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37th International Symposium on Remote Sensing of Environment

作者： Pagkalinawan, H. M. Gatdula, N. B. Jose, R. P. Rollan, T. M. Tanada, E. M. Magtalas, S. Y. Dela Torre, D. G. Dela Cruz, P. A. Aves, J. L. Tan, G. D. Apura, R. A. Dimalanta, C. P. Blanco, A. C. Univ Philippines Diliman Natl Engn Ctr Training Ctr Appl Geodesy & Photogrammetry Phil LiDAR 2 Project 1 Agr Resources Extract LIDA Quezon City Philippines Univ Philippines Diliman Coll Engn Dept Geodet Engn Quezon City Philippines

The Agricultural Resources Extraction from LiDAR Surveys (PARMAP) project component of the Nationwide Detailed Resources Assessment using LiDAR (Phil-LiDAR 2) Program aims to produce detailed agricultural maps using LiDAR. Agricultural land cover at crop level was classified through object based image analysis using Support Vector Machine as classifier and LiDAR derivatives from point cloud (2 points per sq.m.) and orthophoto (0.5-meter resolution) as inputs. An accuracy of at least 90%, assessed using validation points from the field and through image interpretation, was required before proceeding to post-processing and map lay-out. Knowledge sharing and capacity development facilitated by the University of the Philippines Diliman (UPD) enabled partner universities across the Philippines to produce outputs for their assigned region. Considering output layers were generated by multiple teams working on different landscape complexities with some degree of data quality variability, quality checking is crucial to ensure accuracy standards were met. UPD PARMap devised a centralized and end-to-end scheme divided into four steps-land classification, GIS post-processing, schema application, and map lay-out. At each step, a block is reviewed and, subsequently, either approved or returned with documentation on required revisions. Turnaround time of review is at least one block (area ranging from 10 to 580 ***.) per day. For coastal municipalities, an additional integration process to incorporate mapped coastal features was applied. Common problems observed during quality checking include misclassifications, gaps between features, incomplete attributes and missing map elements. Some issues are particular to specific blocks such as problematic LiDAR derivatives. UPD addressed these problems through discussion and mentoring visits to partner universities. As of March 2017, a total of 336 municipal agricultural maps have been turned-over to various stakeholders. For the remain

关键词： Agricultural Land Cover Nationwide Mapping Quality Assessment and Control LiDAR object based image analysis

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Optimizing Classification Performance in an object-based Very-High-Resolution Land Use-Land Cover Urban Application 2

Optimizing Classification Performance in an Object-Based Ver...

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Conference on Remote Sensing Technologies and Applications in Urban Environments II

作者： Georganos, Stefanos Grippa, Tais Vanhuysse, Sabine Lennert, Moritz Shimoni, Michal Wolff, Eleonore Univ Libre Bruxelles Inst Environm Management & Land Use Planning IGEA DGES B-1050 Brussels Belgium Royal Mil Acad Signal & Image Ctr B-1000 Brussels Belgium

ISBN: (数字)9781510613270

ISBN: (纸本)9781510613270;9781510613263

This study evaluates the impact of three Feature Selection (FS) algorithms in an object based image analysis (OBIA) framework for Very-High-Resolution (VHR) Land Use-Land Cover (LULC) classification. The three selected FS algorithms, Correlation based Selection (CFS), Mean Decrease in Accuracy (MDA) and Random Forest (RF) based Recursive Feature Elimination (RFE), were tested on Support Vector Machine (SVM), K-Nearest Neighbor, and Random Forest (RF) classifiers. The results demonstrate that the accuracy of SVM and KNN classifiers are the most sensitive to FS. The RF appeared to be more robust to high dimensionality, although an increase in accuracy was found by using the RFE method. In terms of classification accuracy, SVM performed the best using FS, followed by RF and KNN. Finally, only a small number of features is needed to achieve the highest performance using each classifier. This study emphasizes the benefits of rigorous FS for maximizing performance, as well as for minimizing model complexity and interpretation.

