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.

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.

Quantifying River Bank Erosion and Accretion Patterns along the Gorai River in Kushtia, Bangladesh: A Geospatial Analysis Utilizing GIS and Remote Sensing Techniques

River bank erosion is a natural process that occurs when the water flow of a river exceeds the bank’s ability to withstand it. It is a common phenomenon that causes extensive land damage, displacement of people, loss of crops, and infrastructure damage. The Gorai River, situated on the right bank of the Ganges, is a significant branch of the river that flows into the Bay of Bengal via the Mathumati and Baleswar rivers. The erosion of the banks of the Gorai River in Kushtia district is not a recent occurrence. Local residents have been dealing with this issue for the past hundred years, and according to the elderly members of the community, the erosion has become more severe activities. Therefore, the main objective of this research is to quantify river bank erosion and accretion and bankline shifting from 2003 to 2022 using multi-temporal Landsat images data with GIS and remote sensing technique. Bank-line migration occurs as a result of the interplay and interconnectedness of various factors such as the degree of river-related processes such as erosion, transportation, and deposition, the amount of water in the river during the high season, the geological and soil makeup, and human intervention in the river. The results show that the highest eroded area was 4.6 square kilometers during the period of 2016 to 2019, while the highest accreted area was 7.12 square kilometers during the period of 2013 to 2016. However, the erosion and accretion values fluctuated from year to year.

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.

Towards an Intelligent Predictive Model for Analyzing Spatio-Temporal Satellite Image Based on Hidden Markov Chain

Nowadays remote sensing is an important technique for observing Earth surface applied to different areas such as, land use, urban planning, remote monitoring, real time deformation of the soil that can be associated with earthquakes or landslides, the variations in thickness of the glaciers, the measurement of volume changes in the case of volcanic eruptions, deforestation, etc. To follow the evolution of these phenomena and to predict their future states, many approaches have been proposed. However, these approaches do not respond completely to the specialists who process yet more commonly the data extracted from the images in their studies to predict the future. In this paper, we propose an innovative methodology based on hidden Markov models (HMM). Our approach exploits temporal series of satellite images in order to predict spatio-temporal phenomena. It uses HMM for representing and making prediction concerning any objects in a satellite image. The first step builds a set of feature vectors gathering the available information. The next step uses a Baum-Welch learning algorithm on these vectors for detecting state changes. Finally, the system interprets these changes to make predictions. The performance of our approach is evaluated by tests of space-time interpretation of events conducted over two study sites, using different time series of SPOT images and application to the change in vegetation with LANDSAT images.

Classifications of Satellite Imagery for Identifying Urban Area Structures

This study compares three types of classifications of satellite data to identify the most suitable for making city maps in a semi-arid region. The source of our data was GeoEye 1 satellite. To classify this data, two pro-grammes were used: an Object-Based Classification and a Pixel-Based Classification. The second classification programme was further subdi-vided into two groups. The first group included classes (buildings, streets, vacant land, vegetations) which were treated simultaneously and on a single image basis. The second, however, was where each class was identified individually, and the results of each class produced a single image and were later enhanced. The classification results were then as-sessed and compared before and after enhancement using visual then automatic assessment. The results of the evaluation showed that the pix-el-based individual classification of each class was rated the highest after enhancement, increasing the Overall Classification Accuracy by 2%, from 89% to 91.00%. The results of this classification type were adopted for mapping Jeddah’s buildings, roads, and vegetations.

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.

A Review of Satellite Remote Sensing Monitoring Methods for Sea Surface Oil Spill

Using satellite remote sensing to monitor oil spill on the sea is an advanced means of oil spill monitoring, and it has the characteristics of wide coverage, speediness and real time, synchronization, continuity, and low cost. Hence, accelerating the research on this technology and establishing a satellite remote sensing monitoring mechanism suitable for oil spill emergency situations is of great significance to improve China's oil spill monitoring capability and prevent or reduce the pollution damage caused by oil spill in the marine environment.This paper analyzes and studies the current situation using satellite remote sensing to monitor oil spills at home and abroad. Based on the basic principle of satellite remote sensing, this paper systematically studies the satellite remote sensing monitoring oil spill principles, satellite data processing methods and oil spill information identification, and summarizes an oil spill identification system that can realize oil spill information reproduction. This system provides an important means of support for the handling of oil spill accidents.

