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Comparison of Pixel- and object-based classification Methods of Unmanned Aerial Vehicle Data Applied to Coastal Dune Vegetation Communities: Casal Borsetti Case Study

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REMOTE SENSING 2019年第12期11卷 1416-1416页

作者： De Giglio, Michaela Greggio, Nicolas Goffo, Floriano Merloni, Nicola Dubbini, Marco Barbarella, Maurizio Univ Bologna DICAM Civil Chem Environm & Mat Engn Dept Viale Risorgimento 2 I-40136 Bologna Italy Univ Bologna Interdept Ctr Environm Sci Res CIRSA BiGeA Dept Lab IGRG Via S Alberto 163 I-48100 Ravenna Italy Sci High Sch A Oriani Ravenna Via C Battisti 2 I-48121 Ravenna Italy Univ Bologna Geog Sect DiSCi Piazza San Giovanni Monte 2 I-40124 Bologna Italy

Coastal dunes provide the hinterland with natural protection from marine dynamics. The specialized plant species that constitute dune vegetation communities are descriptive of the dune evolution status, which in turn reveals the ongoing coastal dynamics. The aims of this paper were to demonstrate the applicability of a low-cost unmanned aerial system for the classification of dune vegetation, in order to determine the level of detail achievable for the identification of vegetation communities and define the best-performing classification method for the dune environment according to pixel-based and object-based approaches. These goals were pursued by studying the north-Adriatic coastal dunes of Casal Borsetti (Ravenna, Italy). Four classification algorithms were applied to three-band orthoimages (red, green, and near-infrared). All classification maps were validated through ground truthing, and comparisons were performed for the three statistical methods, based on the k coefficient and on correctly and incorrectly classified pixel proportions of two maps. All classifications recognized the five vegetation classes considered, and high spatial resolution maps were produced (0.15 m). For both pixel-based and object-based methods, the support vector machine algorithm demonstrated a better accuracy for class recognition. The comparison revealed that an object approach is the better technique, although the required level of detail determines the final decision.

关键词： vegetation mapping dunes unmanned aerial system pixel-based classification object-based classification

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

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

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International Conference on Audio, Language and Image Processing

作者： Laiwen Zheng Lihong Wan Hong Huo Tao Fang Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education

ISBN: (纸本)9781479939046

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 Greenhouse classification from GeoEye-1 and WorldView-2 Stereo Imagery

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REMOTE SENSING 2014年第5期6卷 3554-3582页

作者： Aguilar, Manuel A. Bianconi, Francesco Aguilar, Fernando J. Fernandez, Ismael Univ Almeria Dept Engn E-04120 La Canada De San Urbano Almeria Spain Univ Perugia Dept Engn I-06125 Perugia Italy

Remote sensing technologies have been commonly used to perform greenhouse detection and mapping. In this research, stereo pairs acquired by very high-resolution optical satellites GeoEye-1 (GE1) and WorldView-2 (WV2) have been utilized to carry out the land cover classification of an agricultural area through an object-based image analysis approach, paying special attention to greenhouses extraction. The main novelty of this work lies in the joint use of single-source stereo-photogrammetrically derived heights and multispectral information from both panchromatic and pan-sharpened orthoimages. The main features tested in this research can be grouped into different categories, such as basic spectral information, elevation data (normalized digital surface model;nDSM), band indexes and ratios, texture and shape geometry. Furthermore, spectral information was based on both single orthoimages and multiangle orthoimages. The overall accuracy attained by applying nearest neighbor and support vector machine classifiers to the four multispectral bands of GE1 were very similar to those computed from WV2, for either four or eight multispectral bands. Height data, in the form of nDSM, were the most important feature for greenhouse classification. The best overall accuracy values were close to 90%, and they were not improved by using multiangle orthoimages.

关键词： object-based classification greenhouses GeoEye-1 WorldView-2 normalized digital surface model multiangle image

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A multispectral image segmentation approach for object-based image classification of high resolution satellite imagery

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KSCE JOURNAL OF CIVIL ENGINEERING 2013年第2期17卷 486-497页

作者： Byun, Young Gi Han, You Kyung Chae, Tae Byeong Korea Aerosp Res Inst Taejon 305333 South Korea Seoul Natl Univ Dept Civil & Environm Engn Seoul 151744 South Korea

Image segmentation has been recognized as an essential process that performs an object-based rather than a pixel-based classification of high-resolution satellite imagery. This paper presents an efficient image segmentation method that considers the spatial and spectral information of high-resolution pan-sharpened imagery. First, we conduct multispectral nonlinear edge preserving smoothing and extract the multispectral edge, which is used as valuable information for seed selection and image segmentation. The initial seeds are automatically selected using the proposed edge variation-based seed selection method, which uses the obtained multispectral edge in a local region. After automatic selection of significant seeds, image segmentation is achieved by applying the modified seeded region growing procedure, which integrates the multispectral and gradient information existing in the image to provide homogenous image regions with accurate and closed boundaries. Experimental results on two multispectral satellite images are given to show that the proposed approach has capability superiority to the previous segmentation techniques on visual evaluation and quantitative comparative assessment.

