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.

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.

Relationship between Vegetation Index and Forest Surface Fuel Load in UAV Multispectral Remote Sensing

[Objectives]To explore the relationship between vegetation index and forest surface fuel load.[Methods]UAV multispectral remote sensing was used to obtain large-scale forest images and obtain structural data of forest surface fuel load.This experimental area was located in Gaoming District,Foshan City,Guangdong Province.The average surface fuel load of the experimental area was as high as 39.33 t/ha,and the forest surface fuel load of Pinus elliottii was the highest.[Results]The normalized difference vegetation index(NDVI)and enhanced vegetation index(EVI)had a moderately strong correlation with the forest surface fuel load.The regression model of NDVI(X)and forest surface fuel load(Y)was established:Y=-5.9354X+8.4663,and the regression model of EVI(X)and forest surface fuel load(Y)was established:Y=-5.8485X+6.7271.The study also found that the linear relationship between NDVI and surface fuel load was more significant.[Conclusions]Both NDVI and EVI have moderately strong correlations with forest surface fuel load.NDVI is moderately or strongly correlated with the surface fuel load of Pinus massoniana forest,shrub grassland,broad-leaf forest and bamboo forest,while EVI is only strongly correlated with surface fuel load of broad-leaf forest and bamboo forest.It is expected that the relationship between other vegetation indices and forest surface fuel load can be obtained by the method in this study,so as to find a more universal vegetation index for calculating surface fuel load.

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.

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.

Optimization of the Internet of Remote Things Data Acquisition Based on Satellite UAV Integrated Network

In order to achieve dependable and efficient data acquisition and transmission in the Internet of Remote Things(IoRT),we investigate the optimization scheme of IoRT data acquisition under the unmanned aerial vehicle(UAV)-low earth orbit(LEO)satellite integrated space-air-ground network,in which the UAV acquires data from massive Internet of Things(IoT)devices in special scenarios.To combine with the actual scenario,we consider two different data types,that is,delay-sensitive data and delay-tolerant data,the transmission mode is accordingly divided into two types.For delay-sensitive data,the data will be transmitted via the LEO satellite relay to the data center(DC)in real-time.For delay-tolerant data,the UAV will store and carry the data until the acquisition is completed,and then return to DC.Due to nonconvexity and complexity of the formulated problem,a multi-dimensional optimization Rate Demand based Joint Optimization(RDJO)algorithm is proposed.The algorithm first uses successive convex approximation(SCA)technology to solve the non-convexity,and then based on the block coordinate descent(BCD)method,the data acquisition efficiency is maximized by jointly optimizing UAV deployment,the bandwidth allocation of IoRT devices,and the transmission power of the UAV.Finally,the proposed RDJO algorithm is compared with the conventional algorithms.Simulation consequences demonstrate that the efficiency of IoRT data acquisition can be greatly improved by multi-parameter optimization of the bandwidth allocation,UAV deployment and the transmission power.

Spectrum and energy efficient multi-antenna spectrum sensing for green UAV communication

Unmanned Aerial Vehicle(UAV)communication is a promising technology that provides swift and flexible ondemand wireless connectivity for devices without infrastructure support.With recent developments in UAVs,spectrum and energy efficient green UAV communication has become crucial.To deal with this issue,Spectrum Sharing Policy(SSP)is introduced to support green UAV communication.Spectrum sensing in SSP must be carefully formulated to control interference to the primary users and ground communications.In this paper,we propose spectrum sensing for opportunistic spectrum access in green UAV communication to improve the spectrum utilization efficiency.Different from most existing works,we focus on the problem of spectrum sensing of randomly arriving primary signals in the presence of non-Gaussian noise/interference.We propose a novel and improved p-norm-based spectrum sensing scheme to improve the spectrum utilization efficiency in green UAV communication.Firstly,we construct the p-norm decision statistic based on the assumption that the random arrivals of signals follow a Poisson process.Then,we analyze and derive the approximate analytical expressions of the false-alarm and detection probabilities by utilizing the central limit theorem.Simulation results illustrate the validity and superiority of the proposed scheme when the primary signals are corrupted by additive non-Gaussian noise and arrive randomly during spectrum sensing in the green UAV communication.

