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A Novel object-based Supervised classification Method with Active Learning and Random Forest for PolSAR Imagery

REMOTE SENSING 2018年第7期10卷 1092-1092页

作者： Liu, Wensong Yang, Jie Li, Pingxiang Han, Yue Zhao, Jinqi Shi, Hongtao Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Hubei Peoples R China Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

Most of the traditional supervised classification methods using full-polarimetric synthetic aperture radar (PolSAR) imagery are dependent on sufficient training samples, whereas the results of pixel-based supervised classification methods show a high false alarm rate due to the influence of speckle noise. In this paper, to solve these problems, an object-based supervised classification method with an active learning (AL) method and random forest (RF) classifier is presented, which can enhance the classification performance for PolSAR imagery. The first step of the proposed method is used to reduce the influence of speckle noise through the generalized statistical region merging (GSRM) algorithm. A reliable training set is then selected from the different polarimetric features of the PolSAR imagery by the AL method. Finally, the RF classifier is applied to identify the different types of land cover in the three PolSAR images acquired by different sensors. The experimental results demonstrate that the proposed method can not only better suppress the influence of speckle noise, but can also significantly improve the overall accuracy and Kappa coefficient of the classification results, when compared with the traditional supervised classification methods.

关键词： PolSAR imagery object-based classification generalized statistical region merging (GSRM) active learning (AL) random forest (RF)

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A multiple-point spatially weighted k-NN method for object-based classification

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2016年第0期52卷 263-274页

作者： Tang, Yunwei Jing, Linhai Li, Hui Atkinson, Peter M. Chinese Acad Sci Inst Remote Sensing & Digital Earth Key Lab Digital Earth Sci 9 Dengzhuang South Rd Beijing 100094 Peoples R China Univ Lancaster Fac Sci & Technol Engn Bldg Lancaster LA1 4YR England Queens Univ Belfast Sch Geog Archaeol & Palaeoecol Belfast BT7 1NN Antrim North Ireland Univ Southampton Geog & Environm Southampton SO17 1BJ Hants England

object-based classification, commonly referred to as object-based image analysis (OBIA), is now commonly regarded as able to produce more appealing classification maps, often of greater accuracy, than pixel-based classification and its application is now widespread. Therefore, improvement of OBIA using spatial techniques is of great interest. In this paper, multiple-point statistics (MPS) is proposed for object-based classification enhancement in the form of a new multiple-point k-nearest neighbour (k-NN) classification method (MPk-NN). The proposed method first utilises a training image derived from a pre-classified map to characterise the spatial correlation between multiple points of land cover classes. The MPS borrows spatial structures from other parts of the training image, and then incorporates this spatial information, in the form of multiple-point probabilities, into the k-NN classifier. Two satellite sensor images with a fine spatial resolution were selected to evaluate the new method. One is an IKONOS image of the Beijing urban area and the other is a WorldView-2 image of the Wolong mountainous area, in China. The images were object-based classified using the MPk-NN method and several alternatives, including the k-NN, the geostatistically weighted k-NN, the Bayesian method, the decision tree classifier (DTC), and the support vector machine classifier (SVM). It was demonstrated that the new spatial weighting based on MPS can achieve greater classification accuracy relative to the alternatives and it is, thus, recommended as appropriate for object-based classification. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Multiple-point statistics k-NN object-based classification Training image

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Combining QuickBird, LiDAR, and GIS topography indices to identify a single native tree species in a complex landscape using an object-based classification approach

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2016年 50卷 187-197页

作者： Pham, Lien T. H. Brabyn, Lars Ashraf, Salman Univ Waikato Dept Geog Tourism & Environm Planning Private Bag 3105 Hamilton 3240 New Zealand Univ Sci Vietnam Natl Univ Ho Chi Minh City Fac Environm Sci 227 Nguyen Van Cu StrDist 5 Hcmc Vietnam GNS Sci POB 30368 Lower Hutt 5040 New Zealand

There are now a wide range of techniques that can be combined for image analysis. These include the use of object-based classifications rather than pixel-based classifiers, the use of LiDAR to determine vegetation height and vertical structure, as well terrain variables such as topographic wetness index and slope that can be calculated using GIS. This research investigates the benefits of combining these techniques to identify individual tree species. A QuickBird image and low point density LiDAR data for a coastal region in New Zealand was used to examine the possibility of mapping Pohutukawa trees which are regarded as an iconic tree in New Zealand. The study area included a mix of buildings and vegetation types. After image and LiDAR preparation, single tree objects were identified using a range of techniques including: a threshold of above ground height to eliminate ground based objects;Normalised Difference Vegetation Index and elevation difference between the first and last return of LiDAR data to distinguish vegetation from buildings;geometric information to separate clusters of trees from single trees, and treetop identification and region growing techniques to separate tree clusters into single tree crowns. Important feature variables were identified using Random Forest, and the Support Vector Machine provided the classification. The combined techniques using LiDAR and spectral data produced an overall accuracy of 85.4% (Kappa 80.6%). classification using just the spectral data produced an overall accuracy of 75.8% (Kappa 67.8%). The research findings demonstrate how the combining of LiDAR and spectral data improves classification for Pohutukawa trees. (C) 2016 Elsevier B.V. All rights reserved.