关键词： feature selection random forest support vector machine supervised classification object based image analysis

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A MULTI-SENSOR APPROACH FOR VHR VEGETATION MONITORING

A MULTI-SENSOR APPROACH FOR VHR VEGETATION MONITORING

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作者： William O. Weaver THE UNIVERSITY OF HAWAI'I AT MANOA

学位级别：硕士

The Hawaiian Islands are a showcase of biological diversity. With a myriad of vegetation communities, the tropical forests of Hawaii support a rich assemblage of endemic species, some of which are critically endangered. However, much of the Hawaiian forests are degraded and are subject to disturbance by invasive plants. Monitoring the response of Hawaiian forests to management efforts and tracking how vegetation changes over time is a key component of conservation and restoration efforts. Traditional "on-the-ground" vegetation monitoring techniques are time consuming and costly, and can vary in accuracy and consistency. Recent advances in remote sensing technology hold potential for providing an accurate and timely assessment of vegetation at a set point in time. Until recently, the available satellite sensors lacked the spatial resolution required to differentiate individual tree crowns, and thus, classification was limited to the stand or community level. Several new very high resolution (VHR) platforms have emerged in the field of remote sensing that can differentiate individual tree crowns and, thus, have the potential to change the paradigm of vegetation monitoring and its efficacy. VHR, sub-meter imaging platforms are now readily available for public use with commercial VHR satellite and aircraft imaging, unmanned aerial system (UAS) digital imaging, and the Gigapan system. The primary objective of this thesis was to determine the utility of new high spatial resolution remote sensing technologies for vegetation mapping and monitoring in Hawaiian forests. The strengths of the three platforms were evaluated and then combined, to produce an effective synthesis to implement remote sensing-based mapping to the species level and an OBIA procedural workflow was outlined. WV-3 imagery was classified with object based image analysis in eCognition into 7 vegetation classes and validated with UAS and Gigapan imagery. The dense vegetation of the Hawaiian mixed-mesic fores

关键词： Vegetation monitoring Remote sensing Gigapan UAS Satellite object based image analysis

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Selecting patterns and features for between- and within-crop-row weed mapping using UAV-imagery

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 47卷 85-94页

作者： Perez-Ortiz, Maria Manuel Pena, Jose Antonio Gutierrez, Pedro Torres-Sanchez, Jorge Hervas-Martinez, Cesar Lopez-Granados, Francisca CSIC Inst Sustainable Agr POB 4084 Cordoba 14080 Spain Univ Cordoba Dept Comp Sci & Numer Anal Rabanales CampusC2 Bldg E-14071 Cordoba Spain Univ Loyola Andalucia Dept Math & Engn Third Bldg Cordoba 14004 Spain

This paper approaches the problem of weed mapping for precision agriculture, using imagery provided by Unmanned Aerial Vehicles (UAVs) from sunflower and maize crops. Precision agriculture referred to weed control is mainly based on the design of early post -emergence site-specific control treatments according to weed coverage, where one of the most important challenges is the spectral similarity of crop and weed pixels in early growth stages. Our work tackles this problem in the context of object-based image analysis (OBIA) by means of supervised machine learning methods combined with pattern and feature selection techniques, devising a strategy for alleviating the user intervention in the system while not compromising the accuracy. This work firstly proposes a method for choosing a set of training patterns via clustering techniques so as to consider a representative set of the whole field data spectrum for the classification method. Furthermore, a feature selection method is used to obtain the best discriminating features from a set of several statistics and measures of different nature. Results from this research show that the proposed method for pattern selection is suitable and leads to the construction of robust sets of data. The exploitation of different statistical, spatial and texture metrics represents a new avenue with huge potential for between and within crop-row weed mapping via UAV-imagery and shows good synergy when complemented with OBIA. Finally, there are some measures (specially those linked to vegetation indexes) that are of great influence for weed mapping in both sunflower and maize crops. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Remote sensing Unmanned aerial vehicles (UAV) Weed detection object based image analysis

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objectS GROUPING FOR SEGMENTATION OF ROADS NETWORK IN HIGH RESOLUTION imageS OF URBAN AREAS 23

OBJECTS GROUPING FOR SEGMENTATION OF ROADS NETWORK IN HIGH R...

