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International-Society-for-Photogrammetry-and-Remote-Sensing Hannover Workshop

作者： Djerriri, K. Malki, M. Ctr Spatial Tech Div Earth Observat Arzew Oran Algeria Univ Djillali Liabes Sidi Bel Abbes EEDIS Lab Sidi Bel Abbes Algeria

ISBN: (纸本)9781629935201

The success of the object-based image analysis (OBIA) paradigm can be attributed to the fact that regions obtained by means of segmentation process are depicted with a variety of spectral, shape, texture and context characteristics. These representative objects-attributes can be assigned to different land-cover/land-use types by means of two options. The first is to use supervised classifiers such as K-nearest neighbors (KNN) and Support Vector Machine (SVM), the second is to create classification rules. Supervised classifiers perform very well and have generally higher accuracies. However one of their drawbacks is that they provide no explicit knowledge in understandable and interpretable forms. The building of the rule set is generally based on the domain expert knowledge when dealing with a small number of classes and a small number of attributes, but having a dozens of continuously valued attributes attached to each image object makes it a tedious task and experts quickly get overwhelmed and become totally helpless. This is where data mining techniques for knowledge discovering help to understand the hidden relationships between classes and their attached attributes. The aim of this paper is to highlight the benefits of using knowledge discovery and data-mining tools, especially rule induction algorithms for useful and accurate information extraction from high spatial resolution remotely sensed imagery.

关键词： object-based image analysis Data Mining Classification rule Induction Genetic Algorithm

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Remote sensing methods to monitor habitats potentially threatened by climate change

Remote sensing methods to monitor habitats potentially threa...

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Conference on Earth Resources and Environmental Remote Sensing/GIS Applications IV

作者： Foerster, Michael Schmidt, Tobias Spindler, Nadine Renner, Kathrin Wagner-Luecker, Iris Zebisch, Marc Neubert, Marco Tech Univ Berlin Geoinformat Environm Planning Lab Dept Landscape Architecture & Environm Planning D-10623 Berlin Germany

ISBN: (纸本)9780819497635

The recognition and monitoring of vegetation and habitats for nature conservation is a vital point of research within the remote sensing community. It has been agreed on that there is no general solution on deriving information on habitats due to different data availability and spectral as well as textural behaviour of habitat main types (e.g. woodlands, grasslands, etc.). Therefore the monitoring should be rather multi-scale, versatile, user-friendly, and cost-efficient for predefined indicators. In the presented study, five Central European test sites of natural vegetation communities in an Alpine area (1), a temperate forest (2), a Pannonian grassland (3), a shallow lake (4) and a Carpathian grassland (5) have been investigated by multi-temporal remote sensing. For these studies, different time-series of RapidEye images from the years 2009 to 2011 and LandSat data from the year 1984 to 2010 were acquired. The amount of the images was depending on required acquisitions dates as well as weather conditions. The definition of the indicators was relying on the available ground truth data as well as the demands und judgement of the managing authorities in the nature conservation areas. The selected methods for deriving of the indicators depend on the time-series as well as the available calibration and validation data. The techniques vary between unsupervised classification, object-based approaches and supervised classification methods with algorithms such as support vector machines (e.g. SVM) or classification trees (e.g. See5). Often, the named methods are utilized in combined approaches. The resulting indicators for the monitoring are shrub encroachment (for 1), share of naturally occurring tree type (for 2), differentiation of grassland types (for 3 and 5) and the changing extent of a reed belt (for 4). All indicators seem to be valid and useful for the authorities within the conservation areas. In this article a specific example of the share of natural tree types

关键词： SVM object-based image analysis biodiversity indicators Central Europe RapidEye

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Extracting Municipal Solid Waste Dumps based on High Resolution images

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SPECTROSCOPY AND SPECTRAL analysis 2013年第8期33卷 2024-2030页

作者： Zhang Fang-li Du Shi-hong Guo Zhou Peking Univ Inst Remote Sensing Beijing 100871 Peoples R China Peking Univ GIS Beijing 100871 Peoples R China

