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Assessing Texture Features to Classify Coastal Wetland Vegetation from High Spatial Resolution Imagery Using Completed Local Binary Patterns (CLBP)

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REMOTE SENSING 2018年第5期10卷 778-778页

作者： Wang, Minye Fei, Xianyun Zhang, Yuanzhi Chen, Zhou Wang, Xiaoxue Tsou, Jin Yeu Liu, Dawei Lu, Xia HuaiHai Inst Technol Sch Geomat & Marine Informat Lianyungang 222002 Peoples R China Chinese Acad Sci Key Lab Lunar Sci & Deep Space Explorat Natl Astron Observ Beijing 100101 Peoples R China Chinese Univ Hong Kong Ctr Housing Innovat Shatin Hong Kong Peoples R China

Coastal wetland vegetation is a vital component that plays an important role in environmental protection and the maintenance of the ecological balance. As such, the efficient classification of coastal wetland vegetation types is key to the preservation of wetlands. based on its detailed spatial information, high spatial resolution imagery constitutes an important tool for extracting suitable texture features for improving the accuracy of classification. In this paper, a texture feature, Completed Local Binary Patterns (CLBP), which is highly suitable for face recognition, is presented and applied to vegetation classification using high spatial resolution Pleiades satellite imagery in the central zone of Yancheng National Natural Reservation (YNNR) in Jiangsu, China. To demonstrate the potential of CLBP texture features, Grey Level Co-occurrence Matrix (GLCM) texture features were used to compare the classification. Using spectral data alone and spectral data combined with texture features, the image was classified using a Support Vector Machine (SVM) based on vegetation types. The results show that CLBP and GLCM texture features yielded an accuracy 6.50% higher than that gained when using only spectral information for vegetation classification. However, CLBP showed greater improvement in terms of classification accuracy than GLCM for Spartina alterniflora. Furthermore, for the CLBP features, CLBP_magnitude (CLBP_m) was more effective than CLBP_sign (CLBP_s), CLBP_center (CLBP_c), and CLBP_s/m or CLBP_s/m/c. These findings suggest that the CLBP approach offers potential for vegetation classification in high spatial resolution images.

关键词： coastal wetland vegetation feature extraction completed local binary patterns (CLBP) object-based classification

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Image Segmentation Parameter Selection and Ant Colony Optimization for Date Palm Tree Detection and Mapping from Very-High-Spatial-Resolution Aerial Imagery

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REMOTE SENSING 2018年第9期10卷 1413-1413页

作者： Al-Ruzouq, Rami Shanableh, Abdallah Gibril, Mohamed Barakat A. AL-Mansoori, Saeed Univ Sharjah Dept Civil & Environm Engn Sharjah 27272 U Arab Emirates Univ Sharjah Res Inst Sci & Engn Sharjah 27272 U Arab Emirates MBRSC Applicat Dev & Anal Sect Dubai 211833 U Arab Emirates

Accurate mapping of date palm trees is essential for their sustainable management, yield estimation, and environmental studies. In this study, we integrated geographic object-based image analysis, class-specific accuracy measures, fractional factorial design, metaheuristic feature-selection technique, and rule-based classification to detect and map date palm trees from very-high-spatial-resolution (VHSR) aerial images of two study areas. First, multiresolution segmentation was optimized through the synergy of the Fl-score accuracy measure and the robust Taguchi design. Second, ant colony optimization (ACO) was adopted to select the most significant features. Out of 31 features, only 12 significant color invariants and textural features were selected. Third, based on the selected features, the rule-based classification with the aid of a decision tree algorithm was applied to extract date palm trees. The proposed methodology was developed on a subset of the first study area, and ultimately applied to the second study area to investigate its efficiency and transferability. To evaluate the proposed classification scheme, various supervised object-based algorithms, namely random forest (RF), support vector machine (SVM), and k-nearest neighbor (k-NN), were applied to the first study area. The result of image segmentation optimization demonstrated that segmentation optimization based on an integrated Fl-score class-specific accuracy measure and Taguchi statistical design showed improvement compared with objective function, along with the Taguchi design. Moreover, the result of the feature selection by ACO outperformed, with almost 88% overall accuracy, several feature-selection techniques, such as chi-square, correlation-based feature selection, gain ratio, information gain, support vector machine, and principal component analysis. The integrated framework for palm tree detection outperformed RF, SVM, and k-NN classification algorithms with an overall accuracy of 91.88% a

关键词： object-based classification very-high-resolution aerial imagery image segmentation optimization feature selection ant colony optimization date palm tree

