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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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Potential of Multi-scale Completed Local Binary Pattern for object based classification of Very High Spatial Resolution Imagery

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2021年第6期49卷 1245-1255页

作者： Chairet, Radhia Ben Salem, Yassine Aoun, Mohamed Univ Gabes Res Lab Modeling Anal & Control Syst MACS Natl Engn Sch Gabes ENIG Gabes Tunisia

This paper explores the potentiality of using the completed local binary pattern (CLBP) for the classification of an urbanized oasis area situated in southeastern Tunisia, in very high spatial resolution GeoEye imagery. To further improve the spatial information description derived by CLBP, which is successfully used in face recognition, we applied the multi-scale completed local binary pattern (MSCLBP) to classify the single-dated image. A supervised classification framework preceded by mean-shift segmentation is applied using the texture features alone and combined with the normalized difference vegetation index (NDVI). As the segmentation is a crucial step to obtain a good mapping, considerations are given to select the optimal combination of mean-shift input parameters, such as spatial radius and range radius. The results of this study indicate that MSCLBP outperforms the single-scale CLBP and the gray-level co-occurrence matrix (GLCM) descriptors in the task of expressing the classes of interests.

关键词： Features extraction object-based classification CLBP MSCLBP GLCM SVM

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Integration of case-based reasoning and object-based image classification to classify SPOT images: a case study of aquaculture land use mapping in coastal areas of Guangdong province, China

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GISCIENCE & REMOTE SENSING 2013年第5期50卷 574-589页

作者： Du, Yunyan Wu, Di Liang, Fuyuan Li, Ce Chinese Acad Sci Inst Geog Sci & Nat Resources Res State Key Lab Resources & Environm Informat Syst Beijing 100101 Peoples R China Western Illinois Univ Dept Geog Macomb IL 61455 USA Chinese Acad Sci Inst Remote Sensing & Digital Earth Beijing 100094 Peoples R China

We present a method to integrate case-based reasoning (CBR) with object-oriented image classification to classify SPOT images. Images were first segmented into discrete objects at multiple scales. CBR was then used to classify these objects by comparing their geometric shapes, spectral characteristics, and textural measurements with those of the past objects prepared from archived SPOT images and land use data. Once enough past objects were accumulated, this method was able to successfully classify image objects with promising results as demonstrated by a case study of aquaculture land use mapping in coastal areas of Guangdong province, China.

关键词： object-based classification case-based reasoning aquaculture land use mapping

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SUPERVISED OPTIMAL SCALE PARAMETER ESTIMATION FOR MULTISCALE object-based LANDCOVER classification 39

SUPERVISED OPTIMAL SCALE PARAMETER ESTIMATION FOR MULTISCALE...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Hu, Zhongwen Wang, Chisheng Liu, Peng Shenzhen Univ Coll Life Sci & Oceanog Shenzhen 518060 Peoples R China Shenzhen Univ Sch Architecture & Urban Planning Shenzhen 518060 Peoples R China Shenzhen Univ Key Lab Geoenvironm Monitoring Coastal Zone Natl Adm Surveying Mapping & Geoinformat Shenzhen Peoples R China Southern Univ Sci & Technol SUSTech Acad Adv Interdisciplinary Studies Dept Earth & Space Sci Shenzhen 518060 Peoples R China

ISBN: (纸本)9781538691540

Scale parameter selection is a key step in an object-based image analysis (OBIA) work. In existing works, the first step is the selection of optimal scale parameter, followed by feature description and later analysis. However, only low-level image features are used at this step, which are not directly related to the purpose of the application. To overcome the limitation, we propose a multiscale object-based image analysis framework, in which, the multiscale classification is performed first, and the optimal scale parameter is estimated using the multiscale classification results and training samples. The experiments have demonstrated the effectiveness of our approach in estimating optimal scale parameter for object-based landcover classification, and showed great potential in automatic analysis of high spatial resolution remote sensing images.

关键词： object-based classification optimal scale estimation landcover classification remote sensing

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PIXEL VS object-based IMAGE classification TECHNIQUES FOR LIDAR INTENSITY DATA

PIXEL VS OBJECT-BASED IMAGE CLASSIFICATION TECHNIQUES FOR LI...

