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The potential of object-based classification for geological mapping: Applications to the volcanic region of Vakinankaratra (central Madagascar)

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JOURNAL OF AFRICAN EARTH SCIENCES 2025年 228卷

作者： Ranomenjanahary, Frederic L. Srarfi, Feyda Razafimahatratra, Dieudonne Rakotomanana, Dominique Hamdi, Mohamed S. Abidi, Riadh Randriamalala, Josoa R. Shimi, Najet Slim Univ Tunis Manar Fac Sci Tunis Dept Geol Lab 3G LR18ES37 BP 94 Tunis 1068 Tunisia Univ Antananarivo Ecole Doctorale Gest Ressources Nat & Dev ED GRND Antananarivo 101 Madagascar Univ Antananarivo Ecole Super Polytech Ment Genie Geol BP 1500 Antananarivo 102 Madagascar Univ Antananarivo Ecole Super Sci Agron Ment Foresterie & Environm BP 175 Antananarivo 101 Madagascar

A geological map is an indispensable instrument in the field of geological research. In the past, the sole method employed for the production of geological maps was manual mapping. This approach is costly in terms of time, resources and the amount of work required. Since the conclusion of the 20th century, remote sensing techniques, which entail the classification of satellite images, have been employed for the purpose of geological mapping to complement field work. The pixel method, which classifies based on pixels, is the most prevalent approach for the production of geological maps due to its greater ease of implementation. In contrast, object-based classification methods which consider sets of pixels (objects), are seldom employed, despite their considerable potential for geological mapping. The aim of this article is to compare the performance of these two types of satellite image classification for the update of geological maps (pixel and object-based method). The Central Highlands of Madagascar region has diverse geological, volcanic, sedimentary and metamorphism formations. The richness of geological features make harder geologic mapping. The use of Sentinel-1 and 2 images was justified by their superior accuracy and a high spatial resolution, permitting an easier details highlighting. The object-based method yielded geological map of notable accuracy, a slight superior Kappa coefficient (0.88 vs. 0.83). The generated map exhibit greater homogeneity and are more readily interpretable. Enabling, more straightforward identification of the different lithologies and easier geodynamic history comprehension of the study area.

关键词： Mapping Remote sensing object-based classification Structural geology Madagascar

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object-based classification of hyperspectral data using Random Forest algorithm

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Geo-Spatial Information Science 2018年第2期21卷 127-138页

作者： Saeid Amini Saeid Homayouni Abdolreza Safari Ali A.Darvishsefat School of Surveying and Geospatial Engineering College of EngineeringUniversity of TehranTehranIran Department of Geography Environment and GeomaticsUniversity of OttawaOttawaCanada Faculty of Natural Resources Department of ForestryUniversity of TehranKarajIran

This paper presents a new framework for object-based classification of high-resolution hyperspectral *** multi-step framework is based on multi-resolution segmentation(MRS)and Random Forest classifier(RFC)*** first step is to determine of weights of the input features while using the object-based approach with MRS to processing such *** the high number of input features,an automatic method is needed for estimation of this ***,we used the Variable Importance(VI),one of the outputs of the RFC,to determine the importance of each image ***,based on this parameter and other required parameters,the image is segmented into some homogenous ***,the RFC is carried out based on the characteristics of segments for converting them into meaningful *** proposed method,as well as,the conventional pixel-based RFC and Support Vector Machine(SVM)method was applied to three different hyperspectral data-sets with various spectral and spatial *** data were acquired by the HyMap,the Airborne Prism Experiment(APEX),and the Compact Airborne Spectrographic Imager(CASI)hyperspectral *** experimental results show that the proposed method is more consistent for land cover mapping in various *** overall classification accuracy(OA),obtained by the proposed method was 95.48,86.57,and 84.29%for the HyMap,the APEX,and the CASI datasets,***,this method showed better efficiency in comparison to the spectralbased classifications because the OAs of the proposed method was 5.67 and 3.75%higher than the conventional RFC and SVM classifiers,respectively.

