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23rd International Geoscience and Remote Sensing Symposium (IGARSS 2003)

作者： Darwish, A Leukert, K Reinhardt, W Univ Bundeswehr Munich GIS Lab D-85577 Neubiberg Germany

The main aim of this research is to find optimum segmentation parameters for extracting different land cover classes. A relatively new segmentation approach, multiresolution segmentation, is being examined using two d... 详细信息

ISBN: (纸本)0780379292

关键词： multiresolution segmentation object-based classification land-cover classification

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A Noise Removal Approach for object-based classification of VHR Imagery via Post-classification 4

A Noise Removal Approach for Object-based Classification of ...

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International Conference on Audio, Language and Image Processing

作者： Zheng, Laiwen Wan, Lihong Huo, Hong Fang, Tao Shanghai Jiao Tong Univ Dept Automat Shanghai Peoples R China Minist Educ Key Lab Syst Control & Informat Proc Shanghai Peoples R China

ISBN: (纸本)9781479939039

The pixel-based classification of remotely sensed images always produces a large amount of "speckled" or "salt and pepper" noises. Both post-classification smoothing and object-based classification techniques have been proposed to tackle this problem. However, most of them are not adequate to deal with the noises in object-based classification of very high resolution (VHR) remote sensing imagery, because a lot of noisy regions will be produced by image segmentation and the existing post-classification approaches generally are tailored towards pixel-based classification. This paper proposes a novel noise removal approach for object-based classification of VHR imagery via post-classification. It includes four phases: firstly, an image is segmented into homogeneous regions;secondly, all regions are classified according to their spectral and texture features;thirdly, noisy regions are distinguished by using shape features. Finally, the noisy regions are removed by using contextual features. Experimental results show the proposed approach is effective and can improve the overall accuracy of classification of VHR remote sensing imagery.

关键词： VHR Remote Sensing Imagery object-based classification Noise Removal Post-classification

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object-based classification of IKONOS Data for Endemic Torreya Mapping

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Procedia Environmental Sciences 2011年 10卷 1887-1891页

作者： Xiaoming Wang Ke Wang Benzhi Zhou Research Institute of Subtropical Forestry the Chinese Acadamy of Forestry Fuyang 311400 Zhejiang China Institution of Remote Sensing & Information Technology Application Zhejiang University Hangzhou 310029 Zhejiang China

Torreya ( T. grandis ‘Merrillii’ ) is a rare cash crop tree endemic to Kuaiji mountains of southern China. The present research demonstrated the use of spatial information in object-based classification for improving the identification of Torreya from IKONOS images. Pixel-based and object-based classifications on a combination of spectral and textural features were implemented and their accuracies were compared. The object-based classification proved more accurate than the pixel-based classification. The overall classification accuracy and Kappa coefficients were 75.7% and 0.676 for the pixel-based classification; 81.7% and 0.746 for the object-based classification. The overall results demonstrate that object-based classification of very high resolution satellite images using additional features is a practical tool for vegetation mapping in the study area.

关键词： Vegetation mapping object-based classification Texture IKONOS

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Identifying concentrated areas of trip generators from high spatial resolution satellite images using object-based classification techniques

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APPLIED GEOGRAPHY 2014年 53卷 271-283页

作者： Soares Machado, Claudia A. Knopik Beltrame, Alessandra M. Shinohara, Eduardo J. Giannotti, Mariana A. Durieux, Laurent Nobrega, Tania M. Q. Quintanilha, Jose A. Univ Sao Paulo Polytech Sch Dept Transportat Engn Lab Geoproc BR-05508070 Sao Paulo Brazil Serv Nacl Aprendizagem Comercial SENAC Natl Serv Commercial Learning Off Grad Geoproc Sao Paulo Brazil CO AMBIENTAL ESTADO SAO PAULO CETESB SAO PAULO STATE ENVIRONM AGCY BR-05459900 Sao Paulo Brazil

The urban environment is highly complex and heterogeneous and is characterised by rapid changes in its configuration and characteristics, which scholars have referred to as urban growth. However, urban growth is not synonymous with urban development. However, urban growth is not synonymous with urban development. For development to accompany growth, territorial ordinances must be adopted, which highlights the need for planning. The high frequency and broad scope of geographic alterations in the urban environment require quick and inexpensive methods to produce and update spatial information, such as those methods that depend on remote-sensing tools. The advent of remote-sensing satellite imagery with high spatial resolution introduced a new perspective from which to analyse and study urban areas, particularly with respect to the impact of transportation systems and human activities that operate in the midst of a global context that is looking for ways to promote a sustainable urban growth and development model. In this context, the present paper proposes a methodology for identifying useful urban features for transportation planning, particularly with respect to areas with higher concentrations of trip generators that are identified from satellite images, using object-based classification techniques. The proposed methodology for classifying images minimises costs and prioritises field activities related to research on trip generators, as well as origin/destination studies. The methodology was used in the city of Joao Pessoa, Paraiba State, Brazil with satisfactory and promising results. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Trip generators object-based classification Remote sensing

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URBAN AREA object-based classification BY FUSION OF HYPERSPECTRAL AND LIDAR DATA

URBAN AREA OBJECT-BASED CLASSIFICATION BY FUSION OF HYPERSPE...

