Delineation of groundwater potential zones using remote sensing and Geographic Information Systems(GIS)in Kadaladi region,Southern India

The primary objective of this research is to delineate potential groundwater recharge zones in the Kadaladi taluk of Ramanathapuram,Tamil Nadu,India,using a combination of remote sensing and Geographic Information Systems(GIS)with the Analytical Hierarchical Process(AHP).Various factors such as geology,geomorphology,soil,drainage,density,lineament density,slope,rainfall were analyzed at a specific scale.Thematic layers were evaluated for quality and relevance using Saaty's scale,and then inte-grated using the weighted linear combination technique.The weights assigned to each layer and features were standardized using AHP and the Eigen vector technique,resulting in the final groundwater potential zone map.The AHP method was used to normalize the scores following the assignment of weights to each criterion or factor based on Saaty's 9-point scale.Pair-wise matrix analysis was utilized to calculate the geometric mean and normalized weight for various parameters.The groundwater recharge potential zone map was created by mathematically overlaying the normalized weighted layers.Thematic layers indicating major elements influencing groundwater occurrence and recharge were derived from satellite images.2 Results indicate that approximately 21.8 km of the total area exhibits high potential for groundwater recharge.Groundwater recharge is viable in areas with moderate slopes,particularly in the central and southeastern regions.

Application of PCA Numalgorithm in Remote Sensing Image Processing

A numerical algorithm of principal component analysis (PCA) is proposed and its application in remote sensing image processing is introduced: (1) Multispectral image compression;(2) Multi-spectral image noise cancellation;(3) Information fusion of multi-spectral images and spot panchromatic images. The software experiments verify and evaluate the effectiveness and accuracy of the proposed algorithm.

Bayesian and Geostatistical Approaches to Combining Categorical Data Derived from Visual and Digital Processing of Remotely Sensed Images

This paper seeks a synthesis of Bayesian and geostatistical approaches to combining categorical data in the context of remote sensing classification. By experiment with aerial photographs and Landsat TM data, accuracy of spectral, spatial, and combined classification results was evaluated. It was confirmed that the incorporation of spatial information in spectral classification increases accuracy significantly. Secondly, through test with a 5-class and a 3-class classification schemes, it was revealed that setting a proper semantic framework for classification is fundamental to any endeavors of categorical mapping and the most important factor affecting accuracy. Lastly, this paper promotes non-parametric methods for both definition of class membership profiling based on band-specific histograms of image intensities and derivation of spatial probability via indicator kriging, a non-parametric geostatistical technique.

Delineation of groundwater potential zones in Wadi Saida Watershed of NW-Algeria using remote sensing,geographic information system-based AHP techniques and geostatistical analysis

Sustainable management of groundwater resources has now become an obligation,especially in arid and semi-arid regions given the socio-economic importance of this resource.The optimization in zoning for groundwater exploitation helps in planning and managing groundwater supply works such as boreholes and wells in the catchment.The objective of this study is to use remote sensing and GIS-based Analytical Hierarchy Process(AHP)techniques to evaluate the groundwater potential of Wadi Saida Watershed.Spatial analysis such as geostatistics was also used to validate results and ensure more accuracy.Through the GIS tools and remote sensing technique,earth observation data were converted into thematic layers such as lineament density,geology,drainage density,slope,land use and rainfall,which were combined to delineate groundwater potential zones.Based on their respective impact on groundwater potential,the AHP approach was adopted to assign weights on multi-influencing factors.These results will enable decision-makers to optimize hydrogeological exploration in large-scale catchment areas and map areas.According to the results,the southern part of the Wadi Saida Watershed is characterized as a higher groundwater potential area,where 32%of the total surface area falls in the excellent and good class of groundwater potential.The validation process revealed a 71%agreement between the estimated and actual yield of the existing boreholes in the study area.