基于时间序列植被指数的小麦条锈病抗性等级鉴定方法

条锈病严重影响小麦产量,培育抗条锈病的小麦品种至关重要。针对传统育种中抗性鉴定手段单一、效率低的问题,该研究提出了一种通过小麦冠层植被指数的时间序列实现对条锈病不同抗性等级的高效鉴定方法。该方法利用无人机采集自然发病的育种群体小麦(共600个样本,516个基因型)冠层多时相的光谱图像,使用随机蛙跳算法和ReliefF算法筛选出6个条锈病病害严重度的敏感特征:归一化色素叶绿素指数(normalized pigment chlorophyll index,NPCI)、沃尔贝克指数(woebbecke index,WI)、叶绿素红边指数(chlorophyll index rededge,CIrededge)、绿大气抵抗植被指数(green atmospherically resistant index,GARI)、归一化差分植被指数(normalized difference vi,NDVI)、叶绿素绿指数(chlorophyll index green,CIgreen),这些敏感特征在试验群体中的时间序列符合条锈病的发病规律,验证了其作为条锈病发病严重度敏感特征的有效性;基于支持向量机(support vector machine,SVM)算法使用上述敏感特征建立条锈病病害严重度等级分类模型,在测试集的表现中,与使用未经过筛选的原始特征所建立的模型相比在精度、平均准确率、平均召回率和F1分数上分别仅下降6.2%、3.3%、2.7%、4.0%,证明了所筛选敏感特征的有效性;针对一般机器学习算法难以捕捉不同抗性等级样本之间较小的特征变化差异的问题,提出了一种从植被指数时间序列转化生成的二维图像中提取特征实现条锈病抗性等级分类的方法。将敏感特征中能够较好区分不同抗病等级的4个时间序列植被指数(NPCI、GARI、NDVI、WI),通过格拉姆角场方法生成格拉姆角和场图像,并制作成数据集,使用DenseNet121网络进行训练,以实现不同条锈病抗病等级的分类。建立的条锈病抗性等级分类模型中,由NPCI时间序列图像建立的分类模型测试效果最佳,其准确率为0.837,召回率为0.834,F1分数可达0.833,能够较好地实现对群体小麦不同品种(系)的条锈病抗性等级差异的区分,表明基于光谱植被指数时间序列的小麦条锈病抗性等级识别方法可以用于小麦抗病育种中抗性等级的鉴定,并可为其他作物的病害抗性等级鉴定提供一定的参考。

基于通感融合的无人机预编码及飞行轨迹设计

无人机(UAVs)具有机动性强,低成本及易部署等特性,通过搭载通信及感知设备,支持通信与感知技术的高效资源共享,无人机可作为融合通信与传感技术的高性能空中平台。该文针对多输入多输出(MIMO)无人机使能的联合通信、感知场景,综合考虑无人机飞行能量、多天线传输及用户业务需求等限制条件,建模无人机通信、感知预编码及飞行轨迹联合优化问题为多目标优化问题,以实现通信用户最低速率最大化及目标最小发现概率最大化。由于通信用户最低速率最大化问题为非凸优化问题,难以直接求解,将原优化问题分解为通信预编码设计子问题及无人机轨迹设计子问题,并采用交替迭代法依次求解两个子问题直至算法收敛,其中,对于通信预编码设计子问题,提出一种基于迫零(ZF)算法的求解策略;对于无人机轨迹设计子问题,提出一种基于连续凸逼近(SCA)算法的求解策略。基于所得到的无人机最优轨迹,将无人机感知位置选择问题建模为加权距离和最小化问题,进而应用泛搜索算法优化确定目标感知位置,并设计基于ZF算法的通信感知预编码联合优化策略,以实现通信感知性能的联合优化。最后通过仿真验证了该文所提算法的有效性。

