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The ISPRS Technical Commission III Midterm Symposium on "developments, Technologies and applications in remote sensing"

作者： Ningna Wang Rujun Yang Land and Resources Information Center of Guangxi Zhuang Autonomous Region

Chinese high-resolution(HR) remote sensing satellites have made huge leap in the past decade. Commercial satellite datasets, such as GF-1, GF-2 and ZY-3 images, the panchromatic images(PAN) resolution of them are 2 m, 1 m and 2.1 m and the multispectral images(MS) resolution are 8 m, 4 m, 5.8 m respectively have been emerged in recent years. Chinese HR satellite imagery has been free downloaded for public welfare purposes using. Local government began to employ more professional technician to improve traditional land management technology. This paper focused on analysing the actual requirements of the applications in government land law enforcement in Guangxi Autonomous Region. 66 counties in Guangxi Autonomous Region were selected for illegal land utilization spot extraction with fusion Chinese HR images. The procedure contains: A. Defines illegal land utilization spot type. B. Data collection, GF-1, GF-2, and ZY-3 datasets were acquired in the first half year of 2016 and other auxiliary data were collected in 2015. C. Batch process, HR images were collected for batch preprocessing through ENVI/IDL tool. D. Illegal land utilization spot extraction by visual interpretation. E. Obtaining attribute data with Arc GIS Geoprocessor(GP) model. F. Thematic mapping and surveying. Through analysing 42 counties results, law enforcement officials found 1092 illegal land using spots and 16 suspicious illegal mining spots. The results show that Chinese HR satellite images have great potential for feature information extraction and the processing procedure appears robust.

关键词： land use high resolution batch process geoprocessing model information extraction mapping

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CLOUD DETECTION BY FUSING MULTI-SCALE CONVOLUTIONAL FEATURES

CLOUD DETECTION BY FUSING MULTI-SCALE CONVOLUTIONAL FEATURES

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The ISPRS Technical Commission III Midterm Symposium on "developments, Technologies and applications in remote sensing"

作者： Zhiwei Li Huanfeng Shen Yancong Wei Qing Cheng Qiangqiang Yuan School of Resource and Environmental Sciences Wuhan University Collaborative Innovation Center of Geospatial Technology State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan University School of Urban Design of Wuhan University Wuhan University School of Geodesy and Geomatics Wuhan University

Clouds detection is an important pre-processing step for accurate application of optical satellite imagery. recent studies indicate that deep learning achieves best performance in image segmentation tasks. Aiming at boosting the accuracy of cloud detection for multispectral imagery, especially for those that contain only visible and near infrared bands, in this paper, we proposed a deep learning based cloud detection method termed MSCN(multi-scale cloud net), which segments cloud by fusing multi-scale convolutional features. MSCN was trained on a global cloud cover validation collection, and was tested in more than ten types of optical images with different resolution. Experiment results show that MSCN has obvious advantages over the traditional multi-feature combined cloud detection method in accuracy, especially when in snow and other areas covered by bright non-cloud objects. Besides, MSCN produced more detailed cloud masks than the compared deep cloud detection convolution network. The effectiveness of MSCN make it promising for practical application in multiple kinds of optical imagery.

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SPATIAL RESOLUTION ASSESSMENT OF THE TELOPS AIRBORNE TIR imageRY 2

SPATIAL RESOLUTION ASSESSMENT OF THE TELOPS AIRBORNE TIR IMA...

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ISPRS Int Joint Conf of 2nd Conf on Geospatial Information Research GI Research, 4th Conf on Sensors and Models in Photogrammetry and remote sensing (SMPR), 6th Conf on Earth Observation of Environmental Changes (EOEC)

作者： Mousakhani, S. Eslami, M. Saadatseresht, M. Univ Tehran Sch Surveying & Geospatial Engn Tehran Iran

