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2018 ISPRS TC II Mid-term Symposium "Towards Photogrammetry 2020"

作者： Oliveira, R.A. Khoramshahi, E. Suomalainen, J. Hakala, T. Viljanen, N. Honkavaara, E. Finnish Geospatial Research Institute FGI Finland University of Helsinki Department of Computer Science Finland

The use of drones and photogrammetric technologies are increasing rapidly in different applications. Currently, drone processing workflow is in most cases based on sequential image acquisition and post-processing, but there are great interests towards real-time solutions. Fast and reliable real-time drone data processing can benefit, for instance, environmental monitoring tasks in precision agriculture and in forest. recent developments in miniaturized and low-cost inertial measurement systems and GNSS sensors, and Real-time kinematic (RTK) position data are offering new perspectives for the comprehensive remote sensing applications. The combination of these sensors and light-weight and low-cost multi- or hyperspectral frame sensors in drones provides the opportunity of creating near real-time or real-time remote sensing data of target object. We have developed a system with direct georeferencing onboard drone to be used combined with hyperspectral frame cameras in real-time remote sensing applications. The objective of this study is to evaluate the real-time georeferencing comparing with post-processing solutions. Experimental data sets were captured in agricultural and forested test sites using the system. The accuracy of onboard georeferencing data were better than 0.5 m. The results showed that the real-time remote sensing is promising and feasible in both test sites. © Authors 2018. CC BY 4.0 License.

关键词： remote sensing

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Impact of Deep Learning in image processing and Computer Vision

Impact of Deep Learning in Image Processing and Computer Vis...

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International Conference on Microelectronics, Electromagnetics and Telecommunications, ICMEET 2017

作者： Goswami, Tilottama Department of Computer Science and Engineering BVRIT Hyderabad College of Engineering for Women Telangana500 090 India

ISBN: (纸本)9789811073281

With deep learning techniques, a revolution has taken place in the field of image processing and computer vision. The survey paper emphasizes the importance of representation learning methods for machine learning tasks. Deep learning, the modern machine learning is commonly used in the vision tasks—semantic segmentation, image captioning, object detection, recognition, and image classification. The paper focuses on the recent developments in the domain of remote sensing, retinal image understanding, and scene understanding based on newly proposed deep architectures. The author finds it quite intriguing of the classical building blocks of image segmentation (Gabor, K-means), shifting gear, and contributing to image recognition tasks based on deep learning (Gabor convolutional network, K-means dictionary learning). The survey makes an attempt to serve as a concise guide in providing latest works in computer vision applications based on deep learning and giving futuristic insights. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Computer vision

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The application of Chinese high-spatial-resolution remote sensing satellite image in land law enforcement information extraction

The application of Chinese high-spatial-resolution remote se...

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2018 ISPRS TC III Mid-Term Symposium on developments, Technologies and applications in remote sensing

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

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 2m, 1m and 2.1m and the multispectral images (MS) resolution are 8m, 4m, 5.8m 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 ArcGIS 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. © Authors 2018. CC BY 4.0 License.

关键词： Land use

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CLOUD DETECTION by FUSING MULTI-SCALE CONVOLUTIONAL FEATURES

CLOUD DETECTION by FUSING MULTI-SCALE CONVOLUTIONAL FEATURES

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2018 ISPRS TC III Mid-Term Symposium on developments, Technologies and applications in remote sensing

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

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. © Authors 2018.

关键词： Cloud detection Convolutional feature fusion Deep learning MSCN Multi-scale

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DESIGN AND VERIFICATION OF remote sensing image DATA CENTER STORAGE ARCHITECTURE BASED ON HADOOP

DESIGN AND VERIFICATION OF REMOTE SENSING IMAGE DATA CENTER ...

