Correg-Yolov3:a Method for Dense Buildings Detection in High-resolution Remote Sensing Images

The exploration of building detection plays an important role in urban planning,smart city and military.Aiming at the problem of high overlapping ratio of detection frames for dense building detection in high resolution remote sensing images,we present an effective YOLOv3 framework,corner regression-based YOLOv3(Correg-YOLOv3),to localize dense building accurately.This improved YOLOv3 algorithm establishes a vertex regression mechanism and an additional loss item about building vertex offsets relative to the center point of bounding box.By extending output dimensions,the trained model is able to output the rectangular bounding boxes and the building vertices meanwhile.Finally,we evaluate the performance of the Correg-YOLOv3 on our self-produced data set and provide a comparative analysis qualitatively and quantitatively.The experimental results achieve high performance in precision(96.45%),recall rate(95.75%),F1 score(96.10%)and average precision(98.05%),which were 2.73%,5.4%,4.1%and 4.73%higher than that of YOLOv3.Therefore,our proposed algorithm effectively tackles the problem of dense building detection in high resolution 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.

Quantizing and analyzing the feature information of coastal zone based on high-resolution remote sensing image

On the basis of realization of beach information and its differentiating of high-resolution remote sensing image on coastal zone, extracting objects are carried through RS multi-scale diagnostic analysis, and fast information extraction methods and key technologies are put forward. Meanwhile image segmentation methods are set forth for objects of coastal zone. And through the application of Otsu2D to the segmentation of water area and dock and the applying of Gabor filter to the separation and extraction of construction, some typical applications of high-resolution RS image are presented in the field of coastal zone surface objects＇ recognition. Quantizing high-resolution RS information on the coastal zone proved to be of great scientific and practical significance for coastal development and management.

Road Vector Map Change Monitoring Based on High Resolution Remote Sensing Images

Some studies about road vector map change detection were done in this paper. Firstly, on the basis of old road vector data, the original high resolution remote sensing image was cut into segments. Then, gray analysis and edge extraction of those segments were done so that changes of roads could be detected. Finally, according to the vector data and gray information of roads which were not changed, road templates were extracted and saved automatically. This method was performed on the World View high resolution image of certain parts in the country. The detection result shows that detection correctness is 79.56% and completeness can reach 97.72%. Moreover, the extracted road templates are essentials for the template matching method of road extraction.

Monitoring of Karst Rocky Desertification Control Projects Based on Remote Sensing Images with Medium and High Spatial Resolution——A Case Study of Disi River Basin in Puan County

[ Objective] The study aimed to improve methods of monitoring Karst Rocky Desertification （KRD） control projects and increase the working efficiency. [Method] Based on remote sensing images with medium and high spatial resolution, KRD control projects in Disi River basin in Puan County were monitored, that is, information of the project construction in the study area was extracted using supervised classification and hu- man-computer interactive interpretation, and the monitoring results were testified with the aid of GPS. [Result] It was feasible to monitor KRD con- trol projects in Disi River basin based on remote sensing images with medium and high resolution, and the monitoring accuracy was satisfactory, reaching above 80% or 90%, so the method is worthy of popularizing. [ Conclusion] Remote sensing images with medium and high resolution can be used to monitor other KRD control Droiects.

Analysis of Urban Greening of Tai'an City in China Based on Remote Sensing Image with High Resolution

By taking urban greening of Tai'an City of Shandong Province for example,selecting remote sensing image Quickbird with high resolution,and combining visual interpretation with automatic classification of the computer,based on urban green space systematic planning map,green space information of the built-up area has been selected for the research centering on green lands in urban parks,productive green lands,green lands attached to residential areas and units,green lands attached to the road,other green lands,water surfaces and so on.Through the statistics and analysis,the distribution condition of each type of urban green land has been obtained,and some suggestions have been proposed in view of existing problems of urban greening.It should enhance the construction of green lands in urban parks,residential areas and units,improve road greening level,implement vertical greening,increase the area of productive green lands and fully make use of idle lands.

New Mathematical Model Based on Affine ransformation for Remote Sensing Image with High Resolution

This paper calculates the parameters of image position and orientation,proposes a mathematical model and adopts a new method with three steps of transformations based on parallel ray projection.Every step of the model is strict,and the map function of each transformation is the first order polynomials and other simple function.The final calculation of the parameters is for the linear equations with good status.As a result,the problem of the relativity of image parameter calculation is solved completely.Some experiments are carried out.

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.

Monitoring of vegetation coverage based on high-resolution images

Measurement of vegetation coverage on a small scale is the foundation for the monitoring of changes in vegetation coverage and of the inversion model of monitoring vegetation coverage on a large scale by remote sensing. Using the object-oriented analytical software, Definiens Professional 5, a new method for calculating vegetation coverage based on high-resolution images （aerial photographs or near-surface photography） is proposed. Our research supplies references to remote sensing measurements of vegetation coverage on a small scale and accurate fundamental data for the inversion model of vegetation coverage on a large and intermediate scale to improve the accuracy of remote sensing monitoring of changes in vegetation coverage.