Having a high spatial resolution of Thermal InfraRed (TIR) Sensors is a challenge in remote sensing applications. Airborne high spatial resolution TIR is a novel source of data that became available lately. recent developments in spatial resolution of the TIR sensors have been an interesting topic for scientists. TIR sensors are very sensitive to the energies emitted from objects. Past researches have been shown that increasing the spatial resolution of an airborne image will decrease the spectral content of the data and will reduce the Signal to Noise Ratio (SNR). Therefore, in this paper a comprehensive assessment is adapted to estimate an appropriate spatial resolution of the TIR data (TELOPS TIR data), in consideration of the SNR. So, firstly, a low-pass filter is applied on TIR data and the achieved products fed to a classification method for analysing of the accuracy improvement. The obtained results show that, there is no significant change in classification accuracy by applying low-pass filter. Furthermore, estimation of the appropriate spatial resolution of the TIR data is evaluated for obtaining higher spectral content and SNR. For this purpose, different resolutions of the TIR data are created and fed to the maximum likelihood classification method separately. The results illustrated in the case of using images with ground pixel size four times greater than the original image, the classification accuracy is not reduced. Also, SNR and spectral contents are improved. But the corners sharpening is declined.

关键词： Thermal InfraRed SNR image processing Classification Spatial resolution Spectral content

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Learning Approaches for Large-Scale remote sensing image Classification

Learning Approaches for Large-Scale Remote Sensing Image Cla...

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作者： Emmanuel Maggiori Universite Cote d'Azur

学位级别：博士

The analysis of airborne and satellite images is one of the core subjects in remote sensing. In recent years, technological developments have facilitated the availability of large-scale sources of data, which cover significant extents of the earth's surface, often at impressive spatial resolutions. In addition to the evident computational complexity issues that arise, one of the current challenges is to handle the variability in the appearance of the objects across different geographic regions. For this, it is necessary to design classification methods that go beyond the analysis of individual pixel spectra, introducing higher-level contextual information in the process. In this thesis, we first propose a method to perform classification with shape priors, based on the optimization of a hierarchical subdivision data structure. We then delve into the use of the increasingly popular convolutional neural networks (CNNs) to learn deep hierarchical contextual features. We investigate CNNs from multiple angles, in order to address the different points required to adapt them to our problem. Among other subjects, we propose different solutions to output high-resolution classification maps and we study the acquisition of training data. We also created a dataset of aerial images over dissimilar locations, and assess the generalization capabilities of CNNs. Finally, we propose a technique to polygonize the output classification maps, so as to integrate them into operational geographic information systems, thus completing the typical processing pipeline observed in a wide number of applications. Throughout this thesis, we experiment on hyperspectral, satellite and aerial images, with scalability, gen- eralization and applicability goals in mind.

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GEOSPATIAL DATA processing FOR 3D CITY MODEL GENERATION, MANAGEMENT AND VISUALIZATION

GEOSPATIAL DATA PROCESSING FOR 3D CITY MODEL GENERATION, MAN...

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ISPRS Hannover Workshop / High-Resolution Earth Imaging for Geospatial Information (HRIGI) Workshop / City Models, Roads and Traffic (CMRT) Workshop / image Sequence Analysis (ISA) Workshop / European Calibration and Orientation (EuroCOW) Workshop

作者： Toschi, I. Nocerino, E. Remondino, F. Revolti, A. Soria, G. Piffer, S. Bruno Kessler Fdn FBK Opt Metrol Unit 3DOM 3D Trento Italy Trilogis Srl Trento Italy

recent developments of 3D technologies and tools have increased availability and relevance of 3D data (from 3D points to complete city models) in the geospatial and geo-information domains. Nevertheless, the potential of 3D data is still underexploited and mainly confined to visualization purposes. Therefore, the major challenge today is to create automatic procedures that make best use of available technologies and data for the benefits and needs of public administrations (PA) and national mapping agencies (NMA) involved in "smart city" applications. The paper aims to demonstrate a step forward in this process by presenting the results of the SENECA project (Smart and SustaiNablE City from Above - http://***). State-of-the-art processing solutions are investigated in order to (i) efficiently exploit the photogrammetric workflow (aerial triangulation and dense image matching), (ii) derive topologically and geometrically accurate 3D geo-objects (i.e. building models) at various levels of detail and (iii) link geometries with non-spatial information within a 3D geo-database management system accessible via web-based client. The developed methodology is tested on two case studies, i.e. the cities of Trento (Italy) and Graz (Austria). Both spatial (i.e. nadir and oblique imagery) and non-spatial (i.e. cadastral information and building energy consumptions) data are collected and used as input for the project workflow, starting from 3D geometry capture and modelling in urban scenarios to geometry enrichment and management within a dedicated webGIS platform.