基于GEOimage的SPOT5数据处理

2003年10月,应用SPOT5卫星影像在云南省沾益县进行了森林资源遥感调查。以性价比较高,对SPOT5支持较好的GEOimage软件为平台,总结了SPOT5正射纠正、分辨率融合、彩色合成、地图制作等影像处理方法和关键技术,并作了精度分析。

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.

Railway Transport Infrastructure Monitoring by UAVs and Satellites

Improving the rail transport security requires development and implementation of neoteric monitoring and control facilities in conditions of increasing speed and intensity of the train movement and high level of terrorist threat. Use of Earth remote sensing (ERS), permitting to obtain information from large areas with a sufficiently high resolution, can provide significant assistance in solving the mentioned problems. This paper discusses the possibility of using various means of remote sensing such as satellites and unmanned aerial vehicles (UAV), also known as drones, for receiving information in different ranges of the electromagnetic spectrum. The paper states that joint using of both these means gives new possibilities in improving railroad security.

体验式教学在遥感数字图像处理课程中的实践

体验式教学基于构建主义思想,通过模拟情境或体验真实情境,引导学生真感受、真感悟,突出学生主体性,丰富学生体验模式,健全学生知识架构,从而极大地提升教学效果.遥感数字图像处理是地理信息科学、遥感科学与技术本科专业的主干课程.针对遥感专业的发展现状及当前课程教学中存在的问题,以课程中部分教学内容为例,从课程特点、教学策略、教学评价等多个角度,探讨体验式教学在遥感数字图像处理课程中的实践应用,为教学内容优化及遥感类课程建设提供参考.

基于GNSS技术的农村土地面积测量精度估算方法

为进一步提升土地面积测量的精度,本次开展基于全球导航卫星系统(GNSS)技术的农村土地面积测量精度估算方法的研究。首先基于GNSS技术构建几何定位模型,对定位点中各参数进行深层解析;然后将得到的定位数据与测绘数据通过坐标系转换、栅格滑动赋值等步骤实现数据的归一化;最后将数据代入模型之中,完成测绘精度估算方法的设计。实验结果表明:在不同比例尺下,设计的估算方法预测的控制点与实际情况较相符,可进行准确的土地面积测量精度估算,具有一定可靠性。

环境二号卫星多光谱图像的薄云检测及去除

在遥感图像中,大面积的薄云会使得地物信息被遮蔽,从而对后续图像的判读以及应用产生极大的影响。为消除卫星图像中薄云对下垫面的影响,开发了针对多光谱图像的薄云检测与去除算法。该算法首先利用蓝绿波段合成外推波段,通过暗像元搜索,生成薄云厚度图(HTM)和薄云掩膜图,进而得到无云区像元与云区像元;再计算图像每个波段的HTM,利用外推波段的HTM与每个波段的HTM求得每个波段的线性回归系数,由该系数对图像进行薄云去除。将该方法应用于环境减灾二号(HJ-2A/B)卫星的多光谱图像,研究结果表明,对不同地表类型,薄云去除后图像质量均得到明显的提升,能够清晰地展现出薄云下覆盖的地物信息,并且不影响无云区域的图像质量。利用该算法进行薄云去除后,卫星多光谱图像的清晰度、对比度和标准差都有显著的提升,为后续图像应用提供了质量保障。

卫星遥感影像处理技术及应用实践

遥感卫星是通过搭载在其上的遥测器,对地表及底层的空气进行光电检测来获得相关资料的一种应用卫星。在处于全球固定的轨道上时,可以对地表特定区域进行持续监测。卫星是由卫星平台、遥感器、信息处理装置和信息传送装置等构成,已在农业、林业、海洋、国土和气象等领域得到越来越多的应用。对遥感卫星进行操作、采集和处理的过程,其主要参与者为企业、政府部门、地面和应用软件提供商;在整个产业链的最下面,是一个以卫星遥感工业为主的应用和服务,其可以被分成几个部分,分别是遥感数据销售、数据加工服务、应用服务等,其所涵盖的范围非常广,其中,在气象、测绘和海洋等方面的应用最为普遍。以这一点为基础,该文对卫星遥感影像处理的主要技术进行剖析,并对其在社会管理与生产的每一个方面的实践与运用进行剖析,这对将其最大的优点发挥出来,扩大其使用范围具有一定的好处。