关键词： object-based classification Pohutukawa Random forest Support vector machine QuickBird LiDAR

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A Novel Wrapper Approach for Feature Selection in object-based Image classification Using Polygon-based Cross-Validation

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2017年第3期14卷 409-413页

作者： Ma, Lei Li, Manchun Gao, Yu Chen, Tan Ma, Xiaoxue Qu, Lean Nanjing Univ Jiangsu Prov Key Lab Geog Informat Sci & Technol Nanjing 210023 Jiangsu Peoples R China Nanjing Second Normal Univ Urban & Resources Environm Coll Nanjing 210013 Jiangsu Peoples R China

Feature selection is becoming a major component of object-based classification as numerous features of segmented object become available. Although common feature selection methods in object-based classification are acknowledged, wrapper-based methods remain an issue due to the diversity of accuracy assessment methods. This letter presents a new wrapper approach using polygon-based cross validation (CV) to overcome possible bias of object-based accuracy assessment for object-based classification. The new method is a two-step wrapper-based feature selection that involves the integration of: 1) feature importance rank using gain ratio and 2) feature subset evaluation using a polygon-based tenfold CV within a support vector machine (SVM) classifier. Several high-resolution images, including both unmanned aerial vehicle images and ISPRS (International Society for Photogrammetry and Remote Sensing) benchmark test data, were used to test the proposed method. Results show that, with the proposed polygon-based CV SVM wrapper, the mean overall accuracy is significantly higher than with an object-based CV SVM wrapper. Furthermore, the proposed method shows potential for comprehensively considering all types of features instead of only spectral features.

关键词： Cross validation (CV) feature selection object-based classification support vector machine (SVM) wrapper

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Comparison of sampling strategies for object-based classification of urban vegetation from Very High Resolution satellite images

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2016年 51卷 60-73页

作者： Rougier, Simon Puissant, Anne Stump, Andre Lachiche, Nicolas Univ Strasbourg CNRS UMR 7362 Lab ImageVilleEnvironmLIVE Strasbourg France Univ Strasbourg CNRS UMR 7516 IPGS Strasbourg France Univ Strasbourg UMR 7357 ICUBE Lab Sci Ingn Informat & Imagerie Strasbourg France

Vegetation monitoring is becoming a major issue in the urban environment due to the services they procure and necessitates an accurate and up to date mapping. Very High Resolution satellite images enable a detailed mapping of the urban tree and herbaceous vegetation. Several supervised classifications with statistical learning techniques have provided good results for the detection of urban vegetation but necessitate a large amount of training data. In this context, this study proposes to investigate the performances of different sampling strategies in order to reduce the number of examples needed. Two windows based active learning algorithms from state-of-art are compared to a classical stratified random sampling and a third combining active learning and stratified strategies is proposed. The efficiency of these strategies is evaluated on two medium size French cities, Strasbourg and Rennes, associated to different datasets. Results demonstrate that classical stratified random sampling can in some cases be just as effective as active learning methods and that it should be used more frequently to evaluate new active learning methods. Moreover, the active learning strategies proposed in this work enables to reduce the computational runtime by selecting multiple windows at each iteration without increasing the number of windows needed. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Urban vegetation mapping object-based classification VHR satellite images Sampling strategies evaluation Active learning Random forest

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COMBINING HIGH SPATIAL RESOLUTION OPTICAL AND LIDAR DATA FOR object-based IMAGE classification

COMBINING HIGH SPATIAL RESOLUTION OPTICAL AND LIDAR DATA FOR...

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The ISPRS Technical Commission III Midterm Symposium on "Developments, Technologies and Applications in Remote Sensing"

作者： Ruonan Li Tianyi Zhang Ruozheng Geng Leiguang Wang School of Forestry Southwest Forestry University Institutes of Big Data and Artificial Intelligence Southwest Forestry University

In order to classify high spatial resolution images more accurately, in this research, a hierarchical rule-based objectbased classification framework was developed based on a high-resolution image with airborne Light Detection and Ranging(LiDAR) data. The eCognition? software is employed to conduct the whole process. In detail, firstly, the FBSP optimizer(Fuzzy-based Segmentation Parameter) is used to obtain the optimal scale parameters for different land cover types. Then, using the segmented regions as basic units, the classification rules for various land cover types are established according to the spectral, morphological and texture features extracted from the optical images, and the height feature from Li DAR respectively. Thirdly, the object classification results are evaluated by using the confusion matrix, overall accuracy and Kappa coefficients. As a result, a method using the combination of an aerial image and the airborne Lidar data shows higher accuracy.