关键词： image segmentation object-based classification seed selection unsupervised segmentation evaluation multispectral edge

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Using object-based hierarchical classification to extract land use land cover classes from high-resolution satellite imagery in a complex urban area

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JOURNAL OF APPLIED REMOTE SENSING 2015年第1期9卷 096052-1-096052-15页

作者： Gholoobi, Mohsen Kumar, Lalit Khaje Nasiredin Toosi Univ Geomant Fac Remote Sensing & Photogrammetry Dept Tehran 15433 Iran Univ New England Sch Environm & Rural Sci Armidale NSW 2351 Australia

Producing land use and land cover (LULC) maps, particularly in complex urban areas, is one of the most important necessities in civil management programs and is an important research topic in satellite image analysis. High-resolution satellite images provide more opportunities for cost-benefit production of such information. This paper proposes a hierarchical LULC classification based on image objects that are created from multiresolution segmentation. A rule-based strategy is used to implement a step-by-step object-based land cover classification on a pan-sharpened IKONOS image taken from a complex urban region in Shiraz, Iran. A new spatial geometrical analysis for the reclassification of unclassified land cover objects is also utilized. After the initial classification, an object-based land use classification is implemented based on the land cover results and using conceptual, spatial, and geometrical modeling of the relationships between land use elements. Overall classification accuracy was 89 and 87% for land cover and land use approaches, respectively. In the best unclassified object analysis, similar to 70% of unclassified objects were reclassified correctly. The hierarchical methodology proposed here results in fewer unclassified objects since a multistage classification process is utilized rather than the traditional one-pass classification. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.

关键词： land cover and land use rule-based classification hierarchical modeling object-based classification

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Active learning for object-based image classification using predefined training objects

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2018年第9期39卷 2746-2765页

作者： Ma, Lei Fu, Tengyu Li, Manchun Nanjing Univ Jiangsu Prov Key Lab Geog Informat Sci & Technol Nanjing Jiangsu Peoples R China Nanjing Univ Sch Geog & Oceanog Sci Nanjing Jiangsu Peoples R China

object-based image analysis (OBIA) is a new remote-sensing-based image processing technology that has become popular in recent years. In spite of its remarkable advantages, the segmentation results that it generates feature a large number of mixed objects owing to the limitations of OBIA segmentation technology. The mixed objects directly influence the acquisition of training samples and the labelling of objects and thus affect the stability of classification performance. In light of this issue, this article evaluates the influence of classification uncertainty on classification performance and proposes a sampling strategy based on active learning. This sampling strategy is novel in two ways: (1) information entropy is used to evaluate the classification uncertainty of segmented objects;all segmented objects are classified as having zero or non-zero entropies, and the latter are arranged in terms of decreasing entropy. (2) based on an evaluation of the influence of classification uncertainty on classification performance, an active learning technology is developed. A certain proportion of zero-entropy objects is acquired via random sampling used as seed training samples for active learning, non-zero-entropy objects are used as a candidate set for active learning, and the entropy query-by-bagging (EQB) algorithm is used to conduct active learning to acquire optimal training samples. In this study, three groups of high-resolution images were tested. The test results show that zero-entropy and non-zero-entropy objects are indispensable to the classifier, where the optimal range of the ratio of combination of the two is between 0.2 and 0.6. Moreover, the proposed sampling strategy can effectively improve the stability and accuracy of classification.

关键词： Active learning object-based classification classification uncertainty mixed objects segmentation sampling

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Identifying Alpine Wetlands in the Damqu River Basin in the Source Area of the Yangtze River Using object-based classification Method

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Journal of Resources and Ecology 2011年第2期2卷 186-192页

作者：张继平张镱锂刘林山丁明军张学儒中国科学院地理科学与资源研究所北京100101 中国科学院研究生院北京100049 江西师范大学鄱阳湖湿地与流域研究教育部重点实验室江西南昌330022

