Automatic Extraction of Urban Road Centerlines from High-Resolution Satellite Imagery Using Automatic Thresholding and Morphological Operation Method

The commercial high-resolution imaging satellite with 1 m spatial resolution IKONOS is an important data source of information for urban planning and geographical information system (GIS) applications. In this paper, a morphological method is proposed. The proposed method combines the automatic thresholding and morphological operation techniques to extract the road centerline of the urban environment. This method intends to solve urban road centerline problems, vehicle, vegetation, building etc. Based on this morphological method, an object extractor is designed to extract road networks from highly remote sensing images. Some filters are applied in this experiment such as line reconstruction and region filling techniques to connect the disconnected road segments and remove the small redundant. Finally, the thinning algorithm is used to extract the road centerline. Experiments have been conducted on a high-resolution IKONOS and QuickBird images showing the efficiency of the proposed method.

Instance Segmentation of Outdoor Sports Ground from High Spatial Resolution Remote Sensing Imagery Using the Improved Mask R-CNN

Aiming at the land cover (features) recognition of outdoor sports venues (football field, basketball court, tennis court and baseball field), this paper proposed a set of object recognition methods and technical flow based on Mask R-CNN. Firstly, through the preprocessing of high spatial resolution remote sensing imagery (HSRRSI) and collecting the artificial samples of outdoor sports venues, the training data set required for object recognition of land cover features was constructed. Secondly, the Mask R-CNN was used as the basic training model to be adapted to cope with outdoor sports venues. Thirdly, the recognition results were compared with the four object-oriented machine learning classification methods in eCognition&#174. The experiment results of effectiveness verification show that the Mask R-CNN is superior to traditional methods not only in technical procedures but also in outdoor sports venues (football field, basketball court, tennis court and baseball field) recognition results, and it achieves the precision of 0.8927, a recall of 0.9356 and an average precision of 0.9235. Finally, from the aspect of practical engineering application, using and validating the well-trained model, an empirical application experiment was performed on the HSRRSI of Xicheng and Daxing District of Beijing respectively, and the generalization ability of the trained model of Mask R-CNN was thoroughly evaluated.

Intelligent High Resolution Satellite/Aerial Imagery

High resolution satellite images are rich source of geospatial information. Nowadays, these images contain finest spectral and spatial information of ground realities in different electromagnetic spectrum. Many image processing softwares, algorithms and techniques are available to extract such information from these images. Multi spectral as well as panchromatic (PAN) high resolution satellite images are missing, one important information, regarding ground features and realities that information is attribute information which is not directly available in high resolution satellite images. From very first day, this information used to be collected through indirect ways using GPS, digitizing, geo-coding, geo tagging, field survey and many other techniques. Our real world has vertical labels for ground observer to identify and use this information. These vertical labels are present in form of names, logos, icons, symbols and numbers. These vertical labels ease us to work in real world. Satellites are unable to read these labels due to their vertical orientation. Making satellite/aerial imagery rich of attribute information, we have the possibility to design our world accordingly. Just like vertical labels we can also place real physical horizontal label for space sensors, to make this information directly available in high resolution satellite/aerial imagery. This work is about possibilities of such techniques and methods.

Earthquake-triggered landslide interpretation model of high resolution remote sensing imageries based on bag of visual word

Traditional visual interpretation is often inefficient due to its excessively workload professional knowledge and strong subjectivity.Therefore,building an automatic interpretation model on high spatial resolution remote sensing images is the key to the quick and efficient interpretation of earthquake-triggered landslides.Aiming at addressing this problem,a landslide interpretation model of high-resolution images based on bag of visual word(BoVW)feature was proposed.The high-resolution images were pre-processed,and then BoVW feature and support vector machine(SVM)was adopted to establish an automatic landslide interpretation model.This model was further compared with the currently widely used Histogram of Oriented Gradient(HoG)feature extraction model.In order to test the effectiveness of the method,typical landslide images were selected to construct a landslide sample library,which was subsequently utilized as the foundation for conducting an experimental study.The results show that the accuracy of landslide extraction using this method reaches as high as 89%,indicating that the method can be used for the automatic interpretation of landslides in disaster-prone areas,and has high practical value for regional disaster prevention and damage reduction.