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

作者： Dejin Tang Xiaoming Zhou Yang Jing Wang Cong Caiping Li National Geomatics Center of China Beijing Institute of Remote Sensing Information

The data center is a new concept of data processing and application proposed in recent years. It is a new method of processing technologies based on data, parallel computing, and compatibility with different hardware clusters. While optimizing the data storage management structure, it fully utilizes cluster resource computing nodes and improves the efficiency of data parallel application. This paper used mature Hadoop technology to build a large-scale distributed image management architecture for remote sensing imagery. Using Map Reduce parallel processing technology, it called many computing nodes to process image storage blocks and pyramids in the background to improve the efficiency of image reading and application and sovled the need for concurrent multi-user high-speed access to remotely sensed data. It verified the rationality, reliability and superiority of the system design by testing the storage efficiency of different image data and multi-users and analyzing the distributed storage architecture to improve the application efficiency of remote sensing images through building an actual Hadoop service system.

关键词： Data center Storage strategy Parallel processing Hadoop HBase

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Urban remote sensing Second edition.

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丛书名： remote sensing applications Series

2018年

作者： Qihao Weng Dale A Quattrochi Paolo E Gamba

ISBN: (数字)9780429888564

ISBN: (纸本)9781138054608

Urban remote sensing, Second Edition assembles a team of professional experts to provide a much-needed update on the applications of remote sensing technology to urban and suburban areas. This book reflects new developments in spaceborne and airborne sensors, image processing methods and techniques, and wider applications of urban remote sensing to meet societal and economic challenges. In various sections of the book the authors address methods for upscaling urban feature extraction to the global scale, new methods in mapping and detecting urban landscape features and structures, and mapping and monitoring urbanization in developing countries. Additionally, readers are provided with valuable case studies such as the HEAT (Heat Energy Assessment Technologies) project in Calgary, Canada and the use of VHR (very high resolution) satellite monitoring in Salzburg, Austria to tackle challenges of urban green planning. Features Explores the most up-to-date developments in the field of urban remote sensing Integrates both technical and practical aspects covering all different topics of global urban growth issues Provides new and updated contributions addressing data mining of remotely sensed big data, recent urban studies on a global scale, accuracy assessment and validation, and new technical challenges Examines various applications of urban remote sensing in support of urban planning, environmental management, and sustainable urban development Authors are renowned figures in the field of remote sensing

关键词： agriculture \u0026 environmental sciences

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How do you find the green sheep? A critical review of the use of remotely sensed imagery to detect and count animals

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METHODS IN ECOLOGY AND EVOLUTION 2018年第4期9卷 881-892页

作者： Hollings, Tracey Burgman, Mark van Andel, Mary Gilbert, Marius Robinson, Timothy Robinson, Andrew Univ Melbourne Sch Biosci Ctr Excellence Biosecur Risk Anal Melbourne Vic Australia Imperial Coll London Ctr Environm Policy London England Minist Primary Ind Wellington New Zealand Univ Libre Bruxelles Spatial Epidemiol Lab Brussels Belgium Fonds Natl Rech Sci Brussels Belgium Food & Agr Org United Nations Rome Italy

1. Animal abundance data are essential for endangered species conservation, tracking invasive species spread, biosecurity, agricultural applications and wildlife monitoring;however, obtaining abundance data are a perennial challenge. recent improvements in the resolution of remotely sensed imagery, and image-processing tools and software have facilitated improvement of methods for the detection of individual, generally large-bodied animals. The potential to monitor and survey populations from remotely sensed imagery is an exciting new development in animal ecology. 2. We review the methods used to analyse remotely sensed imagery for their potential to estimate the abundance of wild and domestic animal populations by directly detecting, identifying and counting individuals. *** many illustrative studies using a variety of methods for detecting animals from remotely sensed imagery, it remains problematic in many situations. Studies that demonstrated reasonably high accuracy using automated and semi-automated techniques have been undertaken on small spatial scales relative to the geographical range of the species of interest and/or in homogenous environments such as sea ice. The major limitations are the relatively low accuracy of automated detection techniques across large spatial extents, false detections and the cost of high-resolution data. 4. Future developments in the analysis of remotely sensed data for population surveys will improve detection capabilities, including the advancement of algorithms, the crossover of software and technology from other disciplines, and improved availability, accessibility, cost and resolution of data.

