing a system for measuring plant health and bacterial infection is critical in agriculture.Previously,the farmers themselves,who observed them with their eyes and relied on their experience in analysis,which could have been incorrect.Plant inspection can determine which plants reflect the quantity of green light and near-infrared using infrared light,both visible and eye using a drone.The goal of this study was to create algorithms for assessing bacterial infections in rice using images from unmanned aerial vehicles(UAVs)with an ensemble classification technique.Convolution neural networks in unmanned aerial vehi-cles image were used.To convey this interest,the rice’s health and bacterial infec-tion inside the photo were detected.The project entailed using pictures to identify bacterial illnesses in rice.The shape and distinct characteristics of each infection were observed.Rice symptoms were defined using machine learning and image processing techniques.Two steps of a convolution neural network based on an image from a UAV were used in this study to determine whether this area will be affected by bacteria.The proposed algorithms can be utilized to classify the types of rice deceases with an accuracy rate of 89.84 percent.

Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features

Road traffic monitoring is an imperative topic widely discussed among researchers.Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides.However,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger area.To this end,different models have shown the ability to recognize and track vehicles.However,these methods are not mature enough to produce accurate results in complex road scenes.Therefore,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image bursts.The extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the images.The masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring system.Next,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise reduction.After preprocessing,the blob detection algorithm helped detect the vehicles.Vehicles of varying sizes have been detected by implementing a dynamic thresholding scheme.Detection was done on the first image of every burst.Then,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching algorithm.To further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple matches.An accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been achieved.For the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.

Estimation and verification of green tide biomass based on UAV remote sensing

Since 2007,the Yellow Sea green tide has broken out every summer,causing great harm to the environment and society.Although satellite remote sensing(RS)has been used in biomass research,there are several shortcomings,such as mixed pixels,atmospheric interference,and difficult field validation.The biomass of green tide has been lacking a high-precision estimation method.In this study,high-resolution unmanned aerial vehicle(UAV)RS was used to quantitatively map the biomass of green tides.By utilizing experimental data from previous studies,a robust relationship was established to link biomass to the red-green-blue floating algae index(RGB-FAI).Then,the lab-based model for green tide biomass from visible images taken by the UAV camera was developed and validated by field measurements.Re sults show that the accurate and cost-effective method is able to estimate the green tide biomass and its changes in given local waters of the near and far seas.The study provided an effective complement to the traditional satellite RS,as well as high-precision quantitative techniques for decision-making in disaster management.

A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography

Recently,there has been a notable surge of interest in scientific research regarding spectral images.The potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable attention.One encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image classification.As a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent beneficiaries.This paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and vegetables.By meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical use.Additionally,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural context.The findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in agriculture.Nevertheless,we also shed light on the various issues and limitations of working with spectral images.This comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.

Tree species classification using deep learning and RGB optical images obtained by an unmanned aerial vehicle

The diversity of tree species and the complexity of land use in cities create challenging issues for tree species classification.The combination of deep learning methods and RGB optical images obtained by unmanned aerial vehicles(UAVs) provides a new research direction for urban tree species classification.We proposed an RGB optical image dataset with 10 urban tree species,termed TCC10,which is a benchmark for tree canopy classification(TCC).TCC10 dataset contains two types of data:tree canopy images with simple backgrounds and those with complex backgrounds.The objective was to examine the possibility of using deep learning methods(AlexNet,VGG-16,and ResNet-50) for individual tree species classification.The results of convolutional neural networks(CNNs) were compared with those of K-nearest neighbor(KNN) and BP neural network.Our results demonstrated:(1) ResNet-50 achieved an overall accuracy(OA) of 92.6% and a kappa coefficient of 0.91 for tree species classification on TCC10 and outperformed AlexNet and VGG-16.(2) The classification accuracy of KNN and BP neural network was less than70%,while the accuracy of CNNs was relatively higher.(3)The classification accuracy of tree canopy images with complex backgrounds was lower than that for images with simple backgrounds.For the deciduous tree species in TCC10,the classification accuracy of ResNet-50 was higher in summer than that in autumn.Therefore,the deep learning is effective for urban tree species classification using RGB optical images.