Identification of groundwater potential in hard rock aquifer systems using Remote Sensing, GIS and Magnetic Survey in Veppanthattai, Perambalur, Tamilnadu

Water is an essential natural resource without which life wouldn’t exist.The study aims to identify groundwater potential areas in Vepapanthattai taluk of Perambalur district,Tamil Nadu,India,using analytic hierarchy process(AHP)model.Remote sensing and magnetic parameters have been used to determine the evaluation indicators for groundwater occurrence under the ArcGIS environment.Groundwater occurrence is linked to structural porosity and permeability over the predominantly hard rock terrain,making magnetic data more relevant for locating groundwater potential zones in the research area.NE-SW and NW-SE trending magnetic breaks derived from reduction to pole map are found to be more significant for groundwater exploration.The lineaments rose diagram indicates the general trend of the fracture to be in the NE-SW direction.Assigned normalised criteria weights acquired using the AHP model was used to reclassify the thematic layers.As a result,the taluk’s low,moderate,and high potential zones cover 25.08%,25.68%and 49.24%of the study area,respectively.The high potential zones exhibit characteristics favourable for groundwater infiltration and storage,with factors as gentle slope of<3°,high lineament densities,magnetic breaks,magnetic low zones as indicative of dykes and cracks,lithology as colluvial deposits and land surface with dense vegetation.The depth of the fracture zones was estimated using power spectrum and Euler Deconvolution method.The groundwater potential mapping results were validated using groundwater level data measured from the wells,which indicated that the groundwater potential zoning results are consistent with the data derived from the real world.

Semantic Document Layout Analysis of Handwritten Manuscripts

A document layout can be more informative than merely a document’s visual and structural appearance.Thus,document layout analysis(DLA)is considered a necessary prerequisite for advanced processing and detailed document image analysis to be further used in several applications and different objectives.This research extends the traditional approaches of DLA and introduces the concept of semantic document layout analysis(SDLA)by proposing a novel framework for semantic layout analysis and characterization of handwritten manuscripts.The proposed SDLA approach enables the derivation of implicit information and semantic characteristics,which can be effectively utilized in dozens of practical applications for various purposes,in a way bridging the semantic gap and providingmore understandable high-level document image analysis and more invariant characterization via absolute and relative labeling.This approach is validated and evaluated on a large dataset ofArabic handwrittenmanuscripts comprising complex layouts.The experimental work shows promising results in terms of accurate and effective semantic characteristic-based clustering and retrieval of handwritten manuscripts.It also indicates the expected efficacy of using the capabilities of the proposed approach in automating and facilitating many functional,reallife tasks such as effort estimation and pricing of transcription or typing of such complex manuscripts.

Novel Vegetation Mapping Through Remote Sensing Images Using Deep Meta Fusion Model

Preserving biodiversity and maintaining ecological balance is essential in current environmental conditions.It is challenging to determine vegetation using traditional map classification approaches.The primary issue in detecting vegetation pattern is that it appears with complex spatial structures and similar spectral properties.It is more demandable to determine the multiple spectral ana-lyses for improving the accuracy of vegetation mapping through remotely sensed images.The proposed framework is developed with the idea of ensembling three effective strategies to produce a robust architecture for vegetation mapping.The architecture comprises three approaches,feature-based approach,region-based approach,and texture-based approach for classifying the vegetation area.The novel Deep Meta fusion model(DMFM)is created with a unique fusion frame-work of residual stacking of convolution layers with Unique covariate features(UCF),Intensity features(IF),and Colour features(CF).The overhead issues in GPU utilization during Convolution neural network(CNN)models are reduced here with a lightweight architecture.The system considers detailing feature areas to improve classification accuracy and reduce processing time.The proposed DMFM model achieved 99%accuracy,with a maximum processing time of 130 s.The training,testing,and validation losses are degraded to a significant level that shows the performance quality with the DMFM model.The system acts as a standard analysis platform for dynamic datasets since all three different fea-tures,such as Unique covariate features(UCF),Intensity features(IF),and Colour features(CF),are considered very well.

