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.

Scale Issues of Wetland Classification and Mapping Using Remote Sensing Images: A Case of Honghe National Nature Reserve in Sanjiang Plain, Northeast China

Wetland research has become a hot spot linking multiple disciplines presently. Wetland classification and mapping is the basis for wetland research. It is difficult to generate wetland data sets using traditional methods because of the low accessibility of wetlands, hence remote sensing data have become one of the primary data sources in wetland research. This paper presents a case study conducted at the core area of Honghe National Nature Reserve in the Sanjiang Plain, Northeast China. In this study, three images generated by airship, from Thematic Mapper and from SPOT 5 were selected to produce wetland maps at three different wetland landscape levels. After assessing classification accuracies of the three maps, we compared the different wetland mapping results of 11 plant communities to the airship image, 6 plant ecotypes to the TM image and 9 landscape classifications to the SPOT 5 image. We discussed the different characteristics of the hierarchical ecosystem classifications based on the spatial scales of the different images. The results indicate that spatial scales of remote sensing data have an important link to the hierarchies of wetland plant ecosystems displayed on the wetland landscape maps. The richness of wetland landscape information derived from an image closely relates to its spatial resolution. This study can enrich the ecological classification methods and mapping techniques dealing with the spatial scales of different remote sensing images. With a better understanding of classification accuracies in mapping wetlands by using different scales of remote sensing data, we can make an appropriate approach for dealing with the scale issue of remote sensing images.

Smart Photogrammetric and Remote Sensing Image Processing for Very High Resolution Optical Images——Examples from the CRC-AGIP Lab at UNB

This paper introduces some of the image processing techniques developed in the Canada Research Chair in Advanced Geomatics Image Processing Laboratory (CRC-AGIP Lab) and in the Department of Geodesy and Geomatics Engineering (GGE) at the University of New Brunswick (UNB), Canada. The techniques were developed by innovatively/“smartly” utilizing the characteristics of the available very high resolution optical remote sensing images to solve important problems or create new applications in photogrammetry and remote sensing. The techniques to be introduced are: automated image fusion (UNB-PanSharp), satellite image online mapping, street view technology, moving vehicle detection using single set satellite imagery, supervised image segmentation, image matching in smooth areas, and change detection using images from different viewing angles. Because of their broad application potential, some of the techniques have made a global impact, and some have demonstrated the potential for a global impact.

Remote sensing image encryption algorithm utilizing 2D Logistic memristive hyperchaotic map and SHA-512

The two-dimensional Logistic memristive hyperchaotic map(2D-LMHM)and the secure hash SHA-512 are the foundations of the unique remote sensing image encryption algorithm(RS-IEA)suggested in this research.The proposed map is formed from the improved Logistic map and the memristor,which has wide phase space and hyperchaotic range and is exceptionally excellent to be utilized in specific applications.The proposed image algorithm uses the permutation-assignment-diffusion structure.Permutation generates two position matrices in a progressive manner to achieve an efficient random exchange of pixel positions,assignment is carried through on the image pixels of the permutated image to entirely remove the original image information,strengthening the relationship between permutation and diffusion,and loop diffusion in two different directions can use subtle changes of pixels to affect the whole plane.The random key and plain-image SHA-512 hash values are used to produce an additional key,which is then utilized to figure out the permutation parameters and the initial value of a chaotic map.The experimental results with the average NPCR=99.6094%(NPCR:number of pixels change rate),average UACI=33.4638%(UACI:unified average changing intensity),100%pass rate of the targets in the test set,the average correlation coefficient is 0.00075,and the local information entropy is 7.9025,which shows that the algorithm is able to defend against a variety of illegal attacks and provide more trustworthy protection than some of the existing state-of-the-art algorithms.

Remote sensing imagery in vegetation mapping: a review

Aims Mapping vegetation through remotely sensed images involves various considerations,processes and techniques.Increasing availability of remotely sensed images due to the rapid advancement of remote sensing technology expands the horizon of our choices of imagery sources.Various sources of imagery are known for their differences in spectral,spatial,radioactive and temporal characteristics and thus are suitable for different purposes of vegetation mapping.Generally,it needs to develop a vegetation classification at first for classifying and mapping vegetation cover from remote sensed images either at a community level or species level.Then,correlations of the vegetation types(communities or species)within this classification system with discernible spectral characteristics of remote sensed imagery have to be identified.These spectral classes of the imagery are finally translated into the vegetation types in the image interpretation process,which is also called image processing.This paper presents an overview of how to use remote sensing imagery to classify and map vegetation cover.Methods Specifically,this paper focuses on the comparisons of popular remote sensing sensors,commonly adopted image processing methods and prevailing classification accuracy assessments.Important findings The basic concepts,available imagery sources and classification techniques of remote sensing imagery related to vegetation mapping were introduced,analyzed and compared.The advantages and limitations of using remote sensing imagery for vegetation cover mapping were provided to iterate the importance of thorough understanding of the related concepts and careful design of the technical procedures,which can be utilized to study vegetation cover from remote sensed images.