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Gully Erosion Mapping Using object-based and Pixel-based Image classification Methods

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ENVIRONMENTAL & ENGINEERING GEOSCIENCE 2015年第2期21卷 101-110页

作者： Karami, Ayoob Khoorani, Asadollah Noohegar, Ahmad Shamsi, Seyed Rashid Fallah Moosavi, Vahid Hormozgan Univ Fac Nat Resources Bandar Bbbas Hormozgan Provi Iran Univ Tehran Fac Nat Resources Karaj Iran Shiraz Univ Coll Agr Shiraz Shiraz Province Iran Yazd Univ Fac Nat Resources Yazd Yzad Province Iran

Gully erosion mapping is a crucial step to monitor the erosion process and to study its current and future local impacts. Gully erosion mapping through fieldwork is difficult, time-consuming, and costly. This article compares various pixel-based image classification (PBC) algorithms, such as ISODATA, Maximum Likelihood classification, and Support Vector Machine, with the object-based image analysis (OBIA) technique for gully erosion mapping on IRS-P6 images. Six models defined by classification types, classifiers, and feature spaces were built for comparison. The results show that OBIA classification performed better than PBC in terms of accuracy. We also found that the improvement of OBIA was primarily due to employing textural and shape features and optimized feature space, while the use of standard feature space did not improve OBIA. In addition, OBIA significantly reduced the salt-and-pepper effect that obscures the features on the output maps compared to the PBC maps (which had more salt-and-pepper effects). It seems that object-based techniques have yielded better results because of their focus on the shape of gully networks rather than on their spectral heterogeneity. In order to improve the accuracy, a priority may be gained by fully exploring the use of membership function and hierarchical approach with multi-scale segmentation for gully mapping. In future studies we propose to determine how these factors can affect the performance of OBIA in terms of gully mapping. This study provides information on the location of gullies, gully dynamics over a period of time, and the degree of land degradation (gully density) for developing and implementing soil conservation measures.

关键词： Gully Erosion object-based classification Digital Mapping IRS-P6 Iran

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Greenhouse Crop Identification from Multi-Temporal Multi-Sensor Satellite Imagery Using object-based Approach: A Case Study from Almeria (Spain)

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REMOTE SENSING 2018年第11期10卷 1751-1751页

作者： Nemmaoui, Abderrahim Aguilar, Manuel A. Aguilar, Fernando J. Novelli, Antonio Garcia Lorca, Andres Univ Almeria Dept Engn Ctra Sacramento S-N La Canada De San Urbano 04120 Almeria Spain Eurac Res Inst Renewable Energy Via A Volta 13-A I-39100 Bolzano Italy Univ Almeria Dept Geog Ctra Sacramento S-N Almeria 04120 Spain

A workflow headed up to identify crops growing under plastic-covered greenhouses (PCG) and based on multi-temporal and multi-sensor satellite data is developed in this article. This workflow is made up of four steps: (i) data pre-processing, (ii) PCG segmentation, (iii) binary pre-classification between greenhouses and non-greenhouses, and (iv) classification of horticultural crops under greenhouses regarding two agronomic seasons (autumn and spring). The segmentation stage was carried out by applying a multi-resolution segmentation algorithm on the pre-processed WorldView-2 data. The free access AssesSeg command line tool was used to determine the more suitable multi-resolution algorithm parameters. Two decision tree models mainly based on the Plastic Greenhouse Index were developed to perform greenhouse/non-greenhouse binary classification from Landsat 8 and Sentinel-2A time series, attaining overall accuracies of 92.65% and 93.97%, respectively. With regards to the classification of crops under PCG, pepper in autumn, and melon and watermelon in spring provided the best results (F around 84% and 95%, respectively). Data from the Sentinel-2A time series showed slightly better accuracies than those from Landsat 8.

关键词： Landsat 8 Sentinel-2 WorldView-2 time series object-based classification greenhouse mapping crop types classification

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classification of semiurban landscapes from very high-resolution satellite images using a regionalized multiscale segmentation approach

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JOURNAL OF APPLIED REMOTE SENSING 2017年第3期11卷

作者： Kavzoglu, Taskin Erdemir, Merve Yildiz Tonbul, Hasan Gebze Tech Univ Dept Geomat Engn Gebze Turkey