The dramatically increasing informal MSW dumps are endangering the urban environment. Remote sensing (RS) technologies are more efficient to monitor and manage municipal solid wastes (MSW) than traditional survey-based methods. In high spatial resolution remotely sensed images, these irregularly distributed dumps have complex compositions and strong heterogeneities, thus it is still hard to extract them automatically no matter the pixel-or object-based image analysis method is used. Therefore, based on the analysis of MSW characteristics, the present study develops a multiresolution strategy to extract MSW dumps by combining image features at both high resolution and resampled lowheterogeneity images, while the high resolution images can provide detailed information and the low resolution images can suppress the strong heterogeneities of informal MSW dumps. Taking the QuickBird image covering part of Beijing as an example, this multi-resolution strategy produced a high accuracy (75%), indicating that this multi-resolution strategy is quite effective for extracting the open-air informal MSW dumps.

关键词： Municipal solid waste Highresolution remote sensing Multi-resolution object-based image analysis

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Automatic Moving Vehicles Information Extraction From Single-Pass WorldView-2 imagery

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2012年第1期5卷 135-145页

作者： Salehi, Bahram Zhang, Yun Zhong, Ming Univ New Brunswick Dept Geodesy & Geomat Engn Fredericton NB Canada Univ New Brunswick Dept Civil Engn Fredericton NB E3B 5A3 Canada Univ Calgary Inst Adv Policy Res IAPR Calgary AB T2N 1N4 Canada Univ Regina Regina SK S4S 0A2 Canada

Because of the sub-meter spatial resolution of very high resolution (VHR) optical satellite imagery, vehicles can be identified in this type of imagery. Further, because there is a time lag in image collection between the Panchromatic (Pan) and multispectral (MS) sensors onboard VHR satellites, a moving vehicle is observed by the satellite at slightly different times. Consequently, its velocity information including speed and direction can be determined. The higher spatial resolution and more spectral bands of WorldView-2 (WV2) imagery, compared to those of previous VHR satellites such as QuickBird and GeoEye-1, together with the new sensors' configuration of WV2, i.e., 4 bands on each side of the Pan sensor (MS1 and MS2), adds an opportunity to improve both moving vehicles extraction and the velocity estimation. In this paper, a novel processing framework is proposed for the automatic extraction of moving vehicles and determination of their velocities using single-pass WV2 imagery. The approach contains three major components: a) object-based road extraction, b) moving vehicle extraction from MS1 and MS2, and c) velocity estimation. The method was tested on two different areas of a WV2 image, a high speed and a low speed traffic zone. The method resulted in a correctness of 92% and a completeness of 77% for the extraction of moving vehicles. Furthermore, the estimated speeds and directions are very realistic and are consistent with the speed limits posted on the roads. The results demonstrate a promising potential for automatic and accurate traffic monitoring using a single image of WV2.

关键词： Moving vehicle extraction object-based image analysis road extraction speed determination time lag WorldView-2

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A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species

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REMOTE SENSING OF ENVIRONMENT 2012年 124卷 516-533页

作者： Pu, Ruiliang Landry, Shawn Univ S Florida Dept Geog Environm & Planning Tampa FL 33620 USA Univ S Florida Florida Ctr Community Design & Res Tampa FL 33620 USA

