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.

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 APPLICATION OF REMOTE SENSING TECHNIQUE ON GEOLOGICAL INVESTIGATION OF PLACER DEPOSIT

The practice has proved that it is an economic and effective method to investigate placer gold deposit by using multi-level information sources of remote sensing and multi-variate analysis methods, especially for the area with a sparse population and difficult condition like the Da Hinggan Mountains, China.The information sources used in our work includes Landsat TM, aerial infrared photography and their mosaic image maps and enlarged photos with different scales. According to statistic data, in the study area the gold-bearing rocks are mainly granite, alaskite, granodiorite and some old metamorphic rocks. On gold-bearing geological structures, the fault zones in the four directions (NE, NNE, NW and EW) are obvious, in which NNE and EW are the most key fault zones. On fluvial geomorphology the flow courses stored placer are in the tributaries of the 4th and 5th levels, especially in straight or slight curve reaches. On the basis of analysis the interpretative signs were set up, and the interpretative

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.

基于ZY-3和Landsat-8影像的内蒙古北山地区地层构造解译

内蒙古北山地区属于典型的戈壁荒漠区,基岩裸露,植被不发育,构造复杂,遥感技术在该地区的应用能起到事半功倍的作用。本文基于ZY-3和Landsat-8 OLI影像,选择内蒙古北山基东地区为研究区,利用ZY-3和Landsat-8 OLI数据,并以数据协同理论为基础,运用图像融合、彩色合成、主成分分析、空间滤波等岩性、空间信息增强技术,对研究区展开遥感地质解译,建立该地区地层、构造遥感解译标志。经野外验证,多数解译标志准确可靠,解译地质界线和构造信息与野外实际情况吻合度较高,极大地提高了填图工作效率。研究结果表明,ZY-3和Landsat-8 OLI协同数据在该地区地质调查中具有广阔的应用前景。

基于改进HRNet的遥感影像冬小麦语义分割方法

冬小麦在影像中呈现田块碎小且分布零散等空间特征,同时影像包含的复杂地物对冬小麦识别造成干扰,易出现识别精度低且边界分割模糊等问题。为及时准确获取大范围冬小麦空间分布信息,该研究以高分二号卫星影像作为数据源,提出一种CAHRNet(change attention high-resolution Net)语义分割模型。采用HRNet(high-resolution Net)替换ResNet作为模型的主干网络,网络的并行交互方式易获取高分辨率的特征信息;联合OCR(object-contextual representations)模块聚合上下文信息,以增强像素点与目标对象区域的关联性;3)引入坐标注意力(coordinate attention)机制,使网络模型充分利用有效的空间位置信息,以保留分割区域的边缘细节,提高对分布零散、形状多变的冬小麦田块的特征提取能力。试验结果表明,在自制的高分辨率遥感数据集上,CAHRNet模型的平均交并比(mean intersection over union,MIoU)和像素准确率(pixel accuracy, PA)分别达到81.72%和97.08%,MIoU相较U-Net、DeepLabv3+分别提高了9.09、2.44个百分点;PA相较U-Net、DeepLabv3+分别提高6.80、1.59个百分点,说明CAHRNet模型具有较高的分割识别精度,可为进一步准确获取冬小麦作物分布信息提供技术支撑。

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.

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。

Landsat TM8及GF-1影像黑龙江省线状地物实际与解译宽度对比

线状地物又称为线性地物,是一种普遍存在的土地利用方式。在遥感图像上,线状地物大量存在,这种存在表现为线状地物的可见性,即线状地物的图像特征表现为数个像元宽度的狭长型地物;另一方面,大量线状地物被＂淹没＂在遥感图像的混合像元中,这部分线状地物在遥感图像上具有相对不可见性。在面状地物解译中,线状地物常常由于遥感影像分辨率有限而包含在面状地物中,使面状地物解译结果偏大而不够准确。因此,准确解译线状地物可以校正面状地物解译结果。Landsat TM8影像与GF-1影像作为近几年新出现的高质量高分辨率卫星遥感影像,在各行各业中应用较为广泛,在农业遥感中亦是如此。在农作物面积估算中,Landsat TM8影像与GF-1影像线状地物扣除技术的精确程度直接影响农作物面积估算精度。Landsat TM8影像与GF-1影像线状地物实际宽度与解译宽度对比研究对于农作物面积估算和估产具有重大意义。由于分辨率相差较大,在线状地物解译中,GF-1影像具有明显优势。该文以23景Landsat TM8影像和14景GF-1影像为基础,运用统计学方法对黑龙江省341条线状地物实际宽度与解译宽度做对比研究。结果表明,对线状地物解译精度影响较大的主要因素为卫星遥感影像分辨率。Landsat TM8影像解译精度较差（｜夸张系数｜〉50%）的线状地物共94条,占全部线状地物的27.5660%;在这部分线状地物中,通常是解译宽度远大于实际宽度;以线状地物实际宽度分类中的0~10 m类别中,线状地物的解译精度最差,而按走向分类和按类型分类对线状地物解译精度影像不大。GF-1影像解译精度较差的线状地物共有29条,占全部线状地物的8.5044%,在这部分线状地物中,通常是解译宽度远大于实际宽度。