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23rd Congress of the International-Society-for-Photogrammetry-and-Remote-Sensing (ISPRS)

作者： Maboudi, M. Amini, J. Hahn, M. Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran 14174 Iran Univ Appl Sci Dept Geomat Engn Stuttgart Germany

Updated road databases are required for many purposes such as urban planning, disaster management, car navigation, route planning, traffic management and emergency handling. In the last decade, the improvement in spatial resolution of VHR civilian satellite sensors - as the main source of large scale mapping applications - was so considerable that GSD has become finer than size of common urban objects of interest such as building, trees and road parts. This technological advancement pushed the development of "object-based image analysis (OBIA)" as an alternative to pixel-based image analysis methods. Segmentation as one of the main stages of OBIA provides the image objects on which most of the following processes will be applied. Therefore, the success of an OBIA approach is strongly affected by the segmentation quality. In this paper, we propose a purpose-dependent refmement strategy in order to group road segments in urban areas using maximal similarity based region merging. For investigations with the proposed method, we use high resolution images of some urban sites. The promising results suggest that the proposed approach is applicable in grouping of road segments in urban areas.

关键词： Road extraction Segmentation objects grouping object based image analysis Maximal similarity region merging

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ASSESSMENT OF MULTIRESOLUTION SEGMENTATION FOR EXTRACTING GREENHOUSES FROM WORLDVIEW-2 imageRY 23

ASSESSMENT OF MULTIRESOLUTION SEGMENTATION FOR EXTRACTING GR...

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23rd Congress of the International-Society-for-Photogrammetry-and-Remote-Sensing (ISPRS)

作者： Aguilar, M. A. Aguilar, F. J. Garcia Lorca, A. Guirado, E. Betlej, M. Cichon, P. Nemmaoui, A. Vallario, A. Parente, C. Univ Almeria Dept Engn Almeria 04120 Spain Univ Almeria Dept Geog Almeria 04120 Spain Univ Almeria Dept Biol & Geol Almeria 04120 Spain Univ Naples Parthenope Dept Sci & Technol I-80143 Naples Italy

The latest breed of very high resolution (VHR) commercial satellites opens new possibilities for cartographic and remote sensing applications. In this way, object based image analysis (OBIA) approach has been proved as the best option when working with VHR satellite imagery. OBIA considers spectral, geometric, textural and topological attributes associated with meaningful image objects. Thus, the first step of OBIA, referred to as segmentation, is to delineate objects of interest. Determination of an optimal segmentation is crucial for a good performance of the second stage in OBIA, the classification process. The main goal of this work is to assess the multiresolution segmentation algorithm provided by eCognition software for delineating greenhouses from WorldView2 multispectral orthoimages. Specifically, the focus is on finding the optimal parameters of the multiresolution segmentation approach (i.e., Scale, Shape and Compactness) for plastic greenhouses. The optimum Scale parameter estimation was based on the idea of local variance of object heterogeneity within a scene (ESP2 tool). Moreover, different segmentation results were attained by using different combinations of Shape and Compactness values. Assessment of segmentation quality based on the discrepancy between reference polygons and corresponding image segments was carried out to identify the optimal setting of multiresolution segmentation parameters. Three discrepancy indices were used: Potential Segmentation Error (PSE), Number-of-Segments Ratio (NSR) and Euclidean Distance 2 (ED2).

关键词： Segmentation Multiresolution object based image analysis WorldView-2 Scale Shape Compactness Local Variance

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ROAD SIGNS DETECTION AND RECOGNITION UTILIZING imageS AND 3D POINT CLOUD ACQUIRED BY MOBILE MAPPING SYSTEM 23

ROAD SIGNS DETECTION AND RECOGNITION UTILIZING IMAGES AND 3D...

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23rd Congress of the International-Society-for-Photogrammetry-and-Remote-Sensing (ISPRS)

作者： Li, Y. H. Shinohara, T. Satoh, T. Tachibana, K. PASCO Corp Meguro Ku 2-8-10 Higashiyama Tokyo 1530043 Japan

High-definition and highly accurate road maps are necessary for the realization of automated driving, and road signs are among the most important element in the road map. Therefore, a technique is necessary which can acquire information about all kinds of road signs automatically and efficiently. Due to the continuous technical advancement of Mobile Mapping System (MMS), it has become possible to acquire large number of images and 3d point cloud efficiently with highly precise position information. In this paper, we present an automatic road sign detection and recognition approach utilizing both images and 3D point cloud acquired by MMS. The proposed approach consists of three stages: 1) detection of road signs from images based on their color and shape features using object based image analysis method, 2) filtering out of over detected candidates utilizing size and position information estimated from 3D point cloud, region of candidates and camera information, and 3) road sign recognition using template matching method after shape normalization. The effectiveness of proposed approach was evaluated by testing dataset, acquired from more than 180 km of different types of roads in Japan. The results show a very high success in detection and recognition of road signs, even under the challenging conditions such as discoloration, deformation and in spite of partial occlusions.