关键词： object-based classification Random Forest algorithm multi-resolution segmentation(MRS) hyperspectral imagery

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object-based classification of wetland vegetation using very high-resolution unmanned air system imagery

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EUROPEAN JOURNAL OF REMOTE SENSING 2017年第1期50卷 564-576页

作者： Pande-Chhetri, Roshan Abd-Elrahman, Amr Liu, Tao Morton, Jon Wilhelm, Victor L. Univ Florida Sch Forest Resources & Conservat Geomat 1200 N Pk Rd Plant City FL 33563 USA USACE Invas Species Management Branch Stuart FL USA US Army Corps Engineers UAS Sect Operat Div Surveying & Mapping Branch Jacksonville FL USA

The purpose of this study is to examine the use of multi-resolution object-based classification methods for the classification of Unmanned Aircraft Systems (UAS) images of wetland vegetation and to compare its performance with pixel-based classification approaches. Three types of classifiers (Support Vector Machine, Artificial Neural Network and Maximum Likelihood) were utilized to classify the object-based images, the original 8-cm UAS images and the down-sampled (30 cm) version of the image. The results of the object-based and two pixel-based classifications were evaluated and compared. object-based classification produced higher accuracy than pixel-based classifications if the same type of classifier is used. Our results also showed that under the same classification scheme (i.e. object or pixel), the Support Vector Machine classifier performed slightly better than Artificial Neural Network, which often yielded better results than Maximum Likelihood. With an overall accuracy of 70.78%, object-based classification using Support Vector Machine showed the best performance. This study also concludes that while UAS has the potential to provide flexible and feasible solutions for wetland mapping, some issues related to image quality still need to be addressed in order to improve the classification performance.

关键词： Remote sensing UAS wetland vegetation mapping object-based classification pixel-based classification Support Vector Machine

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object-based classification of Urban Areas Using VHR Imagery and Height Points Ancillary Data

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REMOTE SENSING 2012年第8期4卷 2256-2276页

作者： Salehi, Bahram Zhang, Yun Zhong, Ming Dey, Vivek Univ New Brunswick Dept Geodesy & Geomat Engn Fredericton NB E3B 5A3 Canada Univ New Brunswick Dept Civil Engn Fredericton NB E3B 5A3 Canada

Land cover classification of very high resolution (VHR) imagery over urban areas is an extremely challenging task. Impervious land covers such as buildings, roads, and parking lots are spectrally too similar to be separated using only the spectral information of VHR imagery. Additional information, therefore, is required for separating such land covers by the classifier. One source of additional information is the vector data, which are available in archives for many urban areas. Further, the object-based approach provides a more effective way to incorporate vector data into the classification process as the misregistration between different layers is less problematic in object-based compared to pixel-based image analysis. In this research, a hierarchical rule-based object-based classification framework was developed based on a small subset of QuickBird (QB) imagery coupled with a layer of height points called Spot Height (SH) to classify a complex urban environment. In the rule-set, different spectral, morphological, contextual, class-related, and thematic layer features were employed. To assess the general applicability of the rule-set, the same classification framework and a similar one using slightly different thresholds applied to larger subsets of QB and IKONOS (IK), respectively. Results show an overall accuracy of 92% and 86% and a Kappa coefficient of 0.88 and 0.80 for the QB and IK Test image, respectively. The average producers' accuracies for impervious land cover types were also 82% and 74.5% for QB and IK.

关键词： object-based classification very high resolution imagery multisource data urban land cover misregistration transferability

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object-based classification of ikonos imagery for mapping large-scale vegetation communities in urban areas

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SENSORS 2007年第11期7卷 2860-2880页

作者： Mathieu, Renaud Aryal, Jagannath Chong, Albert K. Univ Otago Sch Surveying Spatial Ecol Res Facil Dunedin New Zealand CSIR Nat Resources & Environm Earth Res Grp ZA-0001 Pretoria South Africa Lincoln Univ Ctr Adv Computat Solut Lincoln New Zealand