关键词： High Spatial Resolution Lidar object-based classification Aerial Image Image segmentation

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Land Cover Analysis by Using Pixel-based and object-based Image classification Method in Bogor 5

Land Cover Analysis by Using Pixel-Based and Object-Based Im...

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5th Geoinformation Science Symposium (GSS) on Bridging Geospatial Industries and Scientific Research

作者： Amalisana, Birohmatin Rokhmatullah Hernina, Revi Univ Indonesia Bachelor Program Geog Depok Indonesia Univ Indonesia Fac Math & Nat Sci Dept Geog Depok Indonesia

The advantage of image classification is to provide earth's surface information like landcover and time-series changes. Nowadays, pixel-based image classification technique is commonly performed with variety of algorithm such as minimum distance, parallelepiped, maximum likelihood, mahalanobis distance. On the other hand, landcover classification can also be acquired by using object-based image classification technique. In addition, object-based classification uses image segmentation from parameter such as scale, form, colour, smoothness and compactness. This research is aimed to compare the result of landcover classification and its change detection between parallelepiped pixel-based and object-based classification method. Location of this research is Bogor with 20 years range of observation from 1996 until 2016. This region is famous as urban areas which continuously change due to its rapid development, so that time-series landcover information of this region will be interesting.

关键词： land cover pixel-based classification object-based classification

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Estimation of biomass and carbon stock in Para rubber plantations using object-based classification from Thaichote satellite data in Eastern Thailand

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JOURNAL OF APPLIED REMOTE SENSING 2015年第1期9卷 096072-1-096072-14页

作者： Charoenjit, Kitsanai Zuddas, Pierpaolo Allemand, Pascal Pattanakiat, Sura Pachana, Katavut UPMC Univ Paris 04 Inst Sci Terre Paris F-75252 Paris France Univ Lyon 1 LGL TPE F-69622 Villeurbanne France Mahidol Univ Fac Environm & Resource Studies Geoinformat Resource & Environm Res & Training Ct Nakhon 73170 Pathom Thailand Burapha Univ Fac Sci Dept Chem Chon Buri 20131 Thailand

This paper deals with the efficiency of measurements of carbon stock by remote sensing techniques on Para rubber plantations in Thailand. These plantations could play an important role in carbon budget and thus are part of the Clean Development Mechanism of the Kyoto Protocol. Current methods of carbon stock estimations use middle resolution images and produce results with a large uncertainty. We use very high resolution images from the Thaichote satellite, associated with field measurements to estimate the carbon stock and its evolution in the Mae num Prasae watershed, Eastern Thailand. Using object-based classifications, the plantations have been mapped and their age has been estimated from a parametric model derived from both spectral and textural information and field data. The total biomass and carbon stocked are 2.23 and 0.99 Megaton with an uncertainty of 11%. One hundred and twenty one tons of carbon are sequestered annually in the Para rubber plantations of the studied area. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.

关键词： carbon stock estimation object-based classification Para rubber plantation Thaichote satellite Eastern Thailand

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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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A New Method for object-based Hyperspectral Image classification

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2022年第9期50卷 1761-1771页

作者： Akbari, Davood Ashrafi, Ali Attarzadeh, Reza Univ Zabol Fac Engn Dept Surveying & Geomat Engn Zabol Iran Univ Birjand Fac Literature & Humanities Dept Geog Birjand Iran Islamic Azad Univ Meybod Branch Meybod Yazd Iran

Hyperspectral remote sensing technology has many applications in the fields of land cover classification and examination of their changes. It seems necessary to use both spectral and spatial information in the hyperspectral image classification due to recent developments and the availability of images at higher spatial resolution. In this study, a new approach for object-based classification of hyperspectral images is introduced. In the proposed approach, first nine spatial features, including mean, standard deviation, contrast, homogeneity, correlation, dissimilarity, energy, wavelet transform and Gabor filter, are extracted from the neighboring pixels of the hyperspectral image. Then, the dimensions of the obtained features are reduced using weighted genetic (WG) algorithm. Next, the hierarchical segmentation (HSEG) algorithm is applied to the reduced features. Then, for the objects obtained from segmentation, nine spatial features, area, perimeter, shape index, strength, maximum intensity, minimum intensity, entropy, relation and adjacency, are extracted. Finally, the classification is performed using the multilayer perceptron neural network (MLP) algorithm. The proposed approach was implemented on three hyperspectral images of Indiana Pine, Berlin and Telops. According to the experimental results, the proposed approach is superior to the MLP classification method. This increase in the overall accuracy is about 12% for the Indiana Pine image, about 11% for the Berlin image, and about 8% for the Telops image.

关键词： Hyperspectral imaging object-based classification Spatial features MLP HSEG

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