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Assessment of urban growth dynamics in Mumbai Metropolitan Region, India using object-based image analysis for medium-resolution data

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APPLIED GEOGRAPHY 2018年 98卷 110-120页

作者： Mendiratta, Priya Gedam, Shirish Indian Inst Technol CSRE Bombay Maharashtra India

Highlights • Spatiotemporal land cover changes mapped by classification of EO images provide database to project urban growth trends. • Reliable methodology to derive consistent multi-temporal land use classification hence assumes critical importance. • This paper describes the application of object-based image analysis for classification of medium-resolution images. • Classification as image objects obliterates the ‘salt and pepper effects’ for heterogeneous land uses. • Inconsistent classification is identified as false land use class transitions in change matrix, and resolved using GIS. • Higher increase in built-up area with respect to population causes population density to decline and urban form to fragment. • Furthermore, the ecologically sensitive zones are split by the ribbon development along transportation corridors. • The study demonstrates utility of Landsat archives to map land use transitions and identify urban growth trends. [ABSTRACT FROM AUTHOR]

关键词： Urban growth dynamics object-based image analysis Medium-resolution images GIS Change matrix Mumbai metropolitan region

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Burn severity estimation using GeoEye imagery, object-based image analysis (OBIA) and Composite Burn Index (CBI) measurements 3

Burn severity estimation using GeoEye imagery, object-based ...

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3rd International Conference on Remote Sensing and Geoinformation of the Environment (RSCy)

作者： Dragozi, E. Gitas, Ioannis Z. Stavrakoudis, Dimitris G. Minakou, C. Aristotle Univ Thessaloniki Lab Forest Management & Remote Sensing GR-54006 Thessaloniki Greece

ISBN: (纸本)9781628417005

Forest fires greatly influence the stability and functions of the forest ecosystems. The ever increasing need for accurate and detailed information regarding post-fire effects (burn severity) has led to several studies on the matter. In this study the combined use of Very High Resolution (VHR) satellite data (GeoEye), object-based image analysis (OBIA) and Composite Burn Index (CBI) measurements in estimating burn severity, at two different time points (2011 and 2012) is assessed. The accuracy of the produced maps was assessed and changes in burn severity between the two dates were detected using the post classification comparison approach. It was found that the produced burn severity map for 2011 was approximately 10% more accurate than that of 2012. This was mainly attributed to the increased heterogeneity of the study area in the second year, which led to an increased number of mixed class objects and consequently made it more difficult to spectrally discriminate between the severity classes. Following the post-classification analysis, the severity class changes were mainly attributed to the trees' ability to survive severe fire damage and sprout new leaves. Moreover, the results of the study suggest that when classifying CBI-based burn severity using VHR imagery it would be preferable to use images captured soon after the fire.

关键词： Burn severity object-based image analysis Composite Burn Index GeoEye

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A NEW FRAMEWORK FOR object-based image analysis based ON SEGMENTATION SCALE SPACE AND RANDOM FOREST CLASSIFIER

A NEW FRAMEWORK FOR OBJECT-BASED IMAGE ANALYSIS BASED ON SEG...

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International Conference on Sensors and Models in Remote Sensing and Photogrammetry

作者： Hadavand, A. Saadatseresht, M. Homayouni, S. Univ Tehran Sch Surveying & Geospatial Informat Engn Tehran 14174 Iran Univ Ottawa Dept Geog Environm Studies & Geomat Ottawa ON K1N 6N5 Canada

ISBN: (纸本)9781629935201

In this paper a new object-based framework is developed for automate scale selection in image segmentation. The quality of image objects have an important impact on further analyses. Due to the strong dependency of segmentation results to the scale parameter, choosing the best value for this parameter, for each class, becomes a main challenge in object-based image analysis. We propose a new framework which employs pixel-based land cover map to estimate the initial scale dedicated to each class. These scales are used to build segmentation scale space (SSS), a hierarchy of image objects. Optimization of SSS, respect to NDVI and DSM values in each super object is used to get the best scale in local regions of image scene. Optimized SSS segmentations are finally classified to produce the final land cover map. Very high resolution aerial image and digital surface model provided by ISPRS 2D semantic labelling dataset is used in our experiments. The result of our proposed method is comparable to those of ESP tool, a well-known method to estimate the scale of segmentation, and marginally improved the overall accuracy of classification from 79% to 80%.

