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Spectral-texture feature extraction using statistical moments with application to object-based vegetation species classification

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INTERNATIONAL JOURNAL OF IMAGE AND DATA FUSION 2011年第4期2卷 347-361页

作者： Li, Zhengrong Hayward, Ross Liu, Yuee Walker, Rodney Queensland Univ Technol Fac Sci & Technol Brisbane Qld 4001 Australia Queensland Univ Technol Australian Res Ctr Aerosp Automat Brisbane Qld 4009 Australia Griffith Univ Sch Engn Brisbane Qld 4111 Australia

The use of appropriate features to characterise an output class or object is critical for all classification problems. In order to find optimal feature descriptors for vegetation species classification in a power line corridor monitoring application, this article evaluates the capability of several spectral and texture features. A new idea of spectral-texture feature descriptor is proposed by incorporating spectral vegetation indices in statistical moment features. The proposed method is evaluated against several classic texture feature descriptors. object-based classification method is used and a support vector machine is employed as the benchmark classifier. Individual tree crowns are first detected and segmented from aerial images and different feature vectors are extracted to represent each tree crown. The experimental results showed that the proposed spectral moment features outperform or can at least compare with the state-of-the-art texture descriptors in terms of classification accuracy. A comprehensive quantitative evaluation using receiver operating characteristic space analysis further demonstrates the strength of the proposed feature descriptors.

关键词： statistical moments spectral vegetation index texture object-based classification SVM

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A fuzzy rule base system for object-based feature extraction and classification

A fuzzy rule base system for object-based feature extraction...

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Conference on Signal Processing, Sensor Fusion, and Target Recognition XVI

作者： Jin, Xiaoying Paswaters, Scott ITT Visual Informat Solut 4990 Pearl East Circle Boulder CO 80301 USA

ISBN: (纸本)9780819466891

In this paper, we present a fuzzy rule base system for object-based feature extraction and classification on remote sensing imagery. First, the object primitives are generated from the segmentation step. object primitives are defined as individual regions with a set of attributes computed on the regions. The attributes computed include spectral, texture and shape measurements. Crisp rules are very intuitive to the users. They are usually represented as "GT (greater than)", "LT (less than)" and "IB (In Between)" with numerical values. The features can be manually generated by querying on the attributes using these crisp rules and monitoring the resulting selected object primitives. However, the attributes of different features are usually overlapping. The information is inexact and not suitable for traditional digital on/off decisions. Here a fuzzy rule base system is built to better model the uncertainty inherent in the data and vague human knowledge. Rather than representing attributes in linguistic terms like "Small", "Medium", "Large", we proposed a new method for automatic fuzzification of the traditional crisp concepts "GT", "LT" and "113". Two sets of membership functions are defined to model those concepts. One is based on the piecewise linear functions, the other is based on Stype membership functions. A novel concept "fuzzy tolerance" is proposed to control the degree of fuzziness of each rule. The experimental results on classification and extracting features such as water, buildings, trees, fields and urban areas have shown that this newly designed fuzzy rule base system is intuitive and allows users to easily generate fuzzy rules.

关键词： fuzzy logic rule base feature extraction object-based classification very high resolution imagery degree of uzziness membership functions

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object-based Multispectral Image Segmentation and classification 7

Object-based Multispectral Image Segmentation and Classifica...

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7th International Symposium on Telecommunications (IST)

作者： Mirzapour, Fardin Ghassemian, Hassan Tathiat Modares Univ Fac Elect & Comp Engn Tehran Iran

ISBN: (纸本)9781479953592

In this paper an object-based method for multispectral image segmentation and classification is proposed. Normally, in remote sensing a scene is represented by pixel-based features. It is possible to reduce data redundancy by a segment-feature extraction process, where the segment-features, rather than the pixel-features, are used for multispectral scene representation and classification. object-based algorithms partition the observation space into a set of disjoint segments (called objects). Then, pixels belonging to each segment are represented by segment features. In this paper, an unsupervised segmentation algorithm based on statistical region merging (SRM) framework is presented. Also a partial differential equations (PDE) algorithm is suggested as a preprocessing phase to improve the segmentation results. Illustrative examples are presented, and the performance of the extracted object features for classification purposes is investigated. Results show significant classification performance improvement.