Alpine wetlands are very sensitive to global change, have great impacts on the hydrological condition of rivers, and are closely related to peoples＇ living in lower reaches. It is essential to monitor alpine wetland changes to appropriately manage and protect wetland resources; however, it is quite difficult to accurately extract such information from remote sensing images due to spectral confusion and arduous field verification. In this study, we identified different wetland types in the Damqu River Basin located in the Yangze River source region from Landsat remote sensing data using the object-based method. In order to ensure the interpretation accuracy of wetland, a digital elevation model （DEM） and its derived data （slope, aspect）, Normalized Difference Vegetation Index （NDVI）, Normalized Difference Water Index （NDWI）, and Kauth-Thomas transformation were considered as the components of the spectral characteristics of wetland types. The spectral characteristics, texture features and spatial structure characteristics of each wetland type were comprehensively analyzed based on the success of image segmentation. The extraction rules for each wetland type were established by determining the thresholds of the spatial, texture and spectral attributes of typical parameter layers according to their histogram statistics. The classification accuracy was assessed using error matrixes and field survey verification data. According to the accuracy assessment, the total accuracy of image classification was 89%.

关键词： alpine wetland remote sensing object-based classification Damqu River Basin

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Man-made Ground Collapse Detection Using High Resolution Aerial Image and object-based classification:Example of Pearl River Delta

Man-made Ground Collapse Detection Using High Resolution Aer...

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The Second International ISCRAM Workshop on Information Systems for Crisis Response and Management

作者： Jiinping Qian Guangzhou Institute of Geography,China Sun Yat-sen University,Guangzhou,China Xiaozhan Zheng Guangzhou Institute of Geological Survey, China Shuisen Chen Guangzhou Institute of Geography,China Ruihua Liu Guangzhou Institute of Geography,China Jie Dou Guangzhou Institute of Geography,China

In this paper,object-based image analysis method was used to detect the ground collapse sites using remote sensing ***,multi-scale image segmentation was performed on the 0.3-meter aerial image of study area and over tens of spatial,spectral,shape and texture features were extracted based on the segmented image *** 4 optimized features for ground collapse classification was selected using generic algorithm(GA),which get the best fitness value in ground collapse classification..After that,some on the spot land collapse sites were selected as cases sites and cased-based-reasoning(CBR) classification was applied on all the segmented image objects,from large scale to small *** the end,classification accuracy was evaluated over the whole study *** average error of object-based CBR classification of ground collapse area is about 20.7%.

关键词： Ground collapse urban geology high resolution image object-based classification CBR(Case-based Reasoning)

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Forest structure parameter extraction using SPOT-7 satellite data by object- and pixel-based classification methods

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2020年第1期192卷 43-43页

作者： Rahimizadeh, Naimeh Kafaky, Sasan Babaie Sahebi, Mahmod Reza Mataji, Asadollah Islamic Azad Univ Sci & Res Branch Dept Environm & Nat Resources Tehran Iran KN Toosi Univ Technol Geodesy & Geomat Engn Fac Tehran Iran

Using satellite data to extract forest structure mapping parameters assists forest management. In this research, structural parameters including species, density, canopy, and gaps were extracted from SPOT-7 satellite data over Hyrcanian forests (Iran). A detailed ground inventory was initially conducted, over 12 x 1 ha (100 m x 100 m) plots, in which tree coordinates were plotted, using a differential global positioning system (DGPS), along with data on tree species, diameter-at-breast-height and height, as well as canopy dimensions, and canopy gap shapes, sizes, and positions, for each plot. Then, spectral transformations, vegetation indices, and simple spectral ratios were extracted from SPOT-7 data, and a supervised, pixel-based classification method and a support-vector machine algorithm were used to classify and determine tree species types. In addition, canopy tree borders and gaps were classified, using an object-based method, and tree densities per unit area were determined, using the canopy gravity center. Finally, the original ground data was used to perform an accuracy assessment on the extracted information, with the results showing that forest type could be determined with 95% accuracy and a Kappa coefficient of 0.8. Canopy and gap coverage achieved an overall accuracy of 91% (Kappa coefficient: 0.7), and tree densities per hectare were determined, on average, to be 47 trees fewer than reality. In conclusion, we have shown that forest structural parameters could be extracted, with good accuracy, using a combination of pixel- and object-based methods applied to SPOT-7 imaging.

关键词： Fagus orientalis Hyrcanian forest object-based classification SPOT-7 Support-vector machine algorithm

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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 International Geoscience and Remote Sensing Symposium

作者： Kamel KIANI Barat MOJARADI Ali ESMAEILY Bahram SALEHI Department of Remote Sensing Engineering Graduate University of Advanced Technology Department of Civil Engineering Iran University of Science and Technology Centre for Cold Ocean Resources Engineering (C-CORE) LOOKNorth Program

ISBN: (纸本)9781479953141

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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