Urban forest tree species mapping has benefitted from advances in remote sensing techniques. In this study, we explored the potential of the newly developed high resolution satellite sensor, WorldView-2 (WV2) imagery for identifying and mapping urban tree species/groups in the City of Tampa, FL, USA by comparing capabilities between high resolution IKONOS (IKO, acquired on April 6, 2006) and WV2 (acquired on May 1, 2011) imagery for identifying the urban tree species. Seven urban tree species/groups were mapped, including: sand live oak (Quercus geminata), laurel oak (Q. laurifolia), live oak (Q. virginiana), pine (species group), palm (species group), camphor (Cinnamomum camphora), and magnolia (Magnolia grandiflora). image-objects (IOs) were used as the tree species mapping unit. A stepwise masking protocol was developed to separate sunlit and shadow/shaded tree canopy IOs from the study area prior to tree species mapping. Comparative analyses examined average accuracies of tree species mapping results between four-band IKO imagery and three different band combinations of WV2 imagery: four "traditional" bands, four additional bands, and all eight bands. Linear Discriminant analysis and Classification and Regression Trees were used to classify IOs using selected 10 features derived from IKO and band combinations of WV2 imagery. With the exception of sand live oak mapping result (due to phenological difference due to image collection dates), validation results indicate significantly improved mapping accuracies using all combinations of WV2 imagery (p<0.01). Results using independent validation samples demonstrated that average accuracy was increased by 16-18% using WV2 imagery compared to that using IKO imagery when considering mapping 6-species/group. Improved results with the WV2 sensor could be attributed to improved spatial resolution (4 m to 2 m) and additional bands (coastal, yellow, red-edge and NIR2). (c) 2012 Elsevier Inc. All rights reserved.

关键词： Urban trees WorldView-2 IKONOS object-based image analysis Stepwise masking system Linear discriminant analysis Classification and regression trees

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object-based landform delineation and classification from DEMs for archaeological predictive mapping

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JOURNAL OF ARCHAEOLOGICAL SCIENCE 2012年第3期39卷 698-703页

作者： Verhagen, Philip Dragut, Lucian Vrije Univ Amsterdam Fac Arts Res Inst Heritage & Hist Cultural Landscape & Urb Amsterdam Netherlands Salzburg Univ Dept Geog & Geol A-5020 Salzburg Austria W Univ Timisoara Dept Geog Timisoara Romania

In this paper we report the results of an experiment with automated landform delineation and classification from digital elevation models (DEMs) using object-based image analysis (OBIA). Archaeologists rely on accurate and detailed geomorphological maps to predict and interpret the location of archaeological sites. However, they have been using high-resolution DEMs primarily for visual interpretation and expert-judgement classification of landform. OBIA can perform these classifications much faster and in a more objective fashion. The method was tested on a study area in the south east of the Netherlands. It is concluded that OBIA is a suitable technique for quick and objective delineation of landform, but needs an improved conceptual framework adapted to the local situation and archaeological questions to better identify and interpret the derived landform objects. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Geomorphometry Archaeological predictive modelling object-based image analysis Geomorphological mapping

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An open source object-based framework to extract landform classes

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第1期39卷 541-554页

作者： Camargo, F. F. Almeida, C. M. Costa, G. A. O. P. Feitosa, R. Q. Oliveira, D. A. B. Heipke, C. Ferreira, R. S. Natl Inst Space Res INPE Remote Sensing Div Sao Jose Dos Campos SP Brazil Catholic Univ Rio de Janeiro PUC Rio Dept Elect Engn Rio De Janeiro Brazil Leibniz Univ Hannover Inst Photogrammetry & GeoInformat D-30167 Hannover Germany

This paper introduces a new open source, knowledge-based framework for automatic interpretation of remote sensing images, called Interimage. This framework exhibits a flexible modular architecture, in which image processing operators can be associated to both root and leaf nodes of a semantic network, which accounts for a differential strategy in comparison to other object-based image analysis platforms currently available. The architecture, main features as well as an overview on the interpretation strategy implemented in Interimage are presented. The paper also reports an experiment on the classification of landforms. Different geomorphometric and textural attributes obtained from ASTER/Terra images were combined with fuzzy logic to drive the interpretation semantic network. object-based statistical agreement indices, estimated from a comparison between the classified scene and a reference map, were used to assess the classification accuracy. The Interimage interpretation strategy yielded a classification result with strong agreement and proved to be effective for the extraction of landforms. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Cognitive approaches object-based image analysis Semantic network Interimage Geomorphology

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Combining object-based texture measures with a neural network for vegetation mapping in the Everglades from hyperspectral imagery

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REMOTE SENSING OF ENVIRONMENT 2012年 124卷 310-320页

作者： Zhang, Caiyun Xie, Zhixiao Florida Atlantic Univ Dept Geosci Boca Raton FL 33431 USA