Launching an unmanned aerial vehicle remote sensing data carrier:concept,key components and prospects

Unmanned aerial vehicles(UAV)based remote sensing is an emerging and important data source.Recently,the use of UAVs for remote sensing applications has been rapidly growing owing to their greater availability and the miniaturization of sensors.UAVs are surpassing satellites and aircraft in remote sensing data supply for many local requirements.In comparison with satellite remote sensing data,most UAV remote sensing data is characterized by high resolution,small coverage area,and heterogeneous multi-sources.However,UAVs lack a unified space–time framework and standardized data process.This paper describes a UAV remote sensing data carrier that can be used as an e-commerce platform for data sharing among registered members and a mission planner for new data acquisition.To the best of our knowledge,the data carriers described herein,are the first of their kind.Through seamless docking with UAVs,the data carrier will form a national UAV network,capable of dynamically obtaining very-high-resolution UAV remote sensing images.In practice,a pilot retrieval system of UAV meta data has been developed to provide a catalogue of data product services.

基于无人机多光谱遥感的苹果树冠层SPAD反演

为探讨利用无人机多光谱遥感影像监测苹果树冠层叶绿素含量的可行性,以南疆矮砧密植苹果树为研究对象,利用无人机获取试验区多光谱影像,选取10个植被指数,分析所选植被指数与实测SPAD值的相关性,将与SPAD相关性较好的7个植被指数作为模型的输入变量,利用机器学习构建一元线性回归、偏最小二乘回归、支持向量机回归、随机森林回归和岭回归的苹果树冠层SPAD反演模型,通过精度检验确定最优模型.结果表明,7个植被指数NDVI,EVI,SAVI,OSAVI,GNDVI,RVI,GRVI与SPAD具有较好的相关性,相关系数为0.4~0.7,均在P小于0.01水平上极显著相关.采用随机森林回归建立的模型表现最优,其建模集R 2为0.728,RMSE为2.292,RPD为1.920;验证集R 2为0.702,RMSE为2.527,RPD为1.832.因此,基于无人机多光谱遥感的RF模型可以实现苹果树冠层SPAD的快速准确估算.

Comparison of leaf area index inversion for grassland vegetation through remotely sensed spectra by unmanned aerial vehicle and field-based spectroradiometer

Aims Remote sensing technology has been proved useful in mapping grass-land vegetation properties.Spectral features of vegetation cover can be recorded by optical sensors on board of different platforms.With increas-ing popularity of applying unmanned aerial vehicle(UAV)to mapping plant cover,the study aims to investigate the possible applications and potential issues related to mapping leaf area index(LAI)through integra-tion of remote sensing imagery collected by multiple sensors.Methods This paper applied the collected spectral data through field-based(FLD)and UAV-borne spectroradiometer to map LAI in a Sino-German experiment pasture located in the Xilingol grassland,Inner Mongolia,China.Spectroradiometers on FLD and UAV platforms were taken to measure spectral reflectance related to the targeted vegetation proper-ties.Based on eight vegetation indices(VIs)computed from the col-lected hyperspectral data,regression models were used to inverse LAI.The spectral responses between FLD and UAV platforms were com-pared,and the regression models relating LAI with VIs from FLD and UAV were established.The modeled LAIs by UAV and FLD platforms were analyzed in order to evaluate the feasibility of potential integra-tion of spectra data for mapping vegetation from the two platforms.Important Findings Results indicated that the spectral reflectance between FLD and UAV showed critical gaps in the green and near-infrared regions of the spec-trum over densely vegetated areas,while the gaps were small over sparsely vegetated areas.The VI values from FLD spectra were greater than their UAV-based counterparts.Out of all the VIs,broadband gen-eralized soil-adjusted vegetation index(GESAVI)and narrow-band nNDVI2 were found to achieve the best results in terms of the accuracy of the inversed LAIs for both FLD and UAV platforms.We conclude that GESAVI and nNDVI2 are the two promising VIs for both platforms and thus preferred for LAI inversion to carry spectra integration of the two platforms.We suggest that accuracy on the LAI inversion could be improved by applying more advanced functions(e.g.non-linear)con-sidering the observed bias for the difference between the UAV-and FLD-inversed LAIs,especially when LAI was low.