关键词： Road Signs Detection and Recognition Mobile Mapping System (MMS) object based image analysis Multi-Scale image Segmentation 3D Point Cloud

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Fast Extraction of Roads for Emergencies with Segmentation of Satellite imagery

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Procedia - Social and Behavioral Sciences 2016年 223卷 903-908页

作者： Vincenzo Barrile Giuliana Bilotta DICEAM - Faculty of Engineering “Mediterranea” University of Reggio Calabria Via Graziella Feo di Vito 89100 Reggio Calabria Italy Department of Planning University IUAV of Venice Santa Croce 191 Tolentini 30135 Venice Italy

Among the problems that national and international organizations often face in rescue operations following earthquakes there is the knowledge of the roads that allow aid to reach its destination. During the Haiti earthquake on 2010-01-12 and the Nepal earthquake on 2015-04-25 proved to be very useful the work of volunteers creating maps of the roads on site, useful in case of emergency. Though away from the site of interest, with such crowdsourcingactions,volunteers can contribute to the efforts of mapping inaccessible or little known regions by tracing images from aerial photographs or satellite images, in some cases made available free of charge. The objective of this research, still in progress, is to experience for obtaining very quickly the road network, by extracting it automatically or semi-automatically from satellite data with the object based image analysis, with a segmentation based on Mathematical Morphology.

关键词： Segmentation Satellite imagery Mathematical morphology Gis object based image analysis

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object recognition in hyperspectral images using Binary Partition Tree representation

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PATTERN RECOGNITION LETTERS 2015年 56卷 45-51页

作者： Valero, Silvia Salembier, Philippe Chanussot, Jocelyn Univ Toulouse CESBIO CNES CNRS UMR 5126 IRD Toulouse France Tech Univ Catalonia UPC Barcelona Catalonia Spain Univ Iceland Fac Elect & Comp Engn Reykjavik Iceland Grenoble Inst Technolo4 GIPSA Lab Signal & Image Dept Grenoble France

In this work, an image representation based on Binary Partition Tree is proposed for object detection in hyperspectral images. This hierarchical region-based representation can be interpreted as a set of hierarchical regions stored in a tree structure, which succeeds in presenting: (i) the decomposition of the image in terms of coherent regions and (ii) the inclusion relations of the regions in the scene. Hence, the BPT representation defines a search space for constructing a robust object identification scheme. Spatial and spectral information are integrated in order to analyze hyperspectral images with a region based perspective. For each region represented in the BPT, spatial and spectral descriptors are computed and the likelihood that they correspond to an instantiation of the object of interest is evaluated. Experimental results demonstrate the good performances of this BPT-based approach. (C) 2015 Elsevier B.V. All rights reserved.

关键词： object based image analysis Binary Partition Tree Hyperspectral images object recognition

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The Effects of Point or Polygon based Training Data on RandomForest Classification Accuracy of Wetlands

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REMOTE SENSING 2015年第4期7卷 4002-4025页

作者： Corcoran, Jennifer Knight, Joseph Pelletier, Keith Rampi, Lian Wang, Yan Minnesota Dept Nat Resources Div Forestry St Paul MN 55108 USA Univ Minnesota Dept Forest Resources St Paul MN 55108 USA

Wetlands are dynamic in space and time, providing varying ecosystem services. Field reference data for both training and assessment of wetland inventories in the State of Minnesota are typically collected as GPS points over wide geographical areas and at infrequent intervals. This status-quo makes it difficult to keep updated maps of wetlands with adequate accuracy, efficiency, and consistency to monitor change. Furthermore, point reference data may not be representative of the prevailing land cover type for an area, due to point location or heterogeneity within the ecosystem of interest. In this research, we present techniques for training a land cover classification for two study sites in different ecoregions by implementing the RandomForest classifier in three ways: (1) field and photo interpreted points;(2) fixed window surrounding the points;and (3) image objects that intersect the points. Additional assessments are made to identify the key input variables. We conclude that the image object area training method is the most accurate and the most important variables include: compound topographic index, summer season green and blue bands, and grid statistics from LiDAR point cloud data, especially those that relate to the height of the return.

关键词： wetlands optical and infrared sensors topographic LiDAR object based image analysis segmentation

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