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IEEE Joint International Geoscience and Remote Sensing Symposium (IGARSS) / 35th Canadian Symposium on Remote Sensing

作者： Kiani, Kamel Mojaradi, Barat Esmaeily, Ali Salehi, Bahram Grad Univ Adv Technol Dept Remote Sensing Engn Kerman Iran

ISBN: (纸本)9781479957750

This research presents a novel strategy for hyperspectral and LiDAR data fusion to generate accurate LC/LU maps with object based classification method. Unlike conventional object-based strategies (hierarchical and multilevel models), in the proposed method, classification has been performed in an iterative Segmentation-classification-Merging (SCM) process. In each iteration, image objects are extracted by using their spectral, height, geometric and class-related characteristics based on data availability, class importance and higher extraction capability. Results indicate great overall accuracy of 97.33% and a kappa coefficient of 0.9710.

关键词： Hyperspectral imagery LiDAR data data Fusion object-based classification precise LC/LU maps

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A NEW HIERARCHICAL object-based classification ALGORITHM FOR WETLAND MAPPING IN NEWFOUNDLAND, CANADA 38

A NEW HIERARCHICAL OBJECT-BASED CLASSIFICATION ALGORITHM FOR...

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38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Mohammadimanesh, Fariba Salehi, Bahram Mandianpari, Masoud Motagh, Mandi Mem Univ Newfoundland C CORE St John NF A1B 3X5 Canada Mem Univ Newfoundland Dept Elect Engn St John NF A1B 3X5 Canada Leibniz Univ Hannover Inst Photogrammetry & GeoInformat D-30167 Hannover Germany

ISBN: (纸本)9781538671504

In this study, a new hierarchical object-based Random Forest (RF) classification approach is proposed for discriminating between different wetland classes in a study area located in the north eastern portion of the Avalon Peninsula, Newfoundland and Labrador province, Canada. Specifically, multi-polarization and multi-frequency SAR data, including single polarized TerraSAR-X (HH), dual polarized L-band ALOS-2 (HH/HV), and fully polarized C band RADARSAT-2 images, were applied in three different classification levels. The overall accuracy and kappa coefficient were determined in each classification level for evaluating the classification results. Importantly, an overall accuracy of 94.82% was obtained for the final classified map in this study.

关键词： object-based classification random forest wetland TerraSAR-X ALOS-2 RADARSAT-2

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HYBRID OPTIMIZATION OF object-based classification IN HIGH-RESOLUTION IMAGES USING CONTINOUS ANT COLONY ALGORITHM WITH EMPHASIS ON BUILDING DETECTION 2

HYBRID OPTIMIZATION OF OBJECT-BASED CLASSIFICATION IN HIGH-R...

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ISPRS Int Joint Conf of 2nd Conf on Geospatial Information Research GI Research, 4th Conf on Sensors and Models in Photogrammetry and Remote Sensing (SMPR), 6th Conf on Earth Observation of Environmental Changes (EOEC)

作者： Tamimi, E. Ebadi, H. Kiani, A. KN Toosi Univ Technol Fac Geodesy & Geomat Engn Tehran Iran

Automatic building detection from High Spatial Resolution (HSR) images is one of the most important issues in Remote Sensing (RS). Due to the limited number of spectral bands in HSR images, using other features will lead to improve accuracy. By adding these features, the presence probability of dependent features will be increased, which leads to accuracy reduction. In addition, some parameters should be determined in Support Vector Machine (SVM) classification. Therefore, it is necessary to simultaneously determine classification parameters and select independent features according to image type. Optimization algorithm is an efficient method to solve this problem. On the other hand, pixel-based classification faces several challenges such as producing salt-paper results and high computational time in high dimensional data. Hence, in this paper, a novel method is proposed to optimize object-based SVM classification by applying continuous Ant Colony Optimization (ACO) algorithm. The advantages of the proposed method are relatively high automation level, independency of image scene and type, post processing reduction for building edge reconstruction and accuracy improvement. The proposed method was evaluated by pixel-based SVM and Random Forest (RF) classification in terms of accuracy. In comparison with optimized pixel-based SVM classification, the results showed that the proposed method improved quality factor and overall accuracy by 17% and 10%, respectively. Also, in the proposed method, Kappa coefficient was improved by 6% rather than RF classification. Time processing of the proposed method was relatively low because of unit of image analysis (image object). These showed the superiority of the proposed method in terms of time and accuracy.

关键词： Hybrid Optimization object-based classification Ant Colony Optimization Algorithm High Spatial Resolution Image

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object-based classification of IKONOS data for endemic Torreya mapping

Object-based classification of IKONOS data for endemic Torre...