关键词： agriculture animals detection population survey remote sensing satellite imagery wildlife

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Targeted change detection in remote sensing images

arXiv

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arXiv 2018年

作者： Ignatiev, Vladimir Trekin, Alexey Lobachev, Viktor Potapov, Georgy Burnaev, Evgeny Skolkovo institute of Science and technology Nobel st. 1 Moscow Russia

recent developments in the remote sensing systems and image processing made it possible to propose a new method of the object classification and detection of the specific changes in the series of satellite Earth images (so called targeted change detection). In this paper we propose a formal problem statement that allows to use effectively the deep learning approach to analyze time-dependent series of remote sensing images. We also introduce a new framework for the development of deep learning models for targeted change detection and demonstrate some cases of business applications it can be used for. Copyright © 2018, The Authors. All rights reserved.

关键词： remote sensing

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INFLUENCE OF SKY CONDITIONS ON ESTIMATION OF PHOTOSYNTHETIC PHOTON FLUX DENSITY FOR AGRICULTURAL ECOSYSTEM

INFLUENCE OF SKY CONDITIONS ON ESTIMATION OF PHOTOSYNTHETIC ...

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

作者： M.Yamashita M.Yoshimura Tokyo University of Agriculture and Technology Faculty of Agriculture

Photosynthetic photon flux density(PPFD: mmol m s) is indispensable for plant physiology processes in photosynthesis. However, PPFD is seldom measured, so that PPFD has been estimated by using solar radiation(SR: W m) measured in world wide. In method using SR, there are two steps: first to estimate photosynthetically active radiation(PAR: W m) by the fraction of PAR to SR（PF） and second: to convert PAR to PPFD using the ratio of quanta to energy(Q/E: mmol J). PF and Q/E usually have been used as the constant values, however, recent studies point out that PF and Q/E would not be constants under various sky conditions. In this study, we use the numeric data of sky-conditions factors such cloud cover, sun appearance/hiding and relative sky brightness derived from whole-sky image processing and examine the influences of sky-conditions factors on PF and Q/E of global and diffuse PAR. Furthermore, we discuss our results by comparing with the existing methods.

关键词： PPFD PAR Solar radiation PAR fraction Quantum-to-energy ratio Diffuse ratio Clearness index Sky conditions

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WATER MAPPING USING MULTISPECTRAL AIRBORNE LIDAR DATA

WATER MAPPING USING MULTISPECTRAL AIRBORNE LIDAR DATA

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

作者： WaiYeung Yan Ahmed Shaker Paul E.La Rocque Department of Civil Engineering Ryerson University

This study investigates the use of the world's first multispectral airborne Li DAR sensor, Optech Titan, manufactured by Teledyne Optech to serve the purpose of automatic land-water classification with a particular focus on near shore region and river *** there exist recent studies utilizing airborne Li DAR data for shoreline detection and water surface mapping, the majority of them only perform experimental testing on clipped data subset or rely on data fusion with aerial/satellite image. In addition, most of the existing approaches require manual intervention or existing tidal/datum data for sample collection of training data. To tackle the drawbacks of previous approaches, we propose and develop an automatic data processing workflow for land-water classification using multispectral airborne Li DAR data. Depending on the nature of the study scene, two methods are proposed for automatic training data selection. The first method utilizes the elevation/intensity histogram fitted with Gaussian mixture model(GMM) to preliminarily split the land and water bodies. The second method mainly relies on the use of a newly developed scan line elevation intensity ratio(SLIER)to estimate the water surface data points. Regardless of the training methods being used, feature spaces can be constructed using the multispectral Li DAR intensity, elevation and other features derived from these parameters. The comprehensive workflow was tested with two datasets collected for different near shore region and river environment, where the overall accuracy yielded better than 96%.

关键词： Land Cover Classification Multispectral LiDAR Optech Titan Point Cloud processing SLIER

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