关键词： Multispectral Images Remote Sensing Segmentation object-based classification Image compression

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object-based LAND COVER classification FOR ALOS IMAGE COMBINING TM SPECTRAL INFORMATION

OBJECT-BASED LAND COVER CLASSIFICATION FOR ALOS IMAGE COMBIN...

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ISPRS Conference on Serving Society with Geoinformatics (SSG)

作者： Wang, Guizhou Liu, Jianbo He, Guojin Chinese Acad Sci Inst Remote Sensing & Digital Earth Beijing 100094 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9781629935201

Land cover classification for high spatial resolution remote sensing images becomes a challenging work. The high spatial resolution remote sensing images have more spatial information. The low or medium resolution remote sensing images have more spectral information. In order to improve the accuracy of high spatial resolution remote sensing image classification, additional information should be incorporated into the classification process of high spatial resolution remote sensing image. This paper proposed a method of object-based land cover classification for high spatial resolution ALOS images combining the spectral information of TM images. First, the high spatial resolution ALOS panchromatic image was segmented by multi-resolution segmentation method. Second, the spectral features of segmented regions were extracted from multi-spectral ALOS image and TM image by spatial mapping mechanism. Third, the regions were classified by SVM classifier. Experimental results show that the classification method for high spatial resolution remote sensing images combining the TM spectral information based on the spatial mapping mechanism can make use of the spectral information both in high and low spatial resolution remote sensing images and improve classification accuracy.

关键词： object-based classification High spatial resolution remote sensing image Spatial mapping mechanism

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WorldView-2 Data for Hierarchical object-based Urban Land Cover classification in Kigali: Integrating Rule-based Approach with Urban Density and Greenness Indices

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REMOTE SENSING 2019年第18期11卷 2128-2128页

作者： Mugiraneza, Theodomir Nascetti, Andrea Ban, Yifang KTH Royal Inst Technol Dept Urban Planning & Environm Div Geoinformat Teknikringen 10A S-10044 Stockholm Sweden Univ Rwanda Coll Sci & Technol Ctr Geog Informat Syst & Remote Sensing KN 67 StPOB 3900 Nyarugenge Kigali Rwanda

The emergence of high-resolution satellite data, such as WorldView-2, has opened the opportunity for urban land cover mapping at fine resolution. However, it is not straightforward to map detailed urban land cover and to detect urban deprived areas, such as informal settlements, in complex urban environments based merely on high-resolution spectral features. Thus, approaches integrating hierarchical segmentation and rule-based classification strategies can play a crucial role in producing high quality urban land cover maps. This study aims to evaluate the potential of WorldView-2 high-resolution multispectral and panchromatic imagery for detailed urban land cover classification in Kigali, Rwanda, a complex urban area characterized by a subtropical highland climate. A multi-stage object-based classification was performed using support vector machines (SVM) and a rule-based approach to derive 12 land cover classes with the input of WorldView-2 spectral bands, spectral indices, gray level co-occurrence matrix (GLCM) texture measures and a digital terrain model (DTM). In the initial classification, confusion existed among the informal settlements, the high- and low-density built-up areas, as well as between the upland and lowland agriculture. To improve the classification accuracy, a framework based on a geometric ruleset and two newly defined indices (urban density and greenness density indices) were developed. The novel framework resulted in an overall classification accuracy at 85.36% with a kappa coefficient at 0.82. The confusion between high- and low-density built-up areas significantly decreased, while informal settlements were successfully extracted with the producer and user's accuracies at 77% and 90% respectively. It was revealed that the integration of an object-based SVM classification of WorldView-2 feature sets and DTM with the geometric ruleset and urban density and greenness indices resulted in better class separability, thus higher classification accur

关键词： WorldView-2 high-resolution object-based classification Support Vector Machine geometric ruleset urban density index greenness density index urban land cover informal settlements

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object-based Image Analysis for Coral Reef Benthic Habitat Mapping with Several classification Algorithms

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Procedia Environmental Sciences 2015年 24卷 222-227页