A New Generation of Intelligent Mapping and Remote Sensing Scientific Test Satellite Luojia-301

With the continuous improvement of the performance and the increasing variety of optical mapping and remote sensing satellites,they have become an important support for obtaining global accurate surveying and mapping remote sensing information.At present,optical mapping and remote sensing satellites already have sub-meter spatial resolution capabilities,but there is a serious lag problem in mapping and remote sensing information services.It is urgent to develop intelligent mapping and remote sensing satellites to promote the transformation and upgrading to real-time intelligent services.Firstly,based on the three imaging systems of the optical mapping and remote sensing satellites and their realization methods and application characteristics,this paper analyzes the applicable system of the intelligent mapping and remote sensing satellites.Further,according to the application requirements of real-time,intelligence,and popularization,puts forward the design concept of integrated intelligent remote sensing satellite integrating communication,navigation,and remote sensing and focuses on the service mode and integrated function composition of intelligent remote sensing satellite.Then expounds on the performance and characteristics of the Luojia-301 satellite,a new generation of intelligent surveying and mapping remote sensing scientific test satellite.And finally summarizes and prospects the development and mission of intelligent mapping remote sensing satellites.Luojia-301 satellite integrates remote sensing and communication functions.It explores an efficient and intelligent service mode of mapping and remote sensing information from data acquisition to the application terminal and provides a real service verification platform for on-orbit processing and real-time transmission of remote sensing data based on space-ground internet,which is of great significance to the construction of China’s spatial information network.

无人机多光谱影像的小麦倒伏信息多特征融合检测研究

为探究多特征融合方法在作物倒伏领域快速精准识别中的适用性,利用无人机获取多田块冠层尺度的不同倒伏率麦田多光谱数据,对原始倒伏图像进行图像拼接、辐射校正、几何校正等预处理,并利用重归一化差值植被指数和阴影指数分别剔除土壤和阴影背景,提取小麦倒伏DSM模型和植被指数分别与多光谱图像进行多特征图像主成分变换融合,筛选差异性较大的纹理特征,采用支持向量机(SVM)、人工神经网络(ANN)和最大似然法(MLC)监督分类模型对多光谱和DSM融合图像、多光谱和归一化植被指数(NDVI)融合图像、多光谱图像和纹理特征图像进行监督分类,并采用总体精度(OA)、 Kappa系数和提取误差综合评价各监督模型的分类性能和倒伏提取精度。分类结果表明:各监督分类方法在不同倒伏区域提取结果建模效果趋势一致,SVM和ANN整体提取精度高于MLC,在高倒伏区域,多光谱与NDVI融合图像的SVM监督模型(OA:92.63%, Kappa系数:0.85,提取误差:1.11%)提取效果最好;在中倒伏区域,多光谱与DSM融合图像的SVM监督模型(OA:90.35%, Kappa系数:0.79,提取误差:9.34%)提取效果最好;在低倒伏区域,均值纹理特征图像的ANN监督模型(OA:91.05%, Kappa系数:0.82,提取误差:8.20%)提取结果较好。本研究将DSM模型、植被指数、纹理特征与多光谱图像进行融合对比,并对多特征融合方法能否高精度有效提取小麦倒伏信息进行了探究,结果表明无人机多光谱遥感结合特征融合技术能有效提取小麦倒伏面积,提取效果优于单特征小麦倒伏图像。本研究结果可为助力小麦倒伏灾情调查数据的精确获取方法提供参考。

空地一体遥感技术在轨道交通安全保护区巡查中的应用

随着城市轨道交通建设的不断发展,轨道交通沿线安全保护区巡查工作的范围越来越大,内容越来越多,区域内的情况越来越复杂。针对存在的问题,本文构建了一套空地一体的遥感综合监测体系,在巡查工作中引入航空摄影测量、低空无人机监测、车载全景扫描、定点实时监控等多种遥感监测技术及DSM差分计算、深度学习智能识别等多种数据处理技术,并以上海为例开展应用实践。结果表明,本文方法具有可行性,对建立全覆盖、高频次、智能化的巡查模式有参考借鉴价值。