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ISPRS Workshop on Laser Scanning

作者： El-Ashmawy, Nagwa Shaker, Ahmed Yan, Wai Yeung Ryerson Univ Dept Civil Engn Toronto ON M5B 2K3 Canada Natl Water Res Ctr Survey Res Inst Cairo Egypt

Light Detection and Ranging (LiDAR) systems are remote sensing techniques used mainly for terrain surface modelling. LiDAR sensors record the distance between the sensor and the targets (range data) with a capability to record the strength of the backscatter energy reflected from the targets (intensity data). The LiDAR sensors use the near-infrared spectrum range which provides high separability in the reflected energy by the target. This phenomenon is investigated to use the LiDAR intensity data for land-cover classification. The goal of this paper is to investigate and evaluates the use of different image classification techniques applied on LiDAR intensity data for land cover classification. The two techniques proposed are: a) Maximum likelihood classifier used as pixel-based classification technique;and b) Image segmentation used as object-based classification technique. A study area covers an urban district in Burnaby, British Colombia, Canada, is selected to test the different classification techniques for extracting four feature classes: buildings, roads and parking areas, trees, and low vegetation (grass) areas, from the LiDAR intensity data. Generally, the results show that LiDAR intensity data can be used for land cover classification. An overall accuracy of 63.5% can be achieved using the pixel-based classification technique. The overall accuracy of the results is improved to 68% using the object-based classification technique. Further research is underway to investigate different criteria for segmentation process and to refine the design of the object-based classification algorithm.

关键词： LiDAR intensity data object-based classification decision tree

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Semi-automatic extraction of land degradation processes using multi sensor data by applying object based classification technique

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APPLIED GEOMATICS 2023年第1期15卷 239-248页

作者： Raghubanshi, Sudhanshu Agrawal, Ritesh Rajawat, A. S. Rajak, D. Ram Space Applicat Ctr ISRO Ahmadabad 380015 India

A semi-automated method has been developed for the extraction of land degradation processes using multi sensor data by applying an object-based classification. The object-based approach creates homogenous objects, which is the key component of this classification. The study utilized optical satellite (Landsat-8), microwave (RISAT-1, SAR) and Cartosat-1 digital elevation model (DEM) over Kanpur Dehat district, Uttar Pradesh, and Surendranagar district, Gujarat, India. The objects were created using Shepherd segmentation algorithm. Normalized difference vegetation index (NDVI) was used to classify the degraded and no apparent degradation (NAD) objects based on the three seasons (rabi, summer, and kharif) Landsat-8 bands. Degraded objects were further classified into salinity, forest water erosion, and water logging using brightness index based on Landsat-8, proximity analysis near the river channel using RISAT-1, and low-lying area using DEM, respectively. The digitally generated results were validated with manual digitized desertification status maps (DSM) published by Space Applications Centre, Ahmedabad, India. The overall accuracy and kappa coefficient for Kanpur Dehat and Surendranagar districts were found 84.67%, 0.79 and 72.33%, 0.60, respectively. This study was carried out based on integrated analysis of different satellites (optical, microwave, and DEM). The advantage of newly designed framework offers less chance of mixing and narrowing down of the area for further classification with better accuracy. The developed framework is based on analytical approach, which was tested and implemented in the Python environment with efficient computing power. The study illustrates that the developed approach is independent of climatic-topographic conditions and executed over pilot study sites, which could be extended over larger regions of the land use/land cover for land degradation mapping.

关键词： Land degradation object-based classification NDVI DSM classification accuracy Microwave Analytical approach

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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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object-based IMAGE classification UTILIZING BACKGROUND KNOWLEDGE: A CASE STUDY OF LAND USE classification

OBJECT-BASED IMAGE CLASSIFICATION UTILIZING BACKGROUND KNOWL...

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Joint International Conference on Theory, Data Handling and Modelling in GeoSpatial Information Science

作者： Zhang, T. Yang, X. M. Zhou, C. H. Su, F. Z. Gong, J. M. Du, Y. Y. Chinese Acad Sci State Key Lab Resources & Environm Informat Syst IGSNRR Beijing Peoples R China

object-based image classification and information extraction approaches are rapidly developing from the beginning of this century, but the automatic procedure for land use mapping are still problematic facing with land use complexity. This paper presents a method of incorporating background knowledge into the object-based image classification procedure, intending to improve the classification accuracy and boundary consistency of land use data. Two forms of knowledge are used in this paper: the expert interpreted land use polygons and the land use change rules. The idea of this paper is tested on the platform of Definiens Developer 7 (trial version). The proposed approach mainly contains three parts: segmentation supported by land-use thematic layer, classification and change detection supported by land-use change rules. The experiment result shows that the proposed procedures have good potential for automatic land use mapping.

关键词： object-based classification background knowledge change detection land use CBERS

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

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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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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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