作者： Nurhalis Wahidin Vincentius P. Siregar Bisman Nababan Indra Jaya Sam Wouthuyzen Marine Technology Graduate Program Faculty of Fisheries and Marine Sciences Bogor Agricultural University Bogor Indonesia Faculty of Fisherie and Marine Science Khiarun University Ternate Indonesia Department of Marine Science and Technology Bogor Agricultural University Bogor Indonesia Oceanographic Research Center Indonesian Institute of Science Jakarta Indonesia

object-based classification technique (OBIA) in Landsat image analysis is assumed to be able to increase classification accuracy result compared with a pixel-based classification technique. Most of the previous research was conducted using conventional (pixel-based) classification technique such as maximum likelihood algorithm. The objective of this research was to determine accuracy values of coral reef habitat classification based on OBIA algorithms such as Support Vector Machine (SVM), Random Tree, Decision Tree (DT), Bayesian, and k-Nearest Neighbour (KNN). The Landsat 8 OLI satellite imagery acquired on 17 October 2013 on coral reef area in Morotai island, North Maluku Province, Indonesia was analysed in this study. Field data was obtained in October 2012 using transect photo technique. The field data was then classified employing OBIA classification within 7 (seven) classes i.e., sand-rubble, rubble, coral, sand, sand-algae, seagrass, and rubble-sand. Results showed that the highest values of overall accuracy and Kappa on coral reef habitat mapping was produced by SVM algorithm which reached 73% and 0.64 followed by RT, KNN, Bayesian, and DT algorithms of 68% and 0.59, 67% and 0.56, 66% and 0.53, and 56% and 0.46, respectively. Those results showed that the classification methods based OBIA approach within > 6 classes produced better accuracy than the pixel based classification technique.

关键词： object-based classification pixel-based classification coral reef benthic habitat overall accuracy Kappa Morotai island

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Dimensionality Reduction and Feature Selection for object-based Land Cover classification based on Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine

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REMOTE SENSING 2020年第1期12卷 76-76页

作者： Stromann, Oliver Nascetti, Andrea Yousif, Osama Ban, Yifang KTH Royal Inst Technol Dept Urban Planning & Environm Div Geoinformat Teknikringen 10 S-14428 Stockholm Sweden

Mapping Earth's surface and its rapid changes with remotely sensed data is a crucial task to understand the impact of an increasingly urban world population on the environment. However, the impressive amount of available Earth observation data is only marginally exploited in common classifications. In this study, we use the computational power of Google Earth Engine and Google Cloud Platform to generate an oversized feature set in which we explore feature importance and analyze the influence of dimensionality reduction methods to object-based land cover classification with Support Vector Machines. We propose a methodology to extract the most relevant features and optimize an SVM classifier hyperparameters to achieve higher classification accuracy. The proposed approach is evaluated in two different urban study areas of Stockholm and Beijing. Despite different training set sizes in the two study sites, the averaged feature importance ranking showed similar results for the top-ranking features. In particular, Sentinel-2 NDVI, NDWI, and Sentinel-1 VV temporal means are the highest ranked features and the experiment results strongly indicated that the fusion of these features improved the separability between urban land cover and land use classes. Overall classification accuracies of 94% and 93% were achieved in Stockholm and Beijing study sites, respectively. The test demonstrated the viability of the methodology in a cloud-computing environment to incorporate dimensionality reduction as a key step in the land cover classification process, which we consider essential for the exploitation of the growing Earth observation big data. To encourage further research and development of reliable workflows, we share our datasets and publish the developed Google Earth Engine and Python scripts as free and open-source software.

关键词： EO big data SAR MSI Google Earth Engine object-based classification

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object-based vs. Pixel-based classification of Mangrove Forest Mapping in Vien An Dong Commune, Ngoc Hien District, Ca Mau Province Using VNREDSat-1 Images

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Advances in Remote Sensing 2016年第4期5卷 284-295页

作者： Nguyen Thi Quynh Trang Le Quang Toan Tong Thi Huyen Ai Nguyen Vu Giang Pham Viet Hoa Space Technology Institute Vietnam Academy of Science and Technology Hanoi Vietnam Ho Chi Minh Institute of Resource Geography Vietnam Academy of Science and Technology Ho Chi Minh City Vietnam