Estimation of chlorophyll content in Brassica napus based on unmanned aerial vehicle images

The chlorophyll content has a direct effect on photosynthesis of crops.In order to explore a quick and convenient method for estimating the chlorophyll content of Brassica napus and facilitate efficient crop monitoring,we measured the actual value of chlorophyll with a SPAD-502 chlorophyll detector,and collected aerial images of B.napus with an unmanned aerial vehicle(UAV)carrying a RGB camera in this study.The total number of 270samples collected images were divided into regions according to the planting conditions of different B.napus varieties in the field.Then,according to the empirical formula,there were 36 colors’characteristic parameters calculated and combined.To estimate the chlorophyll content of rape,189 samples were included in the modeling set,while the other 81 samples were enrolled in the validation set for testing the accuracy of this model.After the combination of R(red),G(green)and B(blue)color channels,the results showed that the color characteristics B/(R+G),b,B/G,(G-B)/(G+B),g-b were highly connected with the measured value of chlorophyll SPAD,and the correlation coefficient between the combination based on B/(R+G)and SPAD value was 0.747.With R2=0.805,RMSE=3.343,and RE=6.84%,the regression model created using random forest had superior outcomes,according to the model comparison.This study offers a new method for quickly estimating the amount of chlorophyll in rapeseed and a workable reference for crop monitoring using the UAV platform.

Archimedes Optimization with Deep Learning Based Aerial Image Classification for Cybersecurity Enabled UAV Networks

The recent adoption of satellite technologies,unmanned aerial vehicles(UAVs)and 5G has encouraged telecom networking to evolve into more stable service to remote areas and render higher quality.But,security concerns with drones were increasing as drone nodes have been striking targets for cyberattacks because of immensely weak inbuilt and growing poor security volumes.This study presents an Archimedes Optimization with Deep Learning based Aerial Image Classification and Intrusion Detection(AODL-AICID)technique in secure UAV networks.The presented AODLAICID technique concentrates on two major processes:image classification and intrusion detection.For aerial image classification,the AODL-AICID technique encompasses MobileNetv2 feature extraction,Archimedes Optimization Algorithm(AOA)based hyperparameter optimizer,and backpropagation neural network(BPNN)based classifier.In addition,the AODLAICID technique employs a stacked bi-directional long short-term memory(SBLSTM)model to accomplish intrusion detection for cybersecurity in UAV networks.At the final stage,the Nadam optimizer is utilized for parameter tuning of the SBLSTM approach.The experimental validation of the AODLAICID technique is tested and the obtained values reported the improved performance of the AODL-AICID technique over other models.

Seamless integration of above-and undercanopy unmanned aerial vehicle laser scanning for forest investigation

Background:Current automated forest investigation is facing a dilemma over how to achieve high tree-and plotlevel completeness while maintaining a high cost and labor efficiency.This study tackles the challenge by exploring a new concept that enables an efficient fusion of aerial and terrestrial perspectives for digitizing and characterizing individual trees in forests through an Unmanned Aerial Vehicle(UAV)that flies above and under canopies in a single operation.The advantage of such concept is that the aerial perspective from the above-canopy UAV and the terrestrial perspective from the under-canopy UAV can be seamlessly integrated in one flight,thus grants the access to simultaneous high completeness,high efficiency,and low cost.Results:In the experiment,an approximately 0.5 ha forest was covered in ca.10 min from takeoff to landing.The GNSS-IMU based positioning supports a geometric accuracy of the produced point cloud that is equivalent to that of the mobile mapping systems,which leads to a 2–4 cm RMSE of the diameter at the breast height estimates,and a 4–7 cm RMSE of the stem curve estimates.Conclusions:Results of the experiment suggested that the integrated flight is capable of combining the high completeness of upper canopies from the above-canopy perspective and the high completeness of stems from the terrestrial perspective.Thus,it is a solution to combine the advantages of the terrestrial static,the mobile,and the above-canopy UAV observations,which is a promising step forward to achieve a fully autonomous in situ forest inventory.Future studies should be aimed to further improve the platform positioning,and to automatize the UAV operation.