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.

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.

Optimization of post-classification processing of high-resolution satellite image:A case study

The application of remote sensing monitoring techniques plays a crucial role in evaluating and governing the vast amount of ecological construction projects in China. However, extracting information of ecological engineering target through high-resolution satellite image is arduous due to the unique topography and complicated spatial pattern on the Loess Plateau of China. As a result, enhancing classification accuracy is a huge challenge to high-resolution image processing techniques. Image processing techniques have a definitive effect on image properties and the selection of different parameters may change the final classification accuracy during post-classification processing. The common method of eliminating noise and smoothing image is majority filtering. However, the filter function may modify the original classified image and the final accuracy. The aim of this study is to develop an efficient and accurate post-processing technique for acquiring information of soil and water conservation engineering, on the Loess Plateau of China, using SPOT image with 2.5 rn resolution. We argue that it is vital to optimize satellite image filtering parameters for special areas and purposes, which focus on monitoring ecological construction projects. We want to know how image filtering influences final classified results and which filtering kernel is optimum. The study design used a series of window sizes to filter the original classified image, and then assess the accuracy of each output map and image quality. We measured the relationship between filtering window size and classification accuracy, and optimized the post-processing techniques of SPOT5satellite images. We conclude that (1) smoothing with the majority filter is sensitive to the information accuracy of soil and water conservation engineering, and (2) for SPOT5 2.5 m image, the 5×5 pixel majority filter is most suitable kernel for extracting information of ecological construction sites in the Loess Plateau of China.

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。

联合目标特征引导与多重注意力的建筑物变化检测

高分遥感影像具有丰富的细节特征,建筑物变化类型多,尺度差异大。针对建筑物变化在复杂环境中易出现空洞和遗漏的问题,本文提出联合目标特征引导与多重注意力的建筑物变化检测方法,通过建筑物目标级引导强化类别信息,实现高分辨影像精细变化信息提取。该方法由建筑物显著增强模块和目标引导的多重注意力模块组成,通过全局深层特征感知与融合,提取建筑物重点区域,结合目标级特征引导和多重自注意力强化特征表达,增强上下文特征相关关系,有效减少细节特征损失,解决目标空洞和边缘不清晰造成的细节损失问题。通过两组试验表明该方法能够提升准确率,在变化种类越多的场景中有效减少变化损失,提高算法稳定性。

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。

面向建筑物轮廓规则化的双路径边界约束与相对论生成对抗网络

高分辨率遥感影像建筑物提取目前仍是遥感应用与地图制图领域的研究热点和难点。尽管深度学习方法的引入极大地提升了建筑物分割的精度,但建筑物分割掩膜中轮廓不规则和边界不清晰的问题依然存在。为了获取规则的建筑物轮廓和清晰的边界,本文基于双路径边界约束与相对论生成对抗网络提出一种建筑物轮廓规则化方法。该网络由双路径边界约束生成器和相对论平均鉴别器共同组成。双路径边界约束生成器通过双路径网络架构和边界损失函数来融合遥感影像和输入标签的边界细节信息来,从而生成规则的建筑物轮廓;而相对论平均鉴别器则通过评估地面实况标签与生成的规则化结果之间的质量差异来迫使生成器生成更为真实的建筑物掩膜。为验证模型性能、探索性能提升原因,本文在WHU建筑物数据集和Inria航空影像标注数据集上设计了对比试验和消融试验。试验结果表明,本文方法可以生成吻合地面实况标签的规则化结果,在解决分割掩膜边界模糊、轮廓不规则的问题上具有显著优势。

注意力机制和全局卷积在光伏板分割中的应用

准确识别光伏对光伏产业有效健康发展至关重要。高分辨率遥感图像复杂的背景和光伏板形状颜色多变给光伏识别带来巨大的挑战。针对高分辨率遥感图像中光伏用地提取问题,提出网络以精确地提取光伏用地。该网络采用编码器和解码器的形式融合多层特征以结合丰富的语义信息,利用全局卷积和双注意力机制捕获重要的空间特征和通道特征,并使用通道融合模块恢复丢失的部分通道信息。提出的方法可以有效解决光伏板边缘模糊和光伏板粘连的问题。在公开光伏数据集上的实验表明,与U-Net、SegNet、DeepLabv3和DeepLabv3+相比,所提方法在PV01、PV03、PV08三个数据集上的IoU分别达到87.02%、92.98%和88.43%。实验证明所提方法能对高分辨率遥感图像光伏板进行高准确率分割。

基于深度学习的高分遥感图像建筑物识别

该文面向高分遥感图像建筑物深度学习检测与识别的具体需求,在归纳和分析现有深度学习与建筑物提取方法的基础上,重点探讨了高分遥感图像建筑物深度学习识别方法和深度学习识别系统,并探讨了未来可能的研究方向。所提方法将为高分遥感图像深度学习目标检测中样本库和遥感数据库的建设提供参考,为利用深度学习开展多尺度、多源高分遥感建筑物检测与识别提供支持。

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。