Many researches have been performed comparing object-based classification (OBC) and pixel-based classification (PBC), particularly in classifying high-resolution satellite images. VNREDSat-1 is the first optical remote sensing satellite of Vietnam with resolution of 2.5 m (Panchromatic) and 10 m (Multispectral). The objective of this research is to compare two classification approaches using VNREDSat-1 image for mapping mangrove forest in Vien An Dong commune, Ngoc Hien district, Ca Mau province. ISODATA algorithm (in PBC method) and membership function classifier (in OBC method) were chosen to classify the same image. The results show that the overall accuracies of OBC and PBC are 73% and 62.16% respectively, and OBC solved the “salt and pepper” which is the main issue of PBC as well. Therefore, OBC is supposed to be the better approach to classify VNREDSat-1 for mapping mangrove forest in Ngoc Hien commune.

关键词： object-based classification Pixel-based classification VNREDSat-1 Mangrove Forest Ca Mau

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AdaBoosted Extra Trees Classifier for object-based Multispectral Image classification of Urban Fringe Area

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INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS 2022年第3期22卷

作者： Patel, Alpesh M. Suthar, Anil Vishwakarma Govt Engn Coll Dept Elect & Commun Chandkheda India Gujarat Technol Univ Ahmadabad 382424 Gujarat India LJ Inst Engn & Technol Ahmadabad Gujarat India

In the past decade, it is proven that satellite image classification using an object-based technique is better than the standard pixel-based technique. With the increasing need for classifying multispectral satellite images for urban planning, the accuracy of the classification becomes a significant performance parameter. object-based classification (OBC) is a technique in which group of pixels having similar spectral properties, called objects, are generated using image segmentation and then these objects are classified based on their attributes. In this paper, the combination of a multiclass AdaBoost algorithm with extra trees classifier (ETC) is proposed with higher prediction accuracy for the OBC of the urban fringe area. The performance of the AdaBoost algorithm is found to be better in terms of classification accuracy than benchmarked SVM and RF classifiers for OBC. These classification methods were applied to IRS-R2 LISS IV data. The AdaBoosted extra trees classifier (ABETC) has demonstrated the highest accuracy with overall accuracy (OA) of 88.47% and a kappa coefficient of 0.85. The computational time of the ABETC is found to be much smaller than the RF algorithm. In detail, the sensitivity of the classifiers was investigated using stratified random sampling with various sample sizes.

关键词： Extra trees classifier image classification object-based classification random forest

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Enhancing Land Cover Mapping through Integration of Pixel-based and object-based classifications from Remotely Sensed Imagery

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REMOTE SENSING 2018年第1期10卷 77-77页

作者： Chen, Yuehong Zhou, Ya'nan Ge, Yong An, Ru Chen, Yu Hohai Univ Sch Earth Sci & Engn Nanjing 210098 Jiangsu Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res State Key Lab Resources & Environm Informat Syst Beijing 100101 Peoples R China Nanjing Normal Univ Sch Geog Sci Nanjing 210023 Jiangsu Peoples R China

Pixel-based and object-based classifications are two commonly used approaches in extracting land cover information from remote sensing images. However, they each have their own inherent merits and limitations. This study, therefore, proposes a new classification method through the integration of pixel-based and object-based classifications (IPOC). Firstly, it employs pixel-based soft classification to obtain the class proportions of pixels to characterize the land cover details from pixel-scale properties. Secondly, it adopts area-to-point kriging to explore the class spatial dependence between objects for each pixel from object-based soft classification results. Thirdly, the class proportions of pixels and the class spatial dependence of pixels are fused as the class occurrence of pixels. Last, a linear optimization model on objects is built to determine the optimal class label of pixels within each object. Two remote sensing images are used to evaluate the effectiveness of IPOC. The experimental results demonstrate that IPOC performs better than the traditional pixel-based hard classification and object-based hard classification methods. Specifically, the overall accuracy of IPOC is 7.64% higher than that of pixel-based hard classification and 4.64% greater than that of object-based hard classification in the first experiment, while the overall accuracy improvements in the second experiment are 3.59% and 3.42%, respectively. Meanwhile, IPOC produces less salt and pepper effect than the pixel-based hard classification method and generates more accurate land cover details and small patches than the object-based hard classification method.

关键词： land cover mapping mixed object uncertainty pixel-based classification object-based classification integration

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