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。

不同影像处理方法在大坝施工范围识别中的应用分析

【目的】施工范围是大坝建设过程中施工精细化管理的基础内容,传统的施工范围识别方法主要是人工解译,效率低、成本高、主观性强。将影像智能解译的新方法应用于大坝施工范围识别成为一项有挑战性的技术问题。【方法】以我国贵州省凤山水库为研究区,通过对大坝施工范围特点与现有常用的遥感影像解译方法的分析,针对大坝施工范围具有的区域面积大且连续、地物复杂、人工扰动性程度高等特征,选用高分二号和哨兵二号遥感影像,研究不同的遥感影像解译方法在大坝施工范围自动识别的应用效果。【结果】文中用到的三种方法均取得较好的结果,且分别具有不同的优势。【结论】结果表明,基于影像分割的施工范围识别方法、基于变化检测的施工范围识别方法和基于影像智能分类的施工范围识别方法均能取得较好的识别结果,其中第二种方法最优,且无需人工干预;第三种方法次之,但是方法二和方法三对样本的数量和质量依赖性较高,且存在离散图斑错分的现象;第一种方法容易受阈值的选择和叠加分析的影响,识别精度低于后两种,但是处理流程简单、容易实现。

基于卷积神经网络的光学遥感影像道路提取方法研究进展

随着光学遥感影像空间分辨率的提升和获取渠道的丰富,利用光学遥感影像实现地物智能解译已成为高效的技术路径。由于卷积神经网络(convolutional neural networks,CNN)强大的特征提取能力以及道路信息在多个领域的应用需求,基于CNN的道路提取方法成为了当前的研究热点。鉴于此,本文根据近年来的相关研究文献,对基于CNN的道路提取方法从基于形状特征的改进、基于连通性的改进、基于多尺度特征的改进和基于提取策略的改进四个方面进行归纳总结,然后描述典型道路遮挡案例,并利用经典CNN从样本标签的局限性层面对当前的技术难点进行分析与验证,最后从多源数据协同、样本库建设、弱监督模型和域适应学习四个方面对遥感影像道路提取的发展趋势进行评估和展望。

高分辨率遥感影像样本库动态构建与智能解译应用

在人工智能时代,遥感影像解译朝着自动化智能化方向发展,高质量的样本数据集是其核心。我国积累了海量优质的时空地理信息基础数据及衍生产品,是深度学习驱动的遥感影像智能解译样本的重要来源。盘活现有数据资源,可推动人工智能与遥感解译的应用深度与广度。本文基于现有数据资源,针对样本数据集区域受限、时效性不强、类型单一等问题,研究了面向深度学习的高分遥感影像智能解译样本库动态构建技术。首先,分析了要素提取、地表覆盖分类、变化检测方面的公开样本数据集的特点,提出业务驱动的样本应需生成-动态构建-智能应用思路;其次,研究了基于历史解译成果的样本自动生成、SAM大模型提示学习引导的样本清洗精化方法及实现过程;再次,设计了具有区域性、时序性、尺度性、多传感器、多类型的样本库,以及顾及空间-时间-地类关系的动态样本数据库架构,研究了样本数据集“量化-检索-组合”动态重构过程,实现时空样本的动态管理与多维检索;最后,开展了地表覆盖分类、要素提取、变化检测等智能解译应用,验证了本文研究思路及方法的可行性,以期推动基于已有基础数据的样本数据集的有效利用,以及样本构建-管理-应用及数据-模型-业务的互联互通,为高分遥感影像智能解译样本库构建与应用提供参考思路。

多源卫星遥感数据驱动地震灾害应急制图研究——以2022年青海门源M W6.7地震为例

针对2022-01-08中国青海省门源县M W6.7地震,提出一种综合多源卫星遥感数据和多种数据处理技术快速解译地震相关资料的应急制图技术流程,为地震灾害救援提供具有时效性、多专题的地图产品,该产品可直接服务于震后应急救援,并为之提供决策依据。

基于高分辨率遥感影像的土耳其地震建筑物震害特征

选取土耳其卡赫拉曼马拉什省努尔达吉镇城区和周边乡村地区作为研究区,通过获取地震前后的高空间分辨率遥感影像数据,提取房屋建筑震害信息,识别房屋建筑震害等级,利用震害指数计算区域内地震烈度等级,综合分析得到建筑物震害特征与主要成因,对地震灾情快速评估、应急救援和恢复重建具有重要参考。