关键词： Segmentation Scale parameter object-based image analysis Land Cover Classification

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Ultra-resolution unmanned aerial vehicle (UAV) and digital surface model (DSM) data-based automatic extraction of urban features using object-based image analysis approach in Gurugram, Haryana

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APPLIED GEOMATICS 2022年第4期14卷 751-764页

作者： Chauhan, Nitin Kumar, Ritesh Mukherjee, Semonti Hazra, Anupam Giri, Kushal Haryana Space Applicat Ctr Hisar 125004 Haryana India

Unmanned aerial vehicles (UAV) have emerged as flexible, swift, and economical imaging systems that have proven their feasibility in urban infrastructure mapping. However, data derived from such systems are not utilized thoroughly. In this study, an object-oriented, multiresolution segmentation-based workflow is explored to automatically extract the urban features such as buildings and roads that can revolutionize the pace of existing mapping methods. This paper contemplates the automatic object-oriented-based feature extraction process on 5-cm true color ortho-rectified images and digital surface model (DSM). The data was generated using the JOUAV/CW-10 model and a Sony Camera with a 40-megapixel resolution. The segmentation procedure was implemented, defining various parameters such as scale, shape, and compactness. Here, the optimum scale, shape, and compactness parameters chosen for buildings and road segmentation are 100, 0.6, and 0.8, and 50, 0.5, and 0.9, respectively. The object-based image analysis (OBIA) results were compared to manually digitized features to assess the accuracy of the automated process. The fractal border error accuracy is calculated for urban features such as roads and buildings. The OBIA results indicated that completeness, correctness, and quality of building features were 98.2%, 97.6%, and 95.9%, respectively. Similarly, the road features' average completeness, correctness, and quality were 85.8%, 73.8%, and 68.2%, respectively, which is on the lower side due to obscuring of roads by the avenue trees. The methodology yielded promising results for urban feature extraction with substantial accuracy and can be implemented in other areas with little fine-tuning of feature extraction parameters.

关键词： Unmanned aerial vehicle (UAV) Digital surface model (DSM) Feature extraction image segmentation object-based image analysis

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Evaluation of object-based image analysis techniques on very high-resolution satellite image for biomass estimation in a watershed of hilly forest of Nepal

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APPLIED GEOMATICS 2014年第1期6卷 59-68页

作者： Hussin, Yousif Ali Gilani, Hammad van Leeuwen, Louise Murthy, M. S. R. Shah, Rachna Baral, Srijana Tsendbazar, Nandin-Erdene Shrestha, Saurav Shah, Shyam Kumar Qamer, Faisal Mueen Univ Twente Fac Geo Informat Sci & Earth Observat NL-7500 AE Enschede Netherlands Int Ctr Integrated Mt Dev GPOB 3226Khumaltar Kathmandu Nepal Liaoning Tech Univ Sch Geomat Fuxing Liaoning Peoples R China Natl Trust Nat Conservat Cent Zoo Kathmandu Nepal Rupantaran Nepal Kathmandu Nepal Wageningen Univ & Res Ctr Lab Geo Informat Sci & Remote Sensing NL-6700 AA Wageningen Netherlands GoN UNDP CSUWN Kathmandu Nepal Dept Natl Pk & Wildlife Conservat Kathmandu Nepal

Methods of forest carbon estimation using remote-sensing data and techniques are evolving within a short timeframe as compared to traditional forest inventory methods. object-based image analysis (OBIA) provided new opportunities to improve biomass and carbon stock estimation and mapping by delineating and classifying crown projection area (CPA) of individual trees. In this paper, image segmentation techniques of OBIA (region growing and valley following) are being applied on GeoEye-1 satellite data and compared in terms of accuracy in Ludhikhola watershed in the Gorkha District of Nepal. Accuracy assessment of tree crown delineation of both segmentation approaches were analyzed using accuracymeasures of D and one-to-one (1:1) correspondence. The combination of over-segmentation and undersegmentation, D is interpreted as the "closeness" measure to an ideal segmentation result in relation to a predefined reference set. Region growing and valley following segmentation 68 and 58 % accuracy, respectively, was achieved and linear regression model was developed for carbon stock for Shorea robusta which resulted into a coefficient of determination value of 0.67 at 95 % confidence level and the coefficient of determination resulted into a value of 0.70 for other species. The research concluded that region growing approach showed better delineation as compared to valley follow approach, since it used both features of local maxima and local minima.

关键词： object-based image analysis Crow projection area Segmentation Tree crown delineation and linear regression model

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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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Automated mound detection using lidar and object-based image analysis in Beaufort County, South Carolina

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Southeastern Archaeology 2019年第1期38卷 23-37页

作者： Davis, Dylan S. Sanger, Matthew C. Lipo, Carl P. Department of Anthropology Binghamton University Binghamton NY United States

The study of precontact anthropogenic mounded features—earthen mounds, shell heaps, and shell rings—in the American Southeast is stymied by the spotty distribution of systematic surveys across the region. Many extant, yet unidentified, archaeological mound features continue to evade detection due to the heavily forested canopies that occupy large areas of the region, making pedestrian surveys difficult and preventing aerial observation. object-based image analysis (OBIA) is a tool for analyzing light and radar (lidar) data and offers an inexpensive opportunity to address this challenge. Using publicly available lidar data from Beaufort County, South Carolina, and an OBIA approach that incorporates morphometric classification and statistical template matching, we systematically identify over 160 previously undetected mound features. This result improves our overall knowledge of settlement patterns by providing systematic knowledge about past landscapes. © 2018, © Southeastern Archaeological Conference 2018.