Integration of optical and SAR remote sensing images for crop-type mapping based on a novel object-oriented feature selection method

Remote sensing is an important technical means to investigate land resources.Optical imagery has been widely used in crop classification and can show changes in moisture and chlorophyll content in crop leaves,whereas synthetic aperture radar(SAR)imagery is sensitive to changes in growth states and morphological structures.Crop-type mapping with a single type of imagery sometimes has unsatisfactory precision,so providing precise spatiotemporal information on crop type at a local scale for agricultural applications is difficult.To explore the abilities of combining optical and SAR images and to solve the problem of inaccurate spatial information for land parcels,a new method is proposed in this paper to improve crop-type identification accuracy.Multifeatures were derived from the full polarimetric SAR data(GaoFen-3)and a high-resolution optical image(GaoFen-2),and the farmland parcels used as the basic for object-oriented classification were obtained from the GaoFen-2 image using optimal scale segmentation.A novel feature subset selection method based on within-class aggregation and between-class scatter(WA-BS)is proposed to extract the optimal feature subset.Finally,crop-type mapping was produced by a support vector machine(SVM)classifier.The results showed that the proposed method achieved good classification results with an overall accuracy of 89.50%,which is better than the crop classification results derived from SAR-based segmentation.Compared with the ReliefF,mRMR and LeastC feature selection algorithms,the WA-BS algorithm can effectively remove redundant features that are strongly correlated and obtain a high classification accuracy via the obtained optimal feature subset.This study shows that the accuracy of crop-type mapping in an area with multiple cropping patterns can be improved by the combination of optical and SAR remote sensing images.

Advanced Investigation of Remote Sensing to Geological Mapping of Zefreh Region in Central Iran

This study has tried to prove the ability of remote sensing techniques to extract information necessary for preparation of geological mapping of the earth’s surface using multi-spectral satellite images which are rich sources of Earth’s surface information. In this study, the surface geological mappings of Zefreh region have been investigated through ASTER, OLI, and IRS-PAN remote sensing data. To prepare the geological map, preprocessing steps and reducing noises from data using MNF algorithm were firstly carried out. Then a set of processing algorithms and image classification methods are included;the band rationing, color composite and pixel classification based on maximum likelihood, spectral and sub-pixel classification methods of spectral angle mapper (SAM), spectral feature fitting (SFF), linear spectral differentiation (LSU), hill-shade images and automatic lineament extraction were used. Confusion matrix was formed for all classified images through control points were randomly selected from 1:25,000 map of the region to determine the accuracy of obtained results, which indicated the maximum accuracy (up to 90%) of output images. Comparing the results obtained from these methods with the map prepared by ground operations confirmed accuracy results. Finally, the surface geology and fault map of Zafreh region was produced by combining detected geological formations and tectonic lineaments.

The Digital Mapping of Ukrainian Soils on the Base of High Resolution Space Images

The first Ukrainian using experience of multispectral space scanning for digital soil mapping is described in this paper. Methodical approaches for detailed soil observation of Ukrainian forest regions are elaborated based on modem mapping principles. For the first time in Ukraine, digital soil maps based on GIS （geographic information system） were obtained for individual farms. In GIS based on space images and digital relief models, the medium-scale and large-scale soil maps were created by geo-statistical methods. According to elaborated methods, modem digital soil mapping should provide all combined works： remote sensing and traditional soil observations. The modem digital soil mapping should be based just on quantitative principles： on remote sensing data, geomorphologic field parameters, and chemical analyses. The methodological approaches, which were used for the first time in Ukraine during digital soil mapping by remote sensing methods, are described in this paper.

Technical progress of China’s national remote sensing mapping:from mapping western China to national dynamic mapping

Remote sensing mapping is an important research direction in the development of geographic surveying and mapping.In order to successfully implement the project of Mapping Western China(MWC),a technical mapping system has been established.In this project,many problems have been solved through technological innovation,such as block adjustment with scarce control points,large-scale aerial/satellite image mapping,and intelligent interpretation of multi-source images.Several softwares were developed,e.g.PixelGrid for aerial/satellite image mapping in a large area,FeatureStation for the integration of multi-source data in the complex terrain areas,and an airborne multi-band and multi-polarization interferometric data acquisition system for SAR mapping.For the first time,full coverage of 1:50,000 topographic data of China’s land territory has been produced,which means the geospatial framework of digital China is basically completed.With the implementation of other key national plans and projects(i.e.national geographic conditions monitoring and national remote sensing mapping),the focus has changed from MWC to national dynamic mapping.Accordingly,a dynamic mapping system is established.The data acquisition capability has developed from a single source to multiple sources and multiple modalities.The mapping capability has developed into dynamic mapping,and the capability for database update shows the characteristics of collaboration.The national geographic condition monitoring creates a multi-scale index system for statistical analysis for various needs.A multi-level and multi-dimensional technical system for statistical computing and decision-making service is developed for the transformation from dynamic monitoring to information service.In this paper,we give a brief introduction about the recent development of remote sensing mapping in China with respect to data acquisition,map production,and information service.The purpose of this paper is to motivate the establishment of theory and method for remote sensing mapping,technical and equipment in the smart mapping era,to improve the capability of perceiving,analyzing,mining,and applying geographic data,and to promote the intelligent development of geographic surveying and mapping.