Developing an Automated Land Cover Classifier Using LiDAR and High Resolution Aerial Imagery

The aim of this project is to create high resolution land cover classification as well as tree canopy density maps at a regional level using high resolution spatial data. Modeling and the data manipulation and analysis of LiDAR LAS point cloud dataset as well as multispectral aerial photographs from the National Agriculture Imagery Program (NAIP) were carried out. Using geoprocessing modeling, a land cover map is created based on filtered returns from LiDAR point cloud data (LAS dataset) to extract features based on their class and return values, and traditional classification methods of high resolution multi-spectral aerial photographs of the remaining ground cover for Clarion County in Pennsylvania. The newly developed model produced 7 classes at 10 ft × 10 ft spatial resolution, namely: water bodies, structures, streets and paved surfaces, bare ground, grassland, trees, and artificial surfaces (e.g. turf). The model was tested against areas with different sizes (townships and municipalities) which revealed a classification accuracy between 94% and 96%. A visual observation of the results shows that some tree-covered areas were misclassified as built up/structures due to the nature of the available LiDAR data, an area of improvement for further studies. Furthermore, a geoprocessing service was created in order to disseminate the results of the land cover classification as well as the tree canopy density calculation to a broader audience. The service was tested and delivered in the form of a web application where users can select an area of interest and the model produces the land cover and/or the tree canopy density results (http://maps.clarion.edu/LandCoverExtractor). The produced output can be printed as a final map layout with the highlighted area of interest and its corresponding legend. The interface also allows the download of the results of an area of interest for further investigation and/or analysis.

Development of a Generic Model for the Detection of Roof Materials Based on an Object-Based Approach Using WorldView-2 Satellite Imagery

The detection of impervious surface (IS) in heterogeneous urban areas is one of the most challenging tasks in urban remote sensing. One of the limitations in IS detection at the parcel level is the lack of sufficient training data. In this study, a generic model of spatial distribution of roof materials is considered to overcome this limitation. A generic model that is based on spectral, spatial and textural information which is extracted from available training data is proposed. An object-based approach is used to extract the information inherent in the image. Furthermore, linear discriminant analysis is used for dimensionality reduction and to discriminate between different spatial, spectral and textural attributes. The generic model is composed of a discriminant function based on linear combinations of the predictor variables that provide the best discrimination among the groups. The discriminate analysis result shows that of the 54 attributes extracted from the WorldView-2 image, only 13 attributes related to spatial, spectral and textural information are useful for discriminating different roof materials. Finally, this model is applied to different WorldView-2 images from different areas and proves that this model has good potential to predict roof materials from the WorldView-2 images without using training data.

Retrieval of High Resolution Satellite Images Using Texture Features

In this research, a content-based image retrieval （CBIR） system for high resolution satellite images has been developed by using texture features. The proposed approach uses the local binary pattern （LBP） texture feature and a block based scheme. The query and database images are divided into equally sized blocks, from which LBP histograms are extracted. The block histograms are then compared by using the Chi-square distance. Experimental results show that the LBP representation provides a powerful tool for high resolution satellite images （HRSI） retrieval.

SAR Image Compression Using Integer to Integer Transformations, Dimensionality Reduction, and High Correlation Modeling

In this document, we present new techniques for near-lossless and lossy compression of SAR imagery saved in PNG and binary formats of magnitude and phase data based on the application of transforms, dimensionality reduction methods, and lossless compression. In particular, we discuss the use of blockwise integer to integer transforms, subsequent application of a dimensionality reduction method, and Burrows-Wheeler based lossless compression for the PNG data and the use of high correlation based modeling of sorted transform coefficients for the raw floating point magnitude and phase data. The gains exhibited are substantial over the application of different lossless methods directly on the data and competitive with existing lossy approaches. The methods presented are effective for large scale processing of similar data formats as they are heavily based on techniques which scale well on parallel architectures.

一种融合多尺度混合注意力的建筑物变化检测模型

针对高分辨率遥感图像非真实变化所引起的错误检测问题,提出一种新颖的轻量化孪生神经网络建筑物变化检测模型。其中轻量化的特征提取模块可以获取不同尺度的局部上下文信息,使其充分学习局部和全局特征。由通道和空间注意力组成的混合注意力模块可以充分利用周围丰富的时空语义信息,以实现变化建筑物的准确提取。针对变化建筑物尺度跨度较大,容易导致建筑物边缘细节提取粗糙、小尺度建筑物漏检等问题,引入多尺度概念,将提取到的特征图划分为多个子区域,并分别引入混合注意力模块,最终将不同尺度的输出特征进行加权融合,以加强边缘细节提取能力。模型在WHU-CD、LEVIR-CD公开数据集进行实验,并分别取得87.8%和88.1%的F 1值,相较于6种对比模型具有更高的变化检测精度。

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U-Net、U-Net++和Attention-U-Net网络的遥感影像水体提取

目前,深度学习在高分辨率遥感影像水体提取方面的应用已成为遥感领域的研究热点。其中基于U-Net网络的算法在水体提取中表现出较好的性能,但鲜有研究对不同U-Net网络算法在水体提取任务中的性能差异进行深入比较。因此,本文选择U-Net、U-Net++和Attention-U-Net 3种卷积神经网络,基于GID数据集,进行试验与定量分析。结果表明:U-Net++的训练精度最高,其次为U-Net、Attention-U-Net,三者分别为0.912、0.907、0.899;U-Net++的边缘提取能力优于其他两种网络;在分割不同类型水体和区分遥感影像中与水体区域相似的非水体区域上,U-Net++的提取效果显著,U-Net和Attention-U-Net易出现漏提现象,效果欠佳。