Automatic area estimation of algal blooms in water bodies from UAV images using texture analysis

Algal blooms,the spread of algae on the surface of water bodies,have adverse effects not only on aquatic ecosystems but also on human life.The adverse effects of harmful algal blooms(HABs)necessitate a convenient solution for detection and monitoring.Unmanned aerial vehicles(UAVs)have recently emerged as a tool for algal bloom detection,efficiently providing on-demand images at high spatiotemporal resolutions.This study developed an image processing method for algal bloom area estimation from the aerial images(obtained from the internet)captured using UAVs.As a remote sensing method of HAB detection,analysis,and monitoring,a combination of histogram and texture analyses was used to efficiently estimate the area of HABs.Statistical features like entropy(using the Kullback-Leibler method)were emphasized with the aid of a gray-level co-occurrence matrix.The results showed that the orthogonal images demonstrated fewer errors,and the morphological filter best detected algal blooms in real time,with a precision of 80%.This study provided efficient image processing approaches using on-board UAVs for HAB monitoring.

Machine learning algorithm partially reconfigured on FPGA for an image edge detection system

Unmanned aerial vehicles(UAVs)have been widely used in military,medical,wireless communications,aerial surveillance,etc.One key topic involving UAVs is pose estimation in autonomous navigation.A standard procedure for this process is to combine inertial navigation system sensor information with the global navigation satellite system(GNSS)signal.However,some factors can interfere with the GNSS signal,such as ionospheric scintillation,jamming,or spoofing.One alternative method to avoid using the GNSS signal is to apply an image processing approach by matching UAV images with georeferenced images.But a high effort is required for image edge extraction.Here a support vector regression(SVR)model is proposed to reduce this computational load and processing time.The dynamic partial reconfiguration(DPR)of part of the SVR datapath is implemented to accelerate the process,reduce the area,and analyze its granularity by increasing the grain size of the reconfigurable region.Results show that the implementation in hardware is 68 times faster than that in software.This architecture with DPR also facilitates the low power consumption of 4 mW,leading to a reduction of 57%than that without DPR.This is also the lowest power consumption in current machine learning hardware implementations.Besides,the circuitry area is 41 times smaller.SVR with Gaussian kernel shows a success rate of 99.18%and minimum square error of 0.0146 for testing with the planning trajectory.This system is useful for adaptive applications where the user/designer can modify/reconfigure the hardware layout during its application,thus contributing to lower power consumption,smaller hardware area,and shorter execution time.

Sensing and Communication Integrated Fast Neighbor Discovery for UAV Networks

In unmanned aerial vehicle(UAV)networks,the high mobility of nodes leads to frequent changes in network topology,which brings challenges to the neighbor discovery(ND)for UAV networks.Integrated sensing and communication(ISAC),as an emerging technology in 6G mobile networks,has shown great potential in improving communication performance with the assistance of sensing information.ISAC obtains the prior information about node distribution,reducing the ND time.However,the prior information obtained through ISAC may be imperfect.Hence,an ND algorithm based on reinforcement learning is proposed.The learning automaton(LA)is applied to interact with the environment and continuously adjust the probability of selecting beams to accelerate the convergence speed of ND algorithms.Besides,an efficient ND algorithm in the neighbor maintenance phase is designed,which applies the Kalman filter to predict node movement.Simulation results show that the LA-based ND algorithm reduces the ND time by up to 32%compared with the Scan-Based Algorithm(SBA),which proves the efficiency of the proposed ND algorithms.

3D modeling of Unmanned Aerial Vehicles Tilt Photogrammetry

Unmanned Aerial Vehicles(UAV)tilt photogrammetry technology can quickly acquire image data in a short time.This technology has been widely used in all walks of life with the rapid development in recent years especially in the rapid acquisition of high-resolution remote sensing images,because of its advantages of high efficiency,reliability,low cost and high precision.Fully using the UAV tilt photogrammetry technology,the construction image progress can be observed by stages,and the construction site can be reasonably and optimally arranged through three-dimensional modeling to create a civilized,safe and tidy construction environment.

