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Large-area settlement pattern recognition from Landsat-8 data

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2016年第0期119卷 294-308页

作者： Wieland, Marc Pittore, Massimiliano GFZ German Res Ctr Geosci Ctr Early Warning Syst D-14473 Potsdam Germany

The study presents an image processing and analysis pipeline that combines object-based image analysis with a Support Vector Machine to derive a multi-layered settlement product from Landsat-8 data over large areas. 43 image scenes are processed over large parts of Central Asia (Southern Kazakhstan, Kyrgyzstan, Tajikistan and Eastern Uzbekistan). The main tasks tackled by this work include built-up area identification, settlement type classification and urban structure types pattern recognition. Besides commonly used accuracy assessments of the resulting map products, thorough performance evaluations are carried out under varying conditions to tune algorithm parameters and assess their applicability for the given tasks. As part of this, several research questions are being addressed. In particular the influence of the improved spatial and spectral resolution of Landsat-8 on the SVM performance to identify built-up areas and urban structure types are evaluated. Also the influence of an extended feature space including digital elevation model features is tested for mountainous regions. Moreover, the spatial distribution of classification uncertainties is analyzed and compared to the heterogeneity of the building stock within the computational unit of the segments. The study concludes that the information content of Landsat-8 images is sufficient for the tested classification tasks and even detailed urban structures could be extracted with satisfying accuracy. Freely available ancillary settlement point location data could further improve the built-up area classification. Digital elevation features and pan-sharpening could, however, not significantly improve the classification results. The study highlights the importance of dynamically tuned classifier parameters, and underlines the use of Shannon entropy computed from the soft answers of the SVM as a valid measure of the spatial distribution of classification uncertainties. (C) 2016 International Society for Photogram

关键词： Landsat-8 object-based image analysis Machine learning Settlements Central Asia

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An object-based Paddy Rice Classification Using Multi-Spectral Data and Crop Phenology in Assam, Northeast India

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REMOTE SENSING 2016年第6期8卷 479页

作者： Singha, Mrinal Wu, Bingfang Zhang, Miao Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Inst Remote Sensing & Digital Earth Div Digital Agr Olymp Village Sci PkWest Beichen Rd Beijing 100101 Peoples R China

Rice is the staple food for half of the world's population. Therefore, accurate information of rice area is vital for food security. This study investigates the effect of phenology for rice mapping using an object-based image analysis (OBIA) approach. Crop phenology is combined with high spatial resolution multispectral data to accurately classify the rice. Phenology was used to capture the seasonal dynamics of the crops, while multispectral data provided the spatial variation patterns. Phenology was extracted from MODIS NDVI time series, and the distribution of rice was mapped from China's Environmental Satellite (HJ-1A/B) data. Classification results were evaluated by a confusion matrix using 100 sample points. The overall accuracy of the resulting map of rice area generated by both spectral and phenology is 93%. The results indicate that the use of phenology improved the overall classification accuracy from 2%-4%. The comparison between the estimated rice areas and the State's statistics shows underestimated values with a percentage difference of -34.53%. The results highlight the potential of the combined use of crop phenology and multispectral satellite data for accurate rice classification in a large area.

关键词： object-based image analysis phenology data fusion paddy rice classification

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Estimation of Seismic Vulnerability Levels of Urban Structures With Multisensor Remote Sensing

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2016年第5期9卷 1913-1936页

作者： Geiss, Christian Jilge, Marianne Lakes, Tobia Taubenboeck, Hannes German Remote Sensing Data Ctr DFD German Aerosp Ctr DLR D-82234 Oberpfaffenhofen Germany Humboldt Univ Dept Geog D-12489 Berlin Germany