结合全局特征与局部互通的河流断流接续优化

高分辨率遥感影像的水体提取常因地物类型复杂、部分河流狭窄等因素导致水体提取结果不完整、不连续。因此,结合水体自身光谱和纹理特征,提出了一种结合全局特征与局部互通的遥感影像水体提取优化方法。首先,在水体提取初始结果的基础上,通过多尺度Frangi滤波和大津法(OTSU)分割算法提取线性支流,对河流进行补充得到初步优化结果。然后,结合局部互通的断流接续算法,对初步优化结果中的断流部分进行连接。最后,通过K-means聚类提取水体部分,并与精确优化结果进行拓扑检查及光谱检查,实现块状水体的验证筛选。实验结果表明,本文方法能够提取到细小支流,提高优化的准确度;断流接续算法的加入,有助于提高河流提取的完整性。与其他方法相比,本文方法的总体精度分别提高1.04%、1.50%,F1分别提高5.84%、8.28%,可以作为有效提升水体优化后处理的手段,提高了水体提取的完整度和精度。

多尺度特征融合与空间优化的弱监督高分遥感建筑变化检测

针对建筑物变化检测中深度学习方法严重依赖大量高成本高难度的像素级标注样本进行模型训练的问题,本文提出一种基于图像级标注样本的高分辨率遥感建筑物弱监督变化检测方法MDF-LSR-Net。该方法首先提取双时相多尺度差异特征,并对多尺度差异特征进行渐进式融合,利用充分融合后的多层次多尺度差异特征来生成变化热力图;然后,利用低层融合差异特征的局部空间相似性来优化初始的变化热力图,进一步增强热力图中变化区域的完整性和准确性;最后,基于高质量的变化热力图训练最终的变化检测模型。在公开的建筑物变化检测数据集WHU和LEVIR上的多组试验结果表明,本文方法能够获取更加完整且准确的变化热力图,从而使得基于此训练的变化检测模型也取得更高的检测精度,其中最终的变化检测模型在WHU数据集上的IOU和F 1值分别可达65%和79%以上。

基于融合多模态的遥感影像冰川识别方法

冰川对气候变化极为敏感,冰川变化与区域生态、自然灾害、水资源等息息相关。高原冰川遥感信息提取及实时监测是监测冰川变化不可或缺的手段。为有效识别多尺度高分辨率遥感影像中的冰川,设计一种Glacier-Unet模型。(1)针对现有的基于Landsat卫星遥感影像高原冰川提取算法因缺乏应对复杂地物干扰影响的有效方法,导致反射目标信息丢失的问题。以青藏高原阿尼玛卿雪山为试验对象,选取基于Landsat-9遥感卫星高分辨率影像制作数据集。对高分辨率冰川遥感影像进行数据预处理,采取特征级融合和像素级融合制作多模态遥感数据影像,通过滑动切片、数据增强手段丰富语义分割数据集,保证模型训练准确性和鲁棒性;(2)针对零散、细小冰川识别能力不足的问题,设计门控多尺度过滤层(Gated Multi-scale Filter Layer,G-MsFL)滤除无用特征信息,使模型具备多尺度特征提取和特征融合能力,有效识别复杂地物环境中的冰川;(3)针对冰川轮廓模糊问题,设计并联双通道注意力模块(Paralleling Dual Attention Module,P-DAM)。将冰川边界丰富的上下文信息进行编码作为特征图的局部特征,从而增强其特征表达能力。对改进的Glacier-Unet模型在阿尼玛卿测试数据集中的实验结果进行定性、定量分析,发现整体分割精度较对比方法提升6.1%,且能有效识别零散、细小冰川,对高原地区冰川识别工作具有重要意义。