含明亮区域的无人机遥感定位图像去雾方法

针对传统DCP去雾算法处理无人机遥感定位含雾图像时,天空或白色等明亮区域颜色易发生失真,图像整体对比度降低等问题,提出了一种自适应阈值分割的DCP去雾方法。利用灰度图像I_(gray)(x)求取图像明亮与非明亮区域的自适应阈值ThrB;根据自适应阈值ThrB将明亮区与非明亮区分割,并设计自适应修正函数M;优化由暗通道图像生成的大气耗散函数粗估计,利用双边滤波再次细化透射率,完成图像去雾复原。实验结果表明:提出方法在处理天空或反光较强的明亮区域时,能够有效避免复原后的颜色失真等问题,进一步改善遥感图像地面景物区域的处理效果,复原后整幅遥感图像的色彩饱和度和对比度明显提高,主观视觉效果有一定改善,且PSNR、FC、SSIM和CR等客观参数均有提升,有利于后续遥感定位图像分析。

各态历经性的案例教学法研究

平稳随机过程的各态历经性是工程应用中非常重要的概念。在随机过程理论授课时,需要学生转换思维去理解各态历经性意义的同时,也要求学生能够熟练掌握各态历经性的物理含义。用传统教学方式和公式列举的推导方式教学,对于学生学习难度较大。该文将各态历经性与工程应用案例相结合,通过从掷骰子的实验结果引入对各态历经性概念的理解,介绍各态历经性的判定方法与物理意义,并借由航天微波遥感中噪声抑制这一工程应用案例,进一步引发学生对各态历经性应用的思考,充分理解各态历经性的实际工程作用;最后根据各态历经性的物理意义,引申到科研生活体悟的人生哲理,让学生学习新概念的同时,在自身思想上也能有所提升。

农田环境下无人机图像并行拼接识别算法

为改善在农田环境下无人机图像计算速度和效率,该研究提出了一种农田环境下无人机图像并行拼接识别算法。利用倒二叉树并行拼接识别算法,通过提取图像拼接中的变换矩阵,实现拼接识别同时进行。根据边缘设备的CPU核心数和图像数量自动将图像拼接识别任务划分为多个子进程,并分配到不同核心上执行,以提高在农田环境下的计算效率。试验结果表明:相同试验环境和数据集条件下,倒二叉树并行拼接算法的拼接耗时相较于其他算法平均减少了60%~90%左右;在农田环境下,倒二叉树并行拼接识别相较于串行拼接识别的耗时减少了70%,图像识别的平均像素交并比提升了10.17个百分点,说明在农田环境下采用多线程倒二叉树并行算法可以更好地利用农田环境下边缘设备的计算资源,大幅提升无人机图像的拼接和识别的速度,为无人机的快速实时监测提供技术支撑。

“任务链+研学支架”教学模式的建构——新工科视角下“遥感数字图像处理”课教学改革

目前,传统教学在内容的系统性、认知活动高阶性、价值引领实效性、促学效果等方面存在着较严重的问题,为此,“遥感数字图像处理”课程基于新工科教学理念,以遥感技术的综合应用情境重构课程体系,开启“任务链+研学支架”教学模式,加强培养学生遥感应用能力和创新能力,以遥感技术服务社会需求为育人路径。新模式的构建,实现了应用与研学互动、研学与研教并举、育人与育才同构的效果,对同类型新工科课程具有借鉴意义。

基于残差密集块的激光遥感图像中目标检测方法

为了提高对目标检测的效果,提出基于残差密集块的激光遥感图像中目标检测方法。首先,设计基于残差密集块的卷积神经网络,在设计ReLU激活函数并完成网络训练后,基于含噪激光遥感图像的初步特征提取结果,利用单个卷积展开卷积映射处理,抽取出潜在干净图像。然后,通过聚类处理的方式,得到激光遥感图像中车辆目标的显著图,再利用大律法,通过建立的特征比例关系的方式检测出其中的目标信息。实验结果表明,应用该方法有效滤除激光遥感图像中的噪声,并精准检测出激光遥感图像中的车辆目标。相比于3种传统方法,该方法检测结果均值误差的最小值仅为0.0156,说明该方法有效实现了设计预期。