An informative and accurate vegetation map for the Greater Everglades of South Florida is in an urgent need to assist with the Comprehensive Everglades Restoration Plan (CERP), a $10.5-billion mission to restore the south Florida ecosystem in 30 + years. In this study, we examined the capability of fine spatial resolution hyperspectral imagery collected from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) for vegetation mapping in the Everglades. In order to obtain an efficient and accurate procedure for vegetation discrimination, we developed a neural network classifier first and then combined the object-based texture measures with the classifier to examine the contribution of the spatial information for vegetation mapping. The neural network is capable of modeling the characteristics of multiple spectral and spatial signatures within a class by an internally unsupervised engine and characterizing spectral and spatial differences between classes by an externally supervised system. The designed procedure was tested in a portion of the Everglades. An object-based vegetation map was generated with an overall classification accuracy averaged 94% and a kappa value averaged 0.94 in discriminating 15 classes. The results are significantly better than those obtained from conventional classifiers such as maximum likelihood and spectral angle mapper. The study illustrates that combining object-based texture measures in the neural network classifier can significantly improve the classification. It is concluded that fine spatial resolution hyperspectral data is an effective solution to accurate vegetation mapping in the Everglades which has a rich plant community with a high degree of spatial and spectral heterogeneity. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Texture analysis object-based image analysis Neural network Hyperspectral imagery Everglades

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Edge-Guided Multiscale Segmentation of Satellite Multispectral imagery

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2012年第11期50卷 4513-4520页

作者： Chen, Jianyu Li, Jonathan Pan, Delu Zhu, Qiankun Mao, Zhihua State Ocean Adm Inst Oceanog 2 State Key Lab Satellite Ocean Environm Dynam Hangzhou 310012 Zhejiang Peoples R China Univ Waterloo Dept Geog & Environm Management Waterloo ON N2L 3G1 Canada Xiamen Univ Sch Informat Sci & Engn Key Lab Underwater Acoust Commun & Marine Informa Xiamen 361005 Peoples R China

This paper presents a new approach to multiscale segmentation of satellite multispectral imagery using edge information. The Canny edge detector is applied to perform multispectral edge detection. The detected edge features are then utilized in a multiscale segmentation loop, and the merge procedure for adjacent image objects is controlled by a separability criterion that combines edge information with segmentation scale. The significance of the edge is measured by adjacent partitioned regions to perform edge assessment. The present method is based on a half-partition structure, which is composed of three steps: single edge detection, separated pixel grouping, and significant feature calculation. The spectral distance of the half-partitions separated by the edge is calculated, compared, and integrated into the edge information. The results show that the proposed approach works well on satellite multispectral images of a coastal area.

关键词： Edge detection multiscale segmentation object-based image analysis scale selection

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Monitoring urban changes based on scale-space filtering and object-oriented classification

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2012年第1期15卷 38-48页

作者： Doxani, G. Karantzalos, K. Tsakiri-Strati, M. Aristotle Univ Thessaloniki Dept Cadastre Photogrammetry & Cartog Thessaloniki 54124 Greece Natl Tech Univ Athens Remote Sensing Lab Athens 15780 Greece

This paper introduces a multi-temporal image processing framework towards an efficient and (semi-) automated detection of urban changes. Nonlinear scale space filtering was embedded in an object-based classification procedure and the resulted simplified images provided a more compact and reliable source in order to generate image objects in various scales. In this manner the multiresolution segmentation outcome was constrained qualitatively. Multivariate alteration detection (MAD) transformation was applied afterwards on the simplified data to facilitate the detection of possible changes. The altered image regions along with the simplified data were further analyzed through a multilevel knowledge-based classification scheme. The developed algorithm was implemented on a number of multi-temporal data acquired by different remote sensing sensors. The qualitative and quantitative evaluation of change detection results performed with the help of the appropriate ancillary ground truth data. Experimental results demonstrated the effectiveness of the developed scale-space, object-oriented classification framework. (C) 2011 Elsevier B.V. All rights reserved.

关键词： object-based image analysis Multivariate alteration detection Morphological scale space filtering

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