Monitoring coal fires in Datong coalfield using multi-source remote sensing data

Numerous coal fires burn underneath the Datong coalfield because of indiscriminate mining.Landsat TM/ETM,unmanned aerial vehicle（UAV）,and infrared thermal imager were employed to monitor underground coal fires in the Majiliang mining area.The thermal field distributions of this area in 2000,2002,2006,2007,and 2009 were obtained using Landsat TM/ETM.The changes in the distribution were then analyzed to approximate the locations of the coal fires.Through UAV imagery employed at a very high resolution（0.2 m）,the texture information,linear features,and brightness of the ground fissures in the coal fire area were determined.All these data were combined to build a knowledge model of determining fissures and were used to support underground coal fire detection.An infrared thermal imager was used to map the thermal field distribution of areas where coal fire is serious.Results were analyzed to identify the hot spot trend and the depth of the burning point.

Online Multi-Object Tracking Under Moving Unmanned Aerial Vehicle Platform Based on Object Detection and Feature Extraction Network

In order to solve the problem of small object size and low detection accuracy under the unmanned aerial vehicle(UAV)platform,the object detection algorithm based on deep aggregation network and high-resolution fusion module is studied.Furthermore,a joint network of object detection and feature extraction is studied to construct a real-time multi-object tracking algorithm.For the problem of object association failure caused by UAV movement,image registration is applied to multi-object tracking and a camera motion discrimination model is proposed to improve the speed of the multi-object tracking algorithm.The simulation results show that the algorithm proposed in this study can improve the accuracy of multi-object tracking under the UAV platform,and effectively solve the problem of association failure caused by UAV movement.

Geometrical feature analysis and disaster assessment of the Xinmo landslide based on remote sensing data

At 5:39 am on June 24, 2017, a landslide occurred in the village of Xinmo in Maoxian County, Aba Tibet and Qiang Autonomous Prefecture(Sichuan Province, Southwest China). On June 25, aerial images were acquired from an unmanned aerial vehicle(UAV), and a digital elevation model(DEM) was processed. Landslide geometrical features were then analyzed. These are the front and rear edge elevation, accumulation area and horizontal sliding distance. Then, the volume and the spatial distribution of the thickness of the deposit were calculated from the difference between the DEM available before the landslide, and the UAV-derived DEM collected after the landslide. Also, the disaster was assessed using high-resolution satellite images acquired before the landslide. These include Quick Bird, Pleiades-1 and GF-2 images with spatial resolutions of 0.65 m, 0.70 m, and 0.80 m, respectively, and the aerial images acquired from the UAV after the landslide with a spatial resolution of 0.1 m. According to the analysis, the area of the landslide was 1.62 km2, and the volume of the landslide was 7.70 ± 1.46 million m3. The average thickness of the landslide accumulation was approximately 8 m. The landslide destroyed a total of 103 buildings. The area of destroyed farmlands was 2.53 ha, and the orchard area was reduced by 28.67 ha. A 2-km section of Songpinggou River was blocked and a 2.1-km section of township road No. 104 was buried. Constrained by the terrain conditions, densely populated and more economically developed areas in the upper reaches of the Minjiang River basin are mainly located in the bottom of the valleys. This is a dangerous area regarding landslide, debris flow and flash flood events Therefore, in mountainous, high-risk disaster areas, it is important to carefully select residential sites to avoid a large number of casualties.