The ongoing global transformation of human habitats from rural villages to ever growing urban agglomerations induces unprecedented seismic risks in earthquake prone regions. To mitigate affiliated perils requires the seismic assessment of built environments. Numerous studies emphasize that remote sensing can play a valuable role in supporting the extraction of relevant features for preevent vulnerability analysis. However, the majority of approaches operate on building level. This induces the deployment of very high spatial resolution remote sensing data, which hampers, nowadays, utilization capabilities for larger areas due to data costs and processing requirements. In this paper, we alter the spatial scale of analysis and propose concepts and methods to estimate the seismic vulnerability level of homogeneous urban structures. A procedure is designed, which comprises four main steps dedicated to: 1) delineation of urban structures by means of a tailored unsupervised data segmentation procedure with scale optimization;2) characterization of urban structures by a joint exploitation of multisensor data;3) selection of most feasible features under consideration of in situ vulnerability information;and 4) estimation of seismic vulnerability levels of urban structures within a supervised learning framework. We render the prediction problem in three ways to address operational requirements that can evolve in real-life situations. 1) To discriminate two or more classes based on labeled samples of all classes present in the data under investigation, we use the framework of soft margin support vector machines (C-SVM). 2) To consider situations, where solely labeled samples are available for the class(es) of interest and not for all classes present in the data, we deploy ensembles of nu-one-class SVM (nu-OC-SVM). and 3) To fit data with a higher statistical level of measurement (interval or ratio scale), we utilize a support vector regression (SVR) approach to estimate a regr

关键词： Earthquakes Istanbul Landsat machine learning object-based image analysis RapidEye seismic vulnerability assessment support vector machines (SVM) TanDEM-X

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Quantifying pruning impacts on olive tree architecture and annual canopy growth by using UAV-based 3D modelling

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PLANT METHODS 2017年第1 p55-55期13卷 55-55页

作者： Jimenez-Brenes, F. M. Lopez-Granados, F. de Castro, A. I. Torres-Sanchez, J. Serrano, N. Pena, J. M. CSIC Inst Sustainable Agr Cordoba 14004 Spain Inst Agr Res & Training IFAPA Cordoba 14004 Spain CSIC Inst Agr Sci E-28006 Madrid Spain

Background: Tree pruning is a costly practice with important implications for crop harvest and nutrition, pest and disease control, soil protection and irrigation strategies. Investigations on tree pruning usually involve tedious onground measurements of the primary tree crown dimensions, which also might generate inconsistent results due to the irregular geometry of the trees. As an alternative to intensive field-work, this study shows a innovative procedure based on combining unmanned aerial vehicle (UAV) technology and advanced object-based image analysis (OBIA) methodology for multi-temporal three-dimensional (3D) monitoring of hundreds of olive trees that were pruned with three different strategies (traditional, adapted and mechanical pruning). The UAV images were collected before pruning, after pruning and a year after pruning, and the impacts of each pruning treatment on the projected canopy area, tree height and crown volume of every tree were quantified and analyzed over time. Results: The full procedure described here automatically identified every olive tree on the orchard and computed their primary 3D dimensions on the three study dates with high accuracy in the most cases. Adapted pruning was generally the most aggressive treatment in terms of the area and volume (the trees decreased by 38.95 and 42.05% on average, respectively), followed by trees under traditional pruning (33.02 and 35.72% on average, respectively). Regarding the tree heights, mechanical pruning produced a greater decrease (12.15%), and these values were minimal for the other two treatments. The tree growth over one year was affected by the pruning severity and by the type of pruning treatment, i.e., the adapted-pruning trees experienced higher growth than the trees from the other two treatments when pruning intensity was low (< 10%), similar to the traditionally pruned trees at moderate intensity (10-30%), and lower than the other trees when the pruning intensity was higher than 30% o

关键词： Crown volume Remote sensing Unmanned aerial vehicle object-based image analysis Precision agriculture

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Landslide Mapping in Vegetated Areas Using Change Detection based on Optical and Polarimetric SAR Data

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REMOTE SENSING 2016年第4期8卷 307页

作者： Plank, Simon Twele, Andre Martinis, Sandro German Aerosp Ctr DLR German Remote Sensing Data Ctr DFD Muenchener Str 20 D-82234 Oberpfaffenhofen Germany

Mapping of landslides, quickly providing information about the extent of the affected area and type and grade of damage, is crucial to enable fast crisis response, i.e., to support rescue and humanitarian operations. Most synthetic aperture radar (SAR) data-based landslide detection approaches reported in the literature use change detection techniques, requiring very high resolution (VHR) SAR imagery acquired shortly before the landslide event, which is commonly not available. Modern VHR SAR missions, e.g., Radarsat-2, TerraSAR-X, or COSMO-SkyMed, do not systematically cover the entire world, due to limitations in onboard disk space and downlink transmission rates. Here, we present a fast and transferable procedure for mapping of landslides, based on change detection between pre-event optical imagery and the polarimetric entropy derived from post-event VHR polarimetric SAR data. Pre-event information is derived from high resolution optical imagery of Landsat-8 or Sentinel-2, which are freely available and systematically acquired over the entire Earth's landmass. The landslide mapping is refined by slope information from a digital elevation model generated from bi-static TanDEM-X imagery. The methodology was successfully applied to two landslide events of different characteristics: A rotational slide near Charleston, West Virginia, USA and a mining waste earthflow near Bolshaya Talda, Russia.