In object-based image analysis, obtaining representative image objects is an important prerequisite for a successful image classification. The major threat is the issue of scale selection due to the complex spatial structure of landscapes portrayed as an image. This study proposes a two-stage approach to conduct regionalized multiscale segmentation. In the first stage, an initial high-level segmentation is applied through a "broadscale," and a set of image objects characterizing natural borders of the landscape features are extracted. Contiguous objects are then merged to create regions by considering their normalized difference vegetation index resemblance. In the second stage, optimal scale values are estimated for the extracted regions, and multiresolution segmentation is applied with these settings. Two satellite images with different spatial and spectral resolutions were utilized to test the effectiveness of the proposed approach and its transferability to different geographical sites. Results were compared to those of image-based single-scale segmentation and it was found that the proposed approach outperformed the single-scale segmentations. Using the proposed methodology, significant improvement in terms of segmentation quality and classification accuracy (up to 5%) was achieved. In addition, the highest classification accuracies were produced using fine-scale values. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： object-based image analysis object-based classification very high-resolution satellite images multiscale segmentation landscape dynamics

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Characterization of particulate matter deposited on urban tree foliage: A landscape analysis approach

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ATMOSPHERIC ENVIRONMENT 2017年 171卷 59-69页

作者： Lin, Lin Yan, Jingli Ma, Keming Zhou, Weiqi Chen, Guojian Tang, Rongli Zhang, Yuxin Chinese Acad Sci Res Ctr Ecoenvironm Sci State Key Lab Urban & Reg Ecol Shuangqing Rd 18 Beijing 100085 Peoples R China Univ Chinese Acad Sci 19 A Yuquan Rd Beijing 100049 Peoples R China Univ Calif Riverside Dept Bot & Plant Sci 900 Univ Ave Riverside CA 92521 USA Chongqing Acad Agr Sci Baixin Rd 100 Chongqing Peoples R China

Plants can mitigate ambient particulate matter by cleaning the air, which is crucial to urban environments. A novel approach was presented to quantitatively characterize particulate matter deposited on urban tree foliage. This approach could accurately quantify the number, size, shape, and spatial distribution of particles with different diameters on leaves. Spatial distribution is represented by proximity, which measures the closeness of particles. We sampled three common broadleaf species and obtained images through field emission scanning electron microscopy. We conducted the object-based method to extract particles from images. We then used Fragstats to analyze the landscape characteristics of these particles in term of selected metrics. Results reveal that Salix matsudana is more efficient than Ailanthus altissima and Fraxinus chinensis in terms of the number and area of particles per unit area and the proportion of fine particulate matter. The shape complexity of the particles increases with their size. Among the three species, S. matsudana and A. altissima particles respectively yield the highest and lowest proximity. PM1 in A. altissima and PMio in F. chinensis and S. matsudana show the highest proximity, which may influence subsequent particle retention. S. matsudana should be generally considered to collect additional small particles. Different species and particle sizes exhibit various proximities, which should be further examined to elucidate the underlying mechanism. (C) 2017 Published by Elsevier Ltd.

关键词： Air pollution FESEM Fragstats object-based classification Spatial characteristic Urban forestry

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object-based Crop Species classification based on the Combination of Airborne Hyperspectral Images and LiDAR Data

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REMOTE SENSING 2015年第1期7卷 922-950页

作者： Liu, Xiaolong Bo, Yanchen Beijing Normal Univ Res Ctr Remote Sensing State Key Lab Remote Sensing Sci Beijing 100875 Peoples R China Beijing Normal Univ GIS Beijing 100875 Peoples R China Beijing Normal Univ Sch Geog Beijing 100875 Peoples R China Beijing Key Lab Remote Sensing Environm & Digital Beijing 100875 Peoples R China

Identification of crop species is an important issue in agricultural management. In recent years, many studies have explored this topic using multi-spectral and hyperspectral remote sensing data. In this study, we perform dedicated research to propose a framework for mapping crop species by combining hyperspectral and Light Detection and Ranging (LiDAR) data in an object-based image analysis (OBIA) paradigm. The aims of this work were the following: (i) to understand the performances of different spectral dimension-reduced features from hyperspectral data and their combination with LiDAR derived height information in image segmentation;(ii) to understand what classification accuracies of crop species can be achieved by combining hyperspectral and LiDAR data in an OBIA paradigm, especially in regions that have fragmented agricultural landscape and complicated crop planting structure;and (iii) to understand the contributions of the crop height that is derived from LiDAR data, as well as the geometric and textural features of image objects, to the crop species' separabilities. The study region was an irrigated agricultural area in the central Heihe river basin, which is characterized by many crop species, complicated crop planting structures, and fragmented landscape. The airborne hyperspectral data acquired by the Compact Airborne Spectrographic Imager (CASI) with a 1 m spatial resolution and the Canopy Height Model (CHM) data derived from the LiDAR data acquired by the airborne Leica ALS70 LiDAR system were used for this study. The image segmentation accuracies of different feature combination schemes (very high-resolution imagery (VHR), VHR/CHM, and minimum noise fractional transformed data (MNF)/CHM) were evaluated and analyzed. The results showed that VHR/CHM outperformed the other two combination schemes with a segmentation accuracy of 84.8%. The object-based crop species classification results of different feature integrations indicated that incorporating the cr