关键词： aerial imagery 3D building models 3D city model mesh 3D geo-database web-GIS smart city

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多时相遥感影像变化检测的现状与展望

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测绘学报 2017年第10期46卷 1447-1459页

作者：张良培武辰武汉大学测绘遥感信息工程国家重点实验室湖北武汉430079 武汉大学国际软件学院湖北武汉430079

多时相遥感影像变化检测技术能够监测生态环境变化、跟踪城市发展,对于研究人类与自然环境之间的交互关系有着重要的意义。随着新型遥感影像的不断普及,变化检测也在高光谱影像变化检测和高分辨率影像变化检测两个方向上有了深入的探索... 详细信息

多时相遥感影像变化检测技术能够监测生态环境变化、跟踪城市发展,对于研究人类与自然环境之间的交互关系有着重要的意义。随着新型遥感影像的不断普及,变化检测也在高光谱影像变化检测和高分辨率影像变化检测两个方向上有了深入的探索。本文围绕变化检测的基本流程,从预处理、变化检测方法、阈值分割与精度评价4个角度介绍了变化检测研究的最新进展,并总结了变化检测技术的主要应用领域。最后,对变化检测技术未来的发展进行了展望。

关键词：变化检测遥感影像多时相高分辨率高光谱

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3D change detection - Approaches and applications

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND remote sensing 2016年第Dec.期122卷 41-56页

作者： Qin, Rongjun Tian, Jiaojiao Reinartz, Peter Ohio State Univ Dept Civil Environm & Geodet Engn 2186 Bolz Hall2036 Neil Ave Columbus OH 43210 USA Ohio State Univ Dept Elect & Comp Engn 205 Dreese Labs2015 Neil Ave Columbus OH 43210 USA German Aerosp Ctr DLR Remote Sensing Technol Inst IMF D-82234 Wessling Germany

Due to the unprecedented technology development of sensors, platforms and algorithms for 3D data acquisition and generation, 3D spaceborne, airborne and close-range data, in the form of image based, Light Detection and Ranging (LiDAR) based point clouds, Digital Elevation Models (DEM) and 3D city models, become more accessible than ever before. Change detection (CD) or time-series data analysis in 3D has gained great attention due to its capability of providing volumetric dynamics to facilitate more applications and provide more accurate results. The state-of-the-art CD reviews aim to provide a comprehensive synthesis and to simplify the taxonomy of the traditional remote sensing CD techniques, which mainly sit within the boundary of 2D image/spectrum analysis, largely ignoring the particularities of 3D aspects of the data. The inclusion of 3D data for change detection (termed 3D CD), not only provides a source with different modality for analysis, but also transcends the border of traditional top-view 2D pixel/object-based analysis to highly detailed, oblique view or voxel-based geometric analysis. This paper reviews the recent developments and applications of 3D CD using remote sensing and close-range data, in support of both academia and industry researchers who seek for solutions in detecting and analyzing 3D dynamics of various objects of interest. We first describe the general considerations of 3D CD problems in different processing stages and identify CD types based on the information used, being the geometric comparison and geometric-spectral analysis. We then summarize relevant works and practices in urban, environment, ecology and civil applications, etc. Given the broad spectrum of applications and different types of 3D data, we discuss important issues in 3D CD methods. Finally, we present concluding remarks in algorithmic aspects of 3D CD. (C) 2016 International Society for Photogrammetry and remote sensing, Inc. (ISPRS). Published by Elsevier B.V. All

关键词： 3D change detection Digital surface models Oblique images LiDAR Land-cover classification Very high resolution

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remote sensing of Landscape Change in Permafrost Regions

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PERMAFROST AND PERIGLACIAL PROCESSES 2016年第4期27卷 324-338页

作者： Jorgenson, Mark Torre Grosse, Guido Alaska Ecosci Fairbanks AK 99709 USA Helmholtz Ctr Polar & Marine Res Alfred Wegener Inst Potsdam Germany