基于高分辨率卫星和无人机的广西滨海盐沼面积变化监测

滨海盐沼作为重要的海岸带生态系统,在海岸保护、生物多样性维持、固碳减污等方面发挥了重要的生态服务功能。及时准确地监测滨海盐沼分布情况和动态变化,对于科学地管理和保护本地滨海盐沼生态系统意义重大。本研究基于2019年和2021年多源国产高空间分辨率卫星数据,结合无人机自主性强、灵活机动、不受云遮挡影响的优势,对广西壮族自治区滨海盐沼开展遥感跟踪监测。研究结果表明,广西2021年滨海盐沼总面积为1 341.40 hm2,其中,北海市、防城港市和钦州市3个海滨城市的滨海盐沼面积分别为1 247.82 hm2、49.73 hm2和43.85 hm2。与2019年相比,广西2021年滨海盐沼总面积减少108.96 hm2,其中,北海市互花米草(Spartina alterniflora)面积减少107.05 hm2,钦州市短叶茳芏(Cyperus malaccensis)和芦苇(Phragmites australis)面积减少1.91 hm2,防城港市滨海盐沼面积不变。广西当地对入侵种互花米草的治理卓有成效,互花米草大范围减少,但局部区域的互花米草分布仍呈不断增长的趋势,仍需重视对互花米草的监测与防控工作。

基于无人机多光谱影像的云南松林蓄积量估测模型

【目的】无人机多光谱遥感影像较可见光影像具有更丰富的光谱信息,在森林蓄积量估测中具有较大潜力。以无人机载多光谱遥感影像为主要数据源,探索森林蓄积量的遥感估测模型,以克服传统地面调查工作量大、耗时长、成本高等弊端。【方法】以滇中地区典型天然云南松Pinusyunnanensis纯林为研究对象,利用无人机多光谱影像提取单波段反射率、各类植被指数、纹理特征等,计算各特征变量的标准地均值;筛选与云南松林蓄积量相关性显著的特征变量,采用多元线性、随机森林、支持向量机建立云南松林蓄积量估测模型,以决定系数(R^(2))、平均绝对误差(E_(MA))、均方根误差(E_(RMS))、平均相对误差(EMR)评价模型精度。【结果】①3种模型中,随机森林的精度最高(R^(2)=0.89,E_(MA)=4.69 m^(3)·hm^(-2),E_(RMS)=5.45 m^(3)·hm^(-2),EMR=14.5%),其次为支持向量机(R^(2)=0.74,E_(MA)=5.27 m^(3)·hm^(-2),E_(RMS)=8.31 m^(3)·hm^(-2),EMR=13.1%),最低为多元线性回归模型(R^(2)=0.35,E_(MA)=10.12 m^(3)·hm^(-2),E_(RMS)=12.85 m^(3)·hm^(-2),EMR=28.1%);3种模型在测试集上的估测精度均有所降低,随机森林的模型表现最好,支持向量机次之,多元线性最差。②3种模型在云南松林蓄积量估测中均存在一定的低值高估和高值低估现象。③基于无人机多光谱影像估测云南松林蓄积量,纹理特征仍是不可忽视的重要因子。【结论】基于无人机多光谱影像,在不进行单木分割的情景下,提取标准地的单波段反射率、植被指数、纹理特征均值,筛选适用于蓄积量估算的变量构建估测模型。通过对3种模型进行精度评价,随机森林为云南松林蓄积量估测的最佳模型。