Land use balance determination using satellite imagery and geographic information system:case study in South Sulawesi Province,Indonesia

This study was conducted to produce a GIS-based land use/land cover(LULC)balance map for a certain period as a reference for policymakers in planning their future regional development.This study also measures supervised classification accuracy based on remote sensing and geographic information system(GIS)integration with field conditions.In June 2005 satellite imagery 7 ETM+was used as asset maps to assess land-use changes(LUC).Although in March 2019,the liability maps used satellite imagery 8 OLI/TIRS.Methods analysis consists of pre-image processing,image interpretation,random point,field check,and accuracy assessment.The image processing results were overlaid with an Indonesian topographic map to draw a LULC balance map.The findings indicate that in June 2005 and March 2019,each LULC had an assessment accuracy value of 82%and 86%,with a predicted assessment accuracy value of 18.05%and20.50%,respectively.These findings are checked to determine the suitability performance of field-based imaging approaches based on the Cohen Kappa coefficient criteria of 0.45 and 0.48 for June 2005 and March 2019.Based on these results,the image processing precision and suitability were excellent since they are more than 80%and satisfy the Cohen Kappa performance criterion.Furthermore,geospatial data on the LULC balance map is essential as a guide for planners and decision-makers to plan their regional development.

MapGis与ArcGis在多源影像图处理中的应用

【目的】利用MapGis、ArcGis软件分别对各类型的地质图件数据进行分析、编辑和处理,实现对多源数据的清晰呈现。【方法】通过文件格式转换、影像配准、影像精校正、提取栅格影像、3D制图等操作对多源影像图进行处理。【结果】图像在经过处理后,不仅能提高影像图件的局部分辨率和色彩对比度,还能直观、精准、立体地呈现出高质量影像信息。【结论】根据专题图的不同,可合理灵活地应用MapGis、ArcGis软件的功能模版,发挥各软件功能模块的优势,不仅能提高地质工作图件编制的效率,还为后续研究提供了重要参考。

遥感影像路径规划中A*算法优化研究

针对在高分辨率遥感影像上进行路径规划时所面临的算法搜索范围大,效率低且转折点较多等问题,提出一种基于A*算法的全局路径规划算法。在原始A*算法的启发函数部分引入余弦函数,减少冗余节点的搜索过程,缩小算法搜索节点的范围,提升算法运行效率;设计拐点优化方案,减少规划路径中不必要的拐点数,提升路径规划结果的平滑性。为验证改进方法的有效性,在Matlab软件中进行仿真实验,分析原始A*算法和改进后A*算法的搜索节点范围与路径中拐点数量,并在遥感影像的二值地图中进行真实路径规划对比实验,分析路径长度与运行时间。实验数据表明,改进后算法的扩展节点减少30%以上,非必要拐点数减少35%以上,路径规划长度缩短10.1%,运行时间减少10.7%,提升了寻求最优路径的效率。

利用Quick Bird全色遥感影像更新城市大比例尺地形图

利用现势的高分辨率遥感影像,经过精确的多项式变换正射几何纠正后,得到正射影像(DOM)与原有的数字线划图(DLG)叠加,经判读识别地物的变化,实现对原有地形图的快速更新。通过对地面控制点和检查点纠正的精度分析说明,利用QuickBird遥感影像数据在地势较为平坦的城区更新1:2000比例尺的地形图是可行的。

面向海岛海岸带区域的高分遥感影像智能化色彩增强方法

原始高空间分辨率海岛海岸带遥感影像往往存在影像灰暗、偏色、地物信息较难辨识的现象。为及时获取清晰、信息丰富、反差适中、亮度均匀的海岛礁遥感影像,满足日益强烈的海岛海岸带地理信息保障需求,针对海岛海岸带高空间分辨率遥感影像,该文提出一种深度学习结合改进直方图匹配的智能化调色方法。首先,进行数据重采样与自适应分块获取抽稀影像;其次,应用MBLLEN网络对抽稀影像进行真彩色增强;最后,采用改进直方图匹配的方法对原始影像进行色彩映射,最终得到符合人眼视觉、色彩一致、细节丰富的遥感影像。采用主客观相结合的方式综合评价调色效果,结果表明:相较于Retinex,HE和MASK等常用调色方法,该文算法结果更符合人眼视觉、色彩一致、细节丰富,可有效改善海岛海岸带高空间分辨率遥感影像视觉效果,较好地保留原始地物的细节信息,大幅提升调色效率。