Amplification of global warming in Arctic and boreal regions is causing significant changes to permafrost-affected landscapes. The nature and extent of the change is complicated by ecological responses that take place across strong gradients in environmental conditions and disturbance regimes. Emerging remote sensing techniques based on a growing array of satellite and airborne platforms that cover a wide range of spatial and temporal scales increasingly allow robust detection of changes in permafrost landscapes. In this review, we summarise recent developments (2010-15) in remote sensing applications to detect and monitor landscape changes involving surface temperatures, snow cover, topography, surface water, vegetation cover and structure, and disturbances from fire and human activities. We then focus on indicators of degrading permafrost, including thermokarst lakes and drained lake basins, thermokarst bogs and fens, thaw slumps and active-layer detachment slides, thermal erosion gullies, thermokarst pits and troughs, and coastal erosion and flooding. Our review highlights the expanding sensor capabilities, new image processing and multivariate analysis techniques, enhanced public access to data and increasingly long image archives that are facilitating novel insights into the multi-decadal dynamics of permafrost landscapes. remote sensing methods that appear especially promising for change detection include: repeat light detection and ranging, interferometric synthetic aperture radar and airborne geophysics for detecting topographic and subsurface changes;temporally dense analyses at high spatial resolution;and multi-sensor data fusion. remotely sensed data are also becoming used more frequently as driving parameters in permafrost model and mapping schemes. Copyright (c) 2016 John Wiley & Sons, Ltd.

关键词： remote sensing landscape change permafrost ecosystem thermokarst

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Evaluation and predictability of water erosion based on spectral information analysis 2

Evaluation and predictability of water erosion based on spec...

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2nd International Conference on Advanced Technologies for Signal and image processing (ATSIP)

作者： Rabah, Z. B. Farah, I. R. Sch Engn & Comp Sci RIADI GDL Lab Manouba Tunisia ENST Bretagne ITI Lab Technopole Brest Iroise CS 83818 F-29238 Brest France

ISBN: (纸本)9781467385268

The recent advances of hyperspectral remote sensing systems lead to a vastly improved ability to classify land-cover classes that are very similar in their spectral properties. In addition, Hyperspectral Imaging has cleared the path for a wide range of applications in the field of geology for the regional mapping of rock types and mineral prospecting. The erosion process that begins with changes in surface states causes structural degradation, manifested by the increase of the fine fraction of the surface and installation of film and slaking crusts. This structural degradation increases flow, stripping and erosion of surface horizons, thereby changing physico-chemical and organic soils and deposits. In the study area ( watershed of Oued El Fard in the South West of Tunisia), an uncontrolled exploitation of land led to widespread development process gully erosion that led to the formation of Badlands. This study aims to propose a method, based on hyperspectral imaging and spectroscopy and new developments in hyperspectral remote sensing for spectral characterization of surface soil eroded to formally assess the extent, distribution and intensity of this phenomenon

关键词： remote sensing water erosion surface hyperspectral image Hyperion image

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VIRTUALIZING SUPER-COMPUTATION ON-BOARD UAS 36

VIRTUALIZING SUPER-COMPUTATION ON-BOARD UAS

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36th International Symposium on remote sensing of the Environment (ISRSE)

作者： Salami, E. Soler, J. A. Cuadrado, R. Barrado, C. Pastor, E. Tech Univ Catalonia UPC Comp Architecture Dept Castelldefels 08860 Spain

ISBN: (纸本)9781629935201

Unmanned aerial systems (UAS, also known as UAV, RPAS or drones) have a great potential to support a wide variety of aerial remote sensing applications. Most UAS work by acquiring data using on-board sensors for later post-processing. Some require the data gathered to be downlinked to the ground in real-time. However, depending on the volume of data and the cost of the communications, this later option is not sustainable in the long term. This paper develops the concept of virtualizing super-computation on-board UAS, as a method to ease the operation by facilitating the downlink of high-level information products instead of raw data. Exploiting recent developments in miniaturized multi-core devices is the way to speed-up on-board computation. This hardware shall satisfy size, power and weight constraints. Several technologies are appearing with promising results for high performance computing on unmanned platforms, such as the 36 cores of the TILE-Gx36 by Tilera (now EZchip) or the 64 cores of the Epiphany-IV by Adapteva. The strategy for virtualizing super-computation on-board includes the benchmarking for hardware selection, the software architecture and the communications aware design. A parallelization strategy is given for the 36-core TILE-Gx36 for a UAS in a fire mission or in similar target-detection applications. The results are obtained for payload image processing algorithms and determine in real-time the data snapshot to gather and transfer to ground according to the needs of the mission, the processing time, and consumed watts.

关键词： UAS remote sensing super-computing parallelization image processing benchmarking virtualization

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