基于多光谱无人机及机器学习的林木火灾受损信息提取研究

为探究中小尺度森林火灾过火区域林木受损程度的准确提取,以2020年5月13日云南省安宁市青龙街道森林火场为研究对象,通过精灵4多光谱无人机获取火场影像,借助红边及近红外波段构建植被指数,结合纹理指标建立影像特征参数,利用机器学习中常用的随机森林(random forest,RF)和支持向量机(support vector machine,SVM)方法提取烧毁、烧死、烧伤及未伤林木空间分布信息,并探讨2种方法对于多光谱无人机遥感林木受损信息提取的精度。结果表明:不同受损程度的林木在红边波段和近红外波段范围内反射率差异较大,但以此构建的植被指数分离能力不同,呈现NDVI>mSR rededge>NDVI rededge>PSRI。基于影像光谱及纹理等多特征的林木受损程度提取方法中,RF精度明显优于SVM,总精度达89.76%,Kappa系数为0.85,相比SVM分别提升4.41%和6.25%。多光谱无人机可用于小范围典型森林火灾区域林木受损程度信息精确提取,而对于大面积范围的林木火灾受损信息的精确提取,综合多光谱无人机数据及多光谱卫星影像数据是解决问题的方向。

一种低空遮挡场景感知增强方案研究

随着低空无人机业务的稳步快速增长,低空场景的通感一体化已成为低空经济发展中必不可少的关键一环,利用现有5G基站进行低空通感一体改造也成为研究热点。对于低空覆盖感知中的遮挡问题,提出利用一种电磁可控智能面及边缘提高感知性能,并提出了性能评估框架,以分析不同的感知方式和电磁操控机制的性能。仿真验证表明,在所研究的低空场景通感一体感知任务中,电磁操控机制的加入可以有效提升无人机感知概率,同时被动感知方式优于主动感知方式,而典型空-地场景下所提出的绕射增强结构的性能表现也优于智能反射面。

基于冠层高度模型的遥感影像玉米倒伏范围提取

精准提取玉米倒伏范围是准确进行田间管理、玉米产量损失估计的基础,无人机获取遥感影像机动灵活,是作物倒伏测量的热门手段。本文提出利用无人技术基于冠层高度差的玉米倒伏范围提取方法。首先通过可见光波段差异植被指数提取玉米背景土壤分布;然后提取玉米的高度;最后基于玉米高度,通过SVM和OSTU自动阈值法提取玉米倒伏范围。试验结果表明,利用SVM法3个样本分类精度分别为88.84%、89.52%和90.80%;OSTU自动阈值法分别为94.61%、89.74%和97.20%,稍优于前者。本文基于作物高度为结构特征参数,提取作物倒伏,机理明确且一定程度上消除了无人机成像不稳定的影响。

基于无人机遥感测绘技术的土壤有机污染监测方法研究

为实时监测土壤有机污染,提出基于无人机遥感测绘技术的土壤有机污染监测方法。设计无人机遥感测绘装置,获取监测区域土壤光谱数据,应用小波变换算法去除土壤光谱数据的噪声信息,采用惩罚最小二乘法去除土壤光谱本底信息,通过LAR算法选择与提取光谱数据特征变量,衡量其与已知有机污染物质光谱特征变量的相似程度,判定监测区域土壤是否存在有机污染,并确定有机污染物质种类。实验数据显示:应用提出方法获得的监测区域土壤有机污染判定结果与实际结果保持一致,土壤有机污染物质监测因子最大值为0.98。

融合无人机多光谱和热红外影像的蔗田土壤含水率监测研究

甘蔗作为广西、云南等地的主要农作物,极易受到干旱的影响。土壤含水率是评估甘蔗是否受到干旱影响的重要指标。以蔗田土壤含水率为研究对象,利用无人机搭载的热红外和多光谱传感器数据计算出甘蔗冠层的温度、重归一化植被指数RDVI等植被指数,采用人工测定的方法对无人机监测数据进行校正和率定,构建了甘蔗的温度植被干旱指数(TVDI)模型。结果表明,利用多光谱和热红外传感器计算的TVDI与蔗田苗期、分蘖期、伸长期和成熟期土壤含水率均具有高度相关性,决定系数R2分别为0.9066、0.8190、0.8529和0.9160。因此,TVDI模型最适合用于监测甘蔗苗期和成熟期的受旱情况。