融合Partial卷积与残差细化的遥感影像建筑物提取算法

由于高空间分辨率遥感图像中背景与建筑物对象的相似度高,导致网络难以兼顾不同大小的建筑物,建筑边界区域的像素与背景混淆,建筑边界很容易被漏检。为解决上述问题,提出融合Partial卷积与残差细化的遥感影像建筑物提取算法(UUNet)。以U-Net为基线网络,首先,改进编码器。在编码器前端加入两个Conv4×4,在最初扩大感受野,捕捉更多遥感影像特征信息,利用Partial卷积(PConv3×3)构造的PC模块,增强编码器提取多尺度建筑物特征的能力,用Conv2×2进行两倍下采样,减少建筑物特征信息丢失。其次,减少参数量。裁剪U-Net网络解码器三层结构为UUNet网络解码器。最后,增加改进的残差细化模块。在解码器输出端构造裁剪到三层结构的U型残差细化模块,对解码器输出的粗糙建筑物特征图进行进一步提纯,使建筑物边缘信息更加清晰,网络解码器与U型残差细化模块编码器进行跳跃连接,保留最初特征,将SimAM嵌入细化模块中,提高建筑物关注度,优化网络改善边界模糊,提升目标边界提取质量。在Satellite datasetⅡ(East Asia)数据集上进行消融实验,UUNet比U-Net的IoU_(Building)、IoU_(Background)、F1、OA和MIoU分别提高2.78个百分点、0.12个百分点、1.91个百分点、0.19个百分点、1.45个百分点,表明UUNet网络优于基线网络;在Satellite datasetⅡ(East Asia)数据集和WHU数据集上做对比实验,UUNet相较于现有的主流算法更优,能够显著地提升高分辨率遥感影像中建筑物提取的效果。

基于主动表征数据的城市虚拟空间意象测度研究--以南京中心城区为例

城市意象作为民众对建成环境特征要素的集体记忆,表征着城市的在地性文化和市民的集体认同,是构建人本城市的重要基石,成为高质量发展背景下的重要议题。在网络社会背景下,城市意象呈现出“基于现实世界的体验式感知”“基于虚拟空间的泛在化感知”两种不同的感知模式;虚拟空间意象的相关研究成为热点。以南京中心城区为例,文章基于主动表征数据,采用地理信息系统(GIS)空间分析、OPTICS算法和自然语言处理(NLP)方法,构建虚拟空间意象测度模型,发现虚拟空间意象热点呈现“核心+边缘”的分布模式和“历史城区集聚,外围散点分布”的集聚特征。基于“由点到面”的聚类思想,采用OPTICS聚类方法测度区域要素,填补相关研究空白;创新性地将情绪性分析引入虚拟空间意象的评估,结合感知热度和情绪强度,划分节点和标志要素;将识别结果与南京市国土空间总体规划进行对比,验证本方法的准确有效性和研究价值。

SGEU-Net:用于从高分遥感影像中提取道路的空间分组增强注意力网络

道路提取是现代路网规划的重要组成部分。近来,许多深度学习方法已被应用于该领域。然而,由于车辆以及树木和建筑物阴影的遮挡,在保持连续性的同时准确提取道路区域仍然是一个问题。论文提出了一种新型的道路提取网络-空间分组增强网络(SGEU-Net),由两个部分构成:一个改进的U-Net编码器-解码器网络和空间分组增强(SGE)注意力模块。SGE模块可以明显改善不同语义子特征在组内的空间分布,产生更可观的统计差异,增强语义区域的特征学习。改进的算法在马萨诸塞州道路数据集上进行实验,结果表明,与当前先进算法相比,所提算法提高了从遥感图像中提取道路的效果。

基于点扩散函数的图像学高分辨率技术

常规基于一维假设的高分辨率处理技术如反褶积、反Q滤波方法等,针对水平构造或者小倾角构造有较好的处理效果,但在面向高陡构造或者断裂等复杂构造时处理效果不佳。而偏移成像结果可以看作是点扩散函数(PSF)与真实反射系数褶积的结果,高维空间条件下的高分辨率处理技术实际上是此正演问题的一个反问题,即基于成像结果进行反射系数反演。为此,提出了一种基于点扩散函数的高分辨率处理技术,首先基于点扩散函数的计算原理,推导出了基于高频近似条件下的快速求解算法,并在高维空间褶积理论的指导下结合图像学反演算法研发了纯数据驱动的点扩散函数提取技术,最后采用高维空间反褶积算法求得高分辨率成像结果,实现地震频带的有效拓宽和成像分辨率的提升。模型数据和实际数据测试结果表明,该算法可在不损失低频的前提下有效扩宽高频,且具有在计算效率与常规高分辨率算法基本相当的条件下有效提高不同展布方位地质体分辨率的优势。

高分辨率卫星数字正射影像生产工艺及关键技术研究

卫星正射影像数据获取相对容易,具有良好的时效性和实用性。通过深入研究高分辨率卫星影像生产所涉及的主要技术工艺,采用一系列方式分析和优化影像正射纠正生产流程,包括调整生产工艺以及Digital Surface Model辅助拉花变形修改等技术手段,旨在减少人工工作量并提高成果精度等,达到了缩短生产周期,提高成果时效性的目的。

浅谈意象性在高温颜色釉中的表现

从商周时期至今,意象性在高温颜色釉中的表现各具特点,经历了独特的发展轨迹。意象性在高温颜色釉中的表现具有民族性、自然性、审美性、情感性等鲜明特色。意象性在高温颜色釉中的表现包括高温颜色釉材料装饰、高温颜色釉绘画和高温颜色釉综合装饰等多种形式。