关键词： landslide change detection SAR polarimetry PolSAR object-based image analysis OBIA TanDEM-X

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An Integrated Approach for Monitoring Contemporary and Recruitable Large Woody Debris

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REMOTE SENSING 2016年第9期8卷 778-778页

作者： Richardson, Jeffrey J. Moskal, L. Monika Univ Washington Sch Environm & Forest Sci Seattle WA 98195 USA

Large woody debris (LWD) plays a critical structural role in riparian ecosystems, but it can be difficult and time-consuming to quantify and survey in the field. We demonstrate an automated method for quantifying LWD using aerial LiDAR and object-based image analysis techniques, as well as a manual method for quantifying LWD using image interpretation derived from LiDAR rasters and aerial four-band imagery. In addition, we employ an established method for estimating the number of individual trees within the riparian forest. These methods are compared to field data showing high accuracies for the LWD method and moderate accuracy for the individual tree method. These methods can be integrated to quantify the contemporary and recruitable LWD in a river system.

关键词： LiDAR object-based image analysis riparian forests

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object-based urban land cover classification using rule inheritance over very high-resolution multisensor and multitemporal data

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GISCIENCE & REMOTE SENSING 2016年第2期53卷 164-182页

作者： Hussain, Ejaz Shan, Jie Natl Univ Sci & Technol Inst Geog Informat Syst H-12 Islamabad 44000 Pakistan Purdue Univ Lyles Sch Civil Engn W Lafayette IN 47907 USA

Very high spatial and temporal resolution remote sensing data facilitate mapping highly complex and diverse urban environments. This study analyzed and demonstrated the usefulness of combined high-resolution aerial digital images and elevation data, and its processing using object-based image analysis for mapping urban land covers and quantifying buildings. It is observed that mapping heterogeneous features across large urban areas is time consuming and challenging. This study presents and demonstrates an approach for formulating an optimal land cover classification rule set over small representative training urban area image, and its subsequent transfer to the multisensor, multitemporal images. The classification results over the training area showed an overall accuracy of 96%, and the application of rule set to different sensor images of other test areas resulted in reduced accuracies of 91% for the same sensor, 90% and 86% for the different sensors temporal data. The comparison of reference and classified buildings showed +/- 4% detection errors. Classification through a transferred rule set reduced the classification accuracy by about 5%-10%. However, the trade-off for this accuracy drop was about a 75% reduction in processing time for performing classification in the training area. The factors influencing the classification accuracies were mainly the shadow and temporal changes in the class characteristics.

关键词： urban land covers object-based image analysis high-resolution remote sensing data building detection inheritance of rules

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DETECTION OF SALINE AND NON-SALINE LAKES ON THE PANTANAL OF NHECOLANDIA (BRAZIL) USING object-based image analysis

DETECTION OF SALINE AND NON-SALINE LAKES ON THE PANTANAL OF ...

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Conference on Geographic object-based image analysis (GEOBIA)

作者： Bertani, T. C. Novack, T. Hayakawa, E. H. Zani, H. Natl Inst Space Res INPE Sao Jose Dos Campos SP Brazil