关键词： Lidar object-based image analysis remote sensing semi-automatic survey South Carolina

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Fuzzy segmentation for geographic object-based image analysis

Fuzzy segmentation for geographic object-based image analysi...

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SPIE Conference on Remote Sensing for Environmental Monitoring, GIS Applications, and Geology IX

作者： Lizarazo, Ivan Elsner, Paul Univ Dist Francisco Jose de Caldas Carrera 7 40-53 Bogota Colombia Univ London Birkbeck Coll London WC1E 7HX England

ISBN: (纸本)9780819477835

image segmentation partitions remote sensing images into image objects before assigning them to categorical land cover classes. Current segmentation methods require users to invest considerable time and effort in the search for meaningful image objects. As an alternative method we propose 'fuzzy' segmentation that offers more flexibility in dealing with remote sensing uncertainty. In the proposed method, original bands are processed using regression techniques to output fuzzy image regions which express degrees of membership to target land cover classes. Contextual properties of fuzzy regions can be measured to indicate potential spectral confusion. A 'defuzzification' process is subsequently conducted to produce the categorical land cover classes. This method was tested using data sets of both high and medium spatial resolution. The results indicate that this approach is able to produce classification with satisfying accuracy and requires very little user interaction.

关键词： image segmentation object-based image analysis land cover classification

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object-based image analysis for coal fire-related land cover mapping in coal mining areas

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Geocarto International 2009年第1期24卷 25-36页

作者： Gao, Yan Kerle, Norman Mas, Jean-F. Centro de Investigaciones en Geografia Ambiental Universidad Nacional Autónoma de México (UNAM) Morelia Michoacán Mexico International Institute for Geo-Information Science and Earth Observation (ITC) Enschede Netherlands

Coal fires worldwide are causing enormous economic loss and environmental pollution, prompting a need to develop the methodology to map, analyse, and monitor them. Coal fire-related land cover mapping yields the knowledge of the distribution of coal, which is crucial for the delineation of potential coal fires and coal fire risk areas. The conventional pixel-based method does not make use of spatial relationships, whereas object-based image analysis (OBIA) has the potential to consider not only objects' spectral but spatial information, in terms of size, shape, texture and relations to other image objects. This article reports on coal fire-related land cover mapping with OBIA using multi-resolution segmentation and hierarchical classification. Both spectral and spatial information were used in the classification. It yielded an overall accuracy of 12.3% higher than that by the pixel-based method (70.67%), and the difference is significant (p = 0.05) by McNemar's test.

关键词： Coal mining area Hierarchical network classification Land cover classification object-based image analysis Pixel-based image analysis

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Offshore benthic habitat mapping based on object-based image analysis and geomorphometric approach. A case study from the Slupsk Bank, Southern Baltic Sea

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SCIENCE OF THE TOTAL ENVIRONMENT 2021年 801卷 149712-149712页

作者： Janowski, Lukasz Wroblewski, Radoslaw Dworniczak, Janusz Kolakowski, Mateusz Rogowska, Karolina Wojcik, Michal Gajewski, Juliusz Gdynia Maritime Univ Maritime Inst Dlugi Targ 41-42 PL-80830 Gdansk Poland Univ Gdansk Inst Geog Bazynskiego 4 PL-480309 Gdansk Poland MEWO SA Starogardzka 16 PL-83010 Straszyn Poland Gdansk Univ Technol Fac Elect Telecommun & Informat Dept Comp Architecture Gabriela Narutowicza 11-12 PL-80233 Gdansk Poland

Benthic habitat mapping is a rapidly growing field of underwater remote sensing studies. This study provides the first insight for high-resolution hydroacoustic surveys in the Slupsk Bank Natura 2000 site, one of the most valuable sites in the Polish Exclusive Zone of the Southern Baltic. This study developed a quick and transparent, automatic classification workflow based on multibeam echosounder and side-scan sonar surveys to classify benthic habitats in eight study sites within the Slupsk Bank. Different predictor variables, four supervised classifiers, and the generalisation approach, improving the accuracy of the developed model were evaluated. The results suggested a very high significance for the classification performance of specific geomorphometric features that were not used in benthic habitat mapping before. These include, e.g., Fuzzy Landform Element Classification, Multiresolution Index of the Valley Bottom Flatness, and Multiresolution Index of the Ridge Top Flatness. Comparison of classification results with manual maps demonstrated that Random Forest had the highest performance of four tested supervised classifiers. Because the current needs include benthic habitat mapping for the whole area of the Polish Exclusive Economic Zone, the key findings of this study may be further applied to extensive areas in the Polish waters and other vast areas worldwide. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http:// ***/licenses/by/4.0/).

关键词： Benthic habitat mapping Multibeam echosounder Side-scan sonar object-based image analysis Feature selection Geomorphometry

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