多级对比学习下的弱监督高分遥感影像城市固废堆场提取

城市固体废物是城市化进程中的重要污染源,对城市生态环境和公共健康造成了巨大危害。高分影像固废堆场智能解译是实现自动排查,提升监测效率的核心和关键技术。基于深度学习的固废堆场自动提取方法严重依赖于获取成本高、制作难度大的高质量像素级标注。为此,本文提出使用更易获取的影像级标注,利用影像自监督学习实现像素级固废堆场提取。围绕固废堆场的影像特征,本文方法在尺度对比约束下综合像素、影像两个层次的对比学习方法,对固废堆场的类别激活图细化和完善,并基于此生成高质量的固废堆场伪像素级标注,用于训练固废堆场提取模型。试验结果表明,本文方法在固废堆场提取的F 1值和IoU分数方面分别达到了71.58%和55.74%,显著优于所有对比方法。这说明利用多级对比学习的弱监督方法能够获得更加完整且准确的类别激活图,从而取得更高的固废堆场提取精度。

基于正则MAP模型的遥感影像亚像元定位

为了更好地解决亚像元的定位问题,基于超分辨率影像重建的技术,结合亚像元定位理论,提出了一种应用于亚像元定位的正则MAP估计模型,并且通过真实数据进行了检验。实验表明,该模型是一种简单、有效地解决亚像元定位问题的方法。

QuickBird遥感影像的融合及在农业园区底图制作中的应用研究

应用比值变换、IHS变换、主成分变换和合成比值变量变换等4种影像融合方法对QuickBird多光谱和全色遥感影像进行融合,并利用偏差指数、平均梯度等指标对融合效果进行定量评价;运用最佳指数因子确定参与成图的多光谱影像的波段.研究结果表明,合成比值变量变换法的融合效果最佳;最优波段组合为431,并在此基础上建立了地物判读分析依据,用于QuickBird遥感影像目视解译;采用容差格网矢量化技术进行遥感影像的屏幕矢量化,制作了1:2000比例尺农业园区底图.

基于MapServer的遥感影像发布系统的研究

遥感影像数据的共享是发展的必然趋势。遥感影像发布系统的研究与建立是当前的一个热点问题。本文介绍了MapServer的特征,阐述了基于MapServer建立遥感影像发布系统的原理和特点及遥感影像数据的组织。讨论了发布界面Ka-map引入的Ajax技术和缓存的设置。给出了利用MapServer等一系列开源软件实现遥感影像网络发布的框架。

基于多位移光谱遥感图像的空间引力模型亚像元定位

目前基于多位移光谱遥感图像的亚像元定位方法通常很少考虑点扩散函数效应影响,本文提出了一种基于多位移光谱遥感图像的空间引力模型亚像元定位方法。为了生成粗糙丰度图像,首先对多个多位移光谱遥感图像进行解混;在点扩散函数效应被考虑的前提下,对粗糙丰度图像实施面积到点的克里插值处理,然后通过理想方波滤波进行滤波,最终获得改进后的粗糙丰度图像;利用空间引力模型对改进后的粗糙丰度图像进行上采样,从而得到上采样丰度图像,再对上采样丰度图像执行整合处理,以生成精细丰度图像;在完成上述所有图像处理步骤后,最终通过应用类别分配方法,将类别标签分发给各个亚像元,以此获得精确的定位结果。在2组实验数据集上的实验结果表明:本文提出的方法比现有的亚像元定位方法获得了更好的定位效果。

基于深度旋转不变特征图哈希的遥感图像检索

哈希技术采用紧致哈希码表示数据,因其高效性被广泛应用于大规模遥感图像检索任务。受卫星观测影响,同一地物在不同遥感图像中呈现不同角度,导致检索性能下降。为解决该问题,该文提出深度特征图旋转不变哈希方法(DRIFMH),包括特征提取、哈希量化2个模块。特征提取模块对特征图进行不同角度旋转,提出特征一致性损失,使不同旋转角度的图像特征保持一致,克服旋转带来的不利影响。哈希量化模块对图像特征进行二值量化,生成哈希码,引入分类交叉熵损失,提升哈希码的鉴别能力。该文选取经典遥感图像数据集AID、UCMD作为实验数据集,将DRIFMH与多个哈希方法进行实验对比,结果表明DRIFMH能够生成旋转不变的遥感图像特征,提升大规模遥感图像检索性能。