The Pantanal (Brazil) is one of the world's largest tropical wetlands. A natural phenomenon that encourages more detailed scientific studies on this region is the genesis and behavior of innumerable small lakes and ponds with open-water surfaces on the Nhecolandia region at the Taquari alluvial fan. In this paper, we suggest a new approach to detect and differentiate lakes among saline and non-saline using solely remote sensing data and object-based image analysis, which have still been undertaken towards this end. In all experiments the visible and near-infrared bands of the ASTER sensor were used. The methodology involved segmentation steps using the multi-resolution segmentation algorithm available on the Definiens Developer system as well as rule-based classifications and merging of objects. The classification model was structured as s process-tree. Initially, a segmentation and classification step was carried out with the purpose of precise detection and delineation of all lakes. Following, sand belts were detected also through a segmentation and classification process. Finally, the differentiation between saline and non-saline lakes was done based on the criterion of relative border to sand belts. A qualitative analysis of the resulting thematic map indicated a good result. Quantitatively, the error matrix scored an overall accuracy of 73% as well as a Kappa index of 0.64. Most of the classification errors were ascribed to intermittent lakes on different drought conditions. The object-based classification approach has proven to be very efficient for the detection of lakes and their differentiation between saline and non-saline in the region of Pantanal of Nhecolandia.

关键词： Pantanal of Nhecolandia object-based image analysis Lakes Delineation Rule-based Classification

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Can segmentation evaluation metric be used as an indicator of land cover classification accuracy?

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JOURNAL OF APPLIED REMOTE SENSING 2016年第4期10卷

作者： Lenarcic, Andreja Svab Duric, Natasa Cotar, Klemen Ritlop, Klemen Ostir, Kristof Slovenian Ctr Excellence Space Sci & Technol Askerceva Cesta 12 SI-1000 Ljubljana Slovenia Univ Ljubljana Fac Civil & Geodet Engn Jamova Cesta 2 SI-1000 Ljubljana Slovenia Slovenian Acad Sci & Arts Res Ctr Novi Trg 2 SI-1000 Ljubljana Slovenia

It is a broadly established belief that the segmentation result significantly affects subsequent image classification accuracy. However, the actual correlation between the two has never been evaluated. Such an evaluation would be of considerable importance for any attempts to automate the object-based classification process, as it would reduce the amount of user intervention required to fine-tune the segmentation parameters. We conducted an assessment of segmentation and classification by analyzing 100 different segmentation parameter combinations, 3 classifiers, 5 land cover classes, 20 segmentation evaluation metrics, and 7 classification accuracy measures. The reliability definition of segmentation evaluation metrics as indicators of land cover classification accuracy was based on the linear correlation between the two. All unsupervised metrics that are not based on number of segments have a very strong correlation with all classification measures and are therefore reliable as indicators of land cover classification accuracy. On the other hand, correlation at supervised metrics is dependent on so many factors that it cannot be trusted as a reliable classification quality indicator. Algorithms for land cover classification studied in this paper are widely used;therefore, presented results are applicable to a wider area. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： object-based image analysis land cover segmentation quality segmentation evaluation metrics classification accuracy assessment correlation

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object-based urban structure type pattern recognition from Landsat TM with a Support Vector Machine

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2016年第17期37卷 4059-4083页

作者： Wieland, Marc Torres, Yolanda Pittore, Massimiliano Benito, Belen GFZ German Res Ctr Geosci Ctr Early Warning Syst D-14473 Potsdam Germany Tech Univ Madrid Earthquake Engn Res Grp Madrid Spain

This study evaluates the potential of object-based image analysis in combination with supervised machine learning to identify urban structure type patterns from Landsat Thematic Mapper (TM) images. The main aim is to assess the influence of several critical choices commonly made during the training stage of a learning machine on the classification performance and to give recommendations for classifier-dependent intelligent training. Particular emphasis is given to assess the influence of size and class distribution of the training data, the approach of training data sampling (user-guided or random) and the type of training samples (squares or segments) on the classification performance of a Support Vector Machine (SVM). Different feature selection algorithms are compared and segmentation and classifier parameters are dynamically tuned for the specific image scene, classification task, and training data. The performance of the classifier is measured against a set of reference data sets from manual image interpretation and furthermore compared on the basis of landscape metrics to a very high resolution reference classification derived from light detection and ranging (lidar) measurements. The study highlights the importance of a careful design of the training stage and dynamically tuned classifier parameters, especially when dealing with noisy data and small training data sets. For the given experimental set-up, the study concludes that given optimized feature space and classifier parameters, training an SVM with segment-shaped samples that were sampled in a guided manner and are balanced between the classes provided the best classification results. If square-shaped samples are used, a random sampling provided better results than a guided selection. Equally balanced sample distributions outperformed unbalanced training sets.

关键词： Machine learning object-based image analysis urban structure types Landsat Haiti

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