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2011 3rd International Conference on Environmental Science and Information Application Technology(ESIAT 2011)

作者： Xiaoming Wang Ke Wang Benzhi Zhou Research Institute of Subtropical Forestry the Chinese Acadamy of Forestry Institution of Remote Sensing & Information Technology Application Zhejiang University

Torreya(T. grandis ‘Merrillii') is a rare cash crop tree endemic to Kuaiji mountains of southern China. The present research demonstrated the use of spatial information in object-based classification for improving the identification of Torreya from IKONOS images. Pixel-based and object-based classifications on a combination of spectral and textural features were implemented and their accuracies were compared. The object-based classification proved more accurate than the pixel-based classification. The overall classification accuracy and Kappa coefficients were 75.7% and 0.676 for the pixel-based classification;81.7% and 0.746 for the object-based classification. The overall results demonstrate that object-based classification of very high resolution satellite images using additional features is a practical tool for vegetation mapping in the study area.

关键词： Vegetation mapping object-based classification Texture IKONOS

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An Efficient Multi-stage object-based classification to Extract Urban Building Footprints from HR Satellite Images

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TRAITEMENT DU SIGNAL 2021年第1期38卷 191-196页

作者： Pendyala, Gopala Krishna V. S. S. N. Kalluri, Hemantha Kumar Rao, Venkateswara C. Natl Remote Sensing Ctr Hyderabad 500037 Telangana India Vignans Fdn Sci Technol & Res Dept Comp Sci & Engn Vadlamudi 522213 Andhra Pradesh India

Urban building information can be effectively extracted by applying object-based image segmentation and multi-stage thresholding on High Resolution (HR) remote sensing satellite imageries. This study provides the results obtained using this method on the images of Indian remote sensing satellite, CARTOSAT-2S launched by the Indian Space Research Organization (ISRO). In this study, a method is developed to extract urban building footprints from the HR remote sensing satellite images. The first step of the process consists of generating highly dense per pixel Digital Surface Model (DSM) by using semi global matching algorithm on HR satellite stereo images and applying robust ground filtering to generate Digital Terrain Model (DTM). In the second step, multi-stage object-based approach is adopted to extract building bases using the PAN sharpened image, normalized Digital Surface Model (nDSM) derived from DSM and DTM, and Normalised Difference Vegetation Index (NDVI). The results are compared with the manual method of drawing building footprints by cartographers. An average precision of 0.930, recall of 0.917, and f-score of 0.922 are obtained. The results are found to be in a match with the method using the high resolution Airborne LiDAR DSM by providing a solution for large areas, low cost and low time.

关键词： nDSM NDVI object-based classification thresholding urban building classification

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A hybrid machine learning technique for feature optimization in object-based classification of debris-covered glaciers

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AIN SHAMS ENGINEERING JOURNAL 2022年第6期13卷

作者： Sharda, Shikha Srivastava, Mohit Gusain, Hemendra Singh Sharma, Naveen Kumar Bhatia, Kamaljit Singh Bajaj, Mohit Kaur, Harsimrat Zawbaa, Hossam M. Kamel, Salah IK Gujral Punjab Tech Univ Dept Elect & Commun Engn Jalandhar 144603 Punjab India Chandigarh Engn Coll Dept Elect & Commun Engn Mohali 140307 Punjab India DRDO Inst Technol Management ITM Mussoorie 248179 India I K Gujral Punjab Tech Univ Dept Elect Engn Jalandhar 144603 Punjab India GB Pant Inst Engn & Technol Dept Elect & Commun Engn Pauri Garhwal 246194 India Natl Inst Technol Dept Elect & Elect Engn Delhi 110040 India CT Inst Engn & Technol Dept Elect & Commun Engn Jalandhar 144623 Punjab India Beni Suef Univ Fac Comp & Artificial Intelligence Bani Suwayf Egypt Technol Univ Dublin Dublin Ireland Aswan Univ Fac Engn Elect Engn Dept Aswan 81542 Egypt Technol Univ Dublin Cabra East Pk House191 N Circular Rd Dublin D07 EWV4 Ireland

object-based features like spectral, topographic, and textural are supportive to determine debris-covered glacier classes. The original feature space includes relevant and irrelevant features. The inclusion of all these features increases the complexity and renders the classifier's performance. Therefore, feature space optimization is requisite for the classification process. Previous studies have shown a rigorous exercise in manually selecting the best combination of features to define the target class and proven to be a timeconsuming task. The present study proposed a hybrid feature selection technique to automate the selection of the best suitable features. This study aimed to reduce the classifier's complexity and enhance the performance of the classification model. Relief-F and Pearson Correlation filter-based feature selection methods ranked features according to the relevance and filtered out irrelevant or less important features based on the defined condition. Later, the hybrid model selected the common features to get an optimal feature set. The proposed hybrid model was tested on Landsat 8 images of debris-covered glaciers in Central Karakoram Range and validated with present glacier inventories. The results showed that the classification accuracy of the proposed hybrid feature selection model with a Decision Tree classifier is 99.82%, which is better than the classification results obtained using other mapping techniques. In addition, the hybrid feature selection technique has sped up the process of classification by reducing the number of features by 77% without compromising the classification accuracy. (c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-ncnd/4.0/).

关键词： Decision tree Feature selection Machine learning object-based classification

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