Effective assessment of biodiversity in cities requires detailed vegetation maps. To date, most remote sensing of urban vegetation has focused on thematically coarse land cover products. Detailed habitat maps are created by manual interpretation of aerial photographs, but this is time consuming and costly at large scale. To address this issue, we tested the effectiveness of object-based classifications that use automated image segmentation to extract meaningful ground features from imagery. We applied these techniques to very high resolution multispectral Ikonos images to produce vegetation community maps in Dunedin City, New Zealand. An Ikonos image was orthorectified and a multi-scale segmentation algorithm used to produce a hierarchical network of image objects. The upper level included four coarse strata: industrial/commercial (commercial buildings), residential (houses and backyard private gardens), vegetation (vegetation patches larger than 0.8/1ha), and water. We focused on the vegetation stratum that was segmented at more detailed level to extract and classify fifteen classes of vegetation communities. The first classification yielded a moderate overall classification accuracy (64%, k = 0.52), which led us to consider a simplified classification with ten vegetation classes. The overall classification accuracy from the simplified classification was 77% with a k value close to the excellent range (k = 0.74). These results compared favourably with similar studies in other environments. We conclude that this approach does not provide maps as detailed as those produced by manually interpreting aerial photographs, but it can still extract ecologically significant classes. It is an efficient way to generate accurate and detailed maps in significantly shorter time. The final map accuracy could be improved by integrating segmentation, automated and manual classification in the mapping process, especially when considering important vegetation classes with limited spec

关键词： object-based classification remote sensing cities New Zealand biodiversity habitat

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object-based classification as an alternative approach to the traditional pixel-based classification to identify potential habitat of the Grasshopper Sparrow

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ENVIRONMENTAL MANAGEMENT 2008年第1期41卷 20-31页

作者： Jobin, Benoit Labrecque, Sandra Grenier, Marcelle Falardeau, Gilles Canadian Wildlife Serv Ste Foy PQ G1V 4H5 Canada

The traditional method of identifying wildlife habitat distribution over large regions consists of pixel-based classification of satellite images into a suite of habitat classes used to select suitable habitat patches. object-based classification is a new method that can achieve the same objective based on the segmentation of spectral bands of the image creating homogeneous polygons with regard to spatial or spectral characteristics. The segmentation algorithm does not solely rely on the single pixel value, but also on shape, texture, and pixel spatial continuity. The object-based classification is a knowledge base process where an interpretation key is developed using ground control points and objects are assigned to specific classes according to threshold values of determined spectral and/or spatial attributes. We developed a model using the eCognition software to identify suitable habitats for the Grasshopper Sparrow, a rare and declining species found in southwestern Quebec. The model was developed in a region with known breeding sites and applied on other images covering adjacent regions where potential breeding habitats may be present. We were successful in locating potential habitats in areas where dairy farming prevailed but failed in an adjacent region covered by a distinct Landsat scene and dominated by annual crops. We discuss the added value of this method, such as the possibility to use the contextual information associated to objects and the ability to eliminate unsuitable areas in the segmentation and land cover classification processes, as well as technical and logistical constraints. A series of recommendations on the use of this method and on conservation issues of Grasshopper Sparrow habitat is also provided.

关键词： ammodramus savannarum Grasshopper Sparrow object-based classification pixel-based classification Quebec segmentation

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object-based classification of Abandoned Logging Roads under Heavy Canopy Using LiDAR

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REMOTE SENSING 2014年第5期6卷 4043-4060页

作者： Sherba, Jason Blesius, Leonhard Davis, Jerry San Francisco State Univ Dept Geog & Environm San Francisco CA 94132 USA

LiDAR-derived slope models may be used to detect abandoned logging roads in steep forested terrain. An object-based classification approach of abandoned logging road detection was employed in this study. First, a slope model of the study site in Marin County, California was created from a LiDAR derived DEM. Multiresolution segmentation was applied to the slope model and road seed objects were iteratively grown into candidate objects. A road classification accuracy of 86% was achieved using this fully automated procedure and post processing increased this accuracy to 90%. In order to assess the sensitivity of the road classification to LiDAR ground point spacing, the LiDAR ground point cloud was repeatedly thinned by a fraction of 0.5 and the classification procedure was reapplied. The producer's accuracy of the road classification declined from 79% with a ground point spacing of 0.91 to below 50% with a ground point spacing of 2, indicating the importance of high point density for accurate classification of abandoned logging roads.