镇村布局规划实施效果评价方法研究

开展镇村布局规划实施效果评价,可以为决策优化和方案调整提供科学依据。本文结合高分辨率遥感影像与国土三调数据,通过面向对象变化检测方法,提取集聚提升类、城郊融合类、特色保护类、拆迁撤并类村庄的建设用地变化图斑,在两期建设用地图斑的基础上,构建建设用地变化量与紧凑度指标,开展村庄建设进程和村庄形态的分析评价,并以盱眙县穆店镇为例,对2019—2022年近期规划的四类村庄进行方法验证,为不同村庄的发展提供适宜的规划建议。

遥感专业《数字图像处理》教材建设有效途径分析

数字图像处理教材存在理多实少、难点解析不足等种种问题。针对这些问题,提出一种混合式立体改进方法,从内容、形式和实践方面提出了多项举措,以实现够用学会、讲练透彻的学习目标。通过分析遥感专业课程内容,提炼出相关知识点进行教材编写,得到理实一体的简明化教材。教学实践证明,这种教材建设方法丰富了教学资源,有利于学生对重难点知识理解,大幅提高学习效率和质量,不失为一种有效的逆向教材建设创新方法。

基于通感一体化技术的远距离感知与高精度测角系统开发

该文基于通感一体化技术,致力于开发一套远距离感知与高精度测角系统。通过深入研究通感一体化技术,以及感知信号处理算法包括距离估计、CFAR检测和角度估计等关键技术,实现对目标的准确感知和测角。系统的设计与硬件实现考虑感知系统架构、硬件配置与选型等方面,旨在提供高效、可靠的系统性能。实验结果表明,该系统在远距离感知和测角方面取得显著成果,为相关领域的研究和应用提供有力支持。

基于DFECANet的遥感图像飞机目标检测方法

针对现有的遥感图像目标检测方法中对小尺寸飞机目标的检测精度不高、特征信息传递不准确、信息交互不充分等问题,提出了一种基于可辨别特征提取和上下文感知的遥感图像飞机目标检测方法。设计了以可辨别特征提取模块为主体的主干网络,用以加强对多尺度飞机目标的特征提取;引入自适应特征增强模块,选择性关注小目标、优化特征信息的传递与信息交互;并设计了特征融合上采样模块对特征图进行上采样操作,用以提升高层语义信息的准确性。在DOTAv1数据集上的检测精度达到了95.2%,相较于YOLOv5s、SCRDet、ASSD等主流算法,飞机目标的检测精度提高了3.7%~18%。此外,该方法的检测速度以及模型参数量分别为147 fps和13.4 M,相较于当前主流算法具备较强的竞争力,满足在遥感背景下对飞机目标的实时检测需求。

利用遥感技术对湿地变化进行绿色金融分析

利用卫星遥感技术对哈尔滨市群力外滩生态湿地进行了信息提取和分析,评估了该湿地近几年水体和绿地面积的变化情况,探讨了开展绿色金融对该湿地生态保护发展的影响。通过目视解译与实地考察获取训练样本和验证点,采用最小距离法(Minimum distance,MD)、最大似然法(Maximum likelihood,ML)和支持向量机(Support vector machine,SVM)进行监督分类并对比其精度,选择更适合北方湿地信息提取的支持向量机的方法进行湿地分类。结合该湿地的生态特征,分析了绿色金融对该湿地的发展潜力和影响因素,并提出了具体的绿色金融策略和建议。研究结果表明,群力外滩生态湿地的水体和绿地面积在近几年稳步增长,说明该湿地保护和修复工作效果显著。