数据激增背景下的海洋卫星地面应用系统建设分析

为解决当前海洋卫星地面应用系统在数据激增背景下准备不足、运行效率不高、普适化应用不够等问题,本文分别从接收、处理、存储和分发4个方面系统总结了各分系统技术发展现状及趋势,分析了国内地面应用系统存在的部分短板,提出加快接收站全球化布局、提前预留天线接收余量、完善国产软件图像处理功能、针对性地制定数据存储和分发策略等观点;指出地面应用系统未来优化的重难点主要在数据处理端,丰富完善算法/模型库、提升对海量遥感图像的解译速度、构建去专业化的遥感图像处理平台应是下一步工作的重点。本文可为海洋卫星地面应用系统优化设计提供参考。

应用卫星遥感影像的内外业一体化地形图制作

随着科技的发展,我国的卫星遥感技术水平具有明显的提升,而且应用范围也变得更加广泛,在内外业一体化地形图制作中使用卫星遥感影像,除了能够提升分别率,还能正确反映土地的利用变化情况,保证内外业一体化地形图的精度,可达到开展土地调查工作的实际要求。本文将对应用卫星遥感影像的内外业一体化地形图制作进行分析。

Interpretation and Compilation of Landsat TM Imagery for Land-use and Site Classification Mapping in the Korqin Sandy Lands, NE China

For the purpose of of forestation, planning and development in the Three-North Region, a series of 6 Landsat TM scenesfrom 1996 to 1997 were used to classify land-use conditions in the whole Korqin Sandy Lands at eastern part of Inner Mongolia, China, with an area of about 430×306 square kilometers. Later on, Site classiflcation was made and mapped for the 4 southern sandy counties. The annotation symbol for each agglomeration of site condition is comprised of six parts: land unit, land use pattern, soi...

A novel mathematical morphology based algorithm for shoreline extraction from satellite images

Shoreline extraction is fundamental and inevitable for several studies.Ascertaining the precise spatial location of the shoreline is crucial.Recently,the need for using remote sensing data to accomplish the complex task of automatic extraction of features,such as shoreline,has considerably increased.Automated feature extraction can drastically minimize the time and cost of data acquisition and database updating.Effective and fast approaches are essential to monitor coastline retreat and update shoreline maps.Here,we present a flexible mathematical morphology-driven approach for shoreline extraction algorithm from satellite imageries.The salient features of this work are the preservation of actual size and shape of the shorelines,run-time structuring element definition,semi-automation,faster processing,and single band adaptability.The proposed approach is tested with various sensor-driven images with low to high resolutions.Accuracy of the developed methodology has been assessed with manually prepared ground truths of the study area and compared with an existing shoreline classification approach.The proposed approach is found successful in shoreline extraction from the wide variety of satellite images based on the results drawn from visual and quantitative assessments.

多源卫星影像的摄影测量遥感智能处理技术框架与初步实践

回顾并分析了摄影测量与遥感学科的发展历程,并针对大数据和智能化测绘新时代的多源遥感影像精准快速智能处理的迫切需求,本文提出摄影测量遥感的科学概念。全新的摄影测量遥感主要由摄影测量和遥感两个学科的交叉融合而形成,致力于研究解决同步探测被摄目标的几何位置、物理属性、语义信息和时序变化关系的理论方法及技术问题,其理论基础和支撑学科包括摄影测量、遥感、人工智能、大数据处理与高性能计算等,将突破目前摄影测量侧重几何处理、遥感侧重语义信息提取反演的相对独立现状和串行技术路线,通过几何模型与光谱辐射反射信息的深度交叉融合形成几何语义一体化处理机制。本文在阐述摄影测量遥感基本概念的基础上,初步探讨了其涉及的主要科学问题及相关研究应用领域,并以多源遥感卫星影像为例构建了一体化摄影测量遥感智能处理技术框架,通过语义信息提取与精准几何处理的交叉闭环融合,显著提升了高分辨率多源遥感卫星影像精准快速处理的自动化和智能化水平,多个应用实践初步验证了相关理论方法的正确性和有效性。