关键词： LiDAR object-based classification logging roads forest roads

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object-based classification of Airborne Light Detection and Ranging Point Clouds in Human Settlements

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SENSOR LETTERS 2012年第1-2期10卷 221-229页

作者： Shen, Jing Liu, Jiping Lin, Xiangguo Zhao, Rong Chinese Acad Surveying & Mapping Inst Photogrammetty & Remote Sensing Beijing 100830 Peoples R China Chinese Acad Surveying & Mapping Res Ctr Govt Geog Informat Syst Beijing 100830 Peoples R China

Airborne LiDAR point clouds classification is meaningful for various applications. In this paper, an object-based analysis method is proposed to classify the point clouds in human settlements. In the process of classification, surface growing algorithm is employed to segment the point clouds into different clusters first. The above segmentation is helpful to derive useful features such as area/size, position, orientation, proportion of multiple echoes, height jump between adjacent segments, topological relationship of neighboring segments etc. At last, rule-based classification is performed on the segmented point clouds. Two datasets are used to test the above method. The results suggest that our method will produce the overall classification accuracy larger than 93% and the Kappa coefficient larger than 0.89, which indicates the promising applications.

关键词： Airborne LiDAR object-based classification Segmentation Surface Growing Algorithm Multiple Echoes Human Settlements

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object-based classification of earthquake damage from high-resolution optical imagery using machine learning

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JOURNAL OF APPLIED REMOTE SENSING 2016年第3期10卷 036025-1-036025-16页

作者： Bialas, James Oommen, Thomas Rebbapragada, Umaa Levin, Eugene Michigan Technol Univ Geol & Min Engn & Sci 1400 Townsend Dr Houghton MI 49931 USA CALTECH Jet Prop Lab 4800 Oak Grove Dr Pasadena CA 91109 USA Michigan Technol Univ Sch Technol 1400 Townsend Dr Houghton MI 49931 USA

object-based approaches in the segmentation and classification of remotely sensed images yield more promising results compared to pixel-based approaches. However, the development of an object-based approach presents challenges in terms of algorithm selection and parameter tuning. Subjective methods are often used, but yield less than optimal results. objective methods are warranted, especially for rapid deployment in time-sensitive applications, such as earthquake damage assessment. Herein, we used a systematic approach in evaluating object-based image segmentation and machine learning algorithms for the classification of earthquake damage in remotely sensed imagery. We tested a variety of algorithms and parameters on post-event aerial imagery for the 2011 earthquake in Christchurch, New Zealand. Results were compared against manually selected test cases representing different classes. In doing so, we can evaluate the effectiveness of the segmentation and classification of different classes and compare different levels of multistep image segmentations. Our classifier is compared against recent pixel-based and object-based classification studies for postevent imagery of earthquake damage. Our results show an improvement against both pixel-based and object-based methods for classifying earthquake damage in high resolution, post-event imagery. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： GEOBIA machine learning earthquake object-based classification

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object-based classification with features extracted by a semi-automatic feature extraction algorithm - SEaTH

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GEOCARTO INTERNATIONAL 2011年第3期26卷 211-226页

作者： Gao, Yan Marpu, Prashanth Niemeyer, Imgard Runfola, Daniel Miller Giner, Nicholas M. Hamill, Thomas Pontius, Robert Gilmore, Jr. UNAM Ctr Invest Ecosistemas Morelia Michoacan Mexico Fac Elect & Comp Engn Reykjavik Iceland Forschungszentrum Julich Inst Energy Res D-52425 Julich Germany Clark Univ Grad Sch Geog Worcester MA 01610 USA

object-based image analysis (OBIA) uses object features (or attributes) that relate to the pixels contained by the image object to assist in image classification. These object features include spectral, shape, texture and context features. With hundreds of available features, the identification of those that can improve separability between classes is critical for OBIA. The Separability and Thresholds (SEaTH) algorithm calculates the SEaTH of object-classes for the given features. The SEaTH algorithm avoids time-consuming trial-and-error practice for seeking important features and thresholds. This article tests the SEaTH algorithm on Landsat-7 Enhanced Thematic Mapper (ETM+) imagery in a heterogeneous landscape with multiple land cover classes. The results suggest SEaTH is a strong alternative to other automated approaches, yielding an agreement of 79% with reference data. In comparison, an object-based nearest neighbour classifier yielded 66% agreement and a pixel-based maximum likelihood classifier yielded 69% agreement.

关键词： object-based classification feature extraction separability and thresholds

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