Adaptive cropping shallow attention network for defect detection of bridge girder steel using unmanned aerial vehicle images

Bridges are an important part of railway infrastructure and need regular inspection and maintenance.Using unmanned aerial vehicle(UAV)technology to inspect railway infrastructure is an active research issue.However,due to the large size of UAV images,flight distance,and height changes,the object scale changes dramatically.At the same time,the elements of interest in railway bridges,such as bolts and corrosion,are small and dense objects,and the sample data set is seriously unbalanced,posing great challenges to the accurate detection of defects.In this paper,an adaptive cropping shallow attention network(ACSANet)is proposed,which includes an adaptive cropping strategy for large UAV images and a shallow attention network for small object detection in limited samples.To enhance the accuracy and generalization of the model,the shallow attention network model integrates a coordinate attention(CA)mechanism module and an alpha intersection over union(α-IOU)loss function,and then carries out defect detection on the bolts,steel surfaces,and railings of railway bridges.The test results show that the ACSANet model outperforms the YOLOv5s model using adaptive cropping strategy in terms of the total mAP(an evaluation index)and missing bolt mAP by 5%and 30%,respectively.Also,compared with the YOLOv5s model that adopts the common cropping strategy,the total mAP and missing bolt mAP are improved by 10%and 60%,respectively.Compared with the YOLOv5s model without any cropping strategy,the total mAP and missing bolt mAP are improved by 40%and 67%,respectively.

Intelligent Deep Data Analytics Based Remote Sensing Scene Classification Model

Latest advancements in the integration of camera sensors paves a way for newUnmannedAerialVehicles(UAVs)applications such as analyzing geographical(spatial)variations of earth science in mitigating harmful environmental impacts and climate change.UAVs have achieved significant attention as a remote sensing environment,which captures high-resolution images from different scenes such as land,forest fire,flooding threats,road collision,landslides,and so on to enhance data analysis and decision making.Dynamic scene classification has attracted much attention in the examination of earth data captured by UAVs.This paper proposes a new multi-modal fusion based earth data classification(MMF-EDC)model.The MMF-EDC technique aims to identify the patterns that exist in the earth data and classifies them into appropriate class labels.The MMF-EDC technique involves a fusion of histogram of gradients(HOG),local binary patterns(LBP),and residual network(ResNet)models.This fusion process integrates many feature vectors and an entropy based fusion process is carried out to enhance the classification performance.In addition,the quantum artificial flora optimization(QAFO)algorithm is applied as a hyperparameter optimization technique.The AFO algorithm is inspired by the reproduction and the migration of flora helps to decide the optimal parameters of the ResNet model namely learning rate,number of hidden layers,and their number of neurons.Besides,Variational Autoencoder(VAE)based classification model is applied to assign appropriate class labels for a useful set of feature vectors.The proposedMMF-EDCmodel has been tested using UCM and WHU-RS datasets.The proposed MMFEDC model attains exhibits promising classification results on the applied remote sensing images with the accuracy of 0.989 and 0.994 on the test UCM and WHU-RS dataset respectively.

Seasonal Vegetation Changes in the Malinda Wetland Using Bi-Temporal, Multi-Sensor, Very High Resolution Remote Sensing Data Sets

Small wetlands in East Africa have grown in prominence driven by the unreliable and diminished rains and the increasing population pressure. Due to their size (less than 500 Ha), these wetlands have not been studied extensively using satellite remote sensing approaches. High spatial resolution remote sensing approaches overcome this limitation allowing detailed inventorying and research on such small wetlands. For understanding the seasonal variations in land cover within the Malinda Wetland in Tanzania (350 Ha), two periods were considered, May 2012 coinciding with the wet period (rainy season) and August 2012 coinciding with a fairly rain depressed period (substantially dry but generally cooler season). The wetland was studied using very high spatial resolution orthophotos derived from Unmanned Aerial Vehicle (UAV) photography fused with TerraSAR-X Spotlight mode dual polarized radar data. Using these fused datasets, five main classes were identified that were used to firstly delineate seasonal changes in land use activities and secondly used in determining phenology changes. Combining fuzzy maximum likelihood classification, knowledge classifier and Change Vector Analysis (CVA), land cover classification was undertaken for both seasons. From the results, manifold anthropogenic activities are taking place between the seasons as evidenced by the high conversion rates (63.01 Ha). The phenological change was also highest within the human influence class due to the growing process of cropped land (26.60 Ha). Much of the changes in both cover and phenology are occurring in the mid upper portion of the wetland, attributed to the presence of springs in this portion of the wetland along the banks of River Mkomazi. There is thus seasonality in the observed anthropogenic influence between the wetland and its periphery.