关键词： crop species classification hyperspectral remote sensing LiDAR data object-based classification image segmentation

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Comparing Machine Learning Classifiers for object-based Land Cover classification Using Very High Resolution Imagery

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REMOTE SENSING 2015年第1期7卷 153-168页

作者： Qian, Yuguo Zhou, Weiqi Yan, Jingli Li, Weifeng Han, Lijian Chinese Acad Sci Res Ctr Ecoenvironm Sci State Key Lab Urban & Reg Ecol Beijing 100085 Peoples R China

This study evaluates and compares the performance of four machine learning classifiers-support vector machine (SVM), normal Bayes (NB), classification and regression tree (CART) and K nearest neighbor (KNN)-to classify very high resolution images, using an object-based classification procedure. In particular, we investigated how tuning parameters affect the classification accuracy with different training sample sizes. We found that: (1) SVM and NB were superior to CART and KNN, and both could achieve high classification accuracy (> 90%);(2) the setting of tuning parameters greatly affected classification accuracy, particularly for the most commonly-used SVM classifier;the optimal values of tuning parameters might vary slightly with the size of training samples;(3) the size of training sample also greatly affected the classification accuracy, when the size of training sample was less than 125. Increasing the size of training samples generally led to the increase of classification accuracies for all four classifiers. In addition, NB and KNN were more sensitive to the sample sizes. This research provides insights into the selection of classifiers and the size of training samples. It also highlights the importance of the appropriate setting of tuning parameters for different machine learning classifiers and provides useful information for optimizing these parameters.

关键词： object-based classification machine learning classifiers very high resolution image urban area tuning parameters

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Wetland classification in Newfoundland and Labrador using multi-source SAR and optical data integration

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GISCIENCE & REMOTE SENSING 2017年第6期54卷 779-796页

作者： Amani, Meisam Salehi, Bahram Mahdavi, Sahel Granger, Jean Brisco, Brian Mem Univ Newfoundland C CORE Fac Engn & Appl Sci St John NF Canada Canada Ctr Mapping & Earth Observat Ottawa ON Canada

A vast portion of Newfoundland and Labrador (NL) is covered by wetland areas. Notably, it is the only province in Atlantic Canada that does not have a wetland inventory system. Wetlands are important areas of research because they play a pivotal role in ecological conservation and impact human activities in the province. Therefore, classifying wetland types and monitoring their changes are crucial tasks recommended for the province. In this study, wetlands in five pilot sites, distributed across NL, were classified using the integration of aerial imagery, Synthetic Aperture Radar, and optical satellite data. First, each study area was segmented using the object-based method, and then various spectral and polarimetric features were evaluated to select the best features for identifying wetland classes using the Random Forest algorithm. The accuracies of the classifications were assessed by the parameters obtained from confusion matrices, and the overall accuracies varied between 81% and 91%. Moreover, the average producer and user accuracies for wetland classes, considering all pilot sites, were 71% and 72%, respectively. Since the proposed methodology demonstrated high accuracies for wetland classification in different study areas with various ecological characteristics, the application of future classifications in other areas of interest is promising.

关键词： wetland remote sensing object-based classification Random Forest Newfoundland and Labrador

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object-based change monitoring in mining areas-taking Pingshuo as an example 2nd

Object-based change monitoring in mining areas-taking Pingsh...

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2nd International Symposium on Land Reclamation and Ecological Restoration (LRER)

作者： Zhang, M. L. Zhou, W. Yuan, T. Xie, Y. H. Li, Y. F. China Univ Geosci Sch Land Sci & Technol Beijing Peoples R China Indiana State Univ Dept Earth & Environm Syst Ctr Urban & Environm Change Terre Haute IN 47809 USA Beijing Normal Univ Sch Environm Beijing Peoples R China

ISBN: (纸本)9781315166582;9781138051034

In this paper, RapidEye Remote Sensing Image (Map 2012) and SPOT7 Remote Sensing Image (Map 2015) in Pingshuo Mining Area are selected as the data. Combined with object-based classification and change vector analysis method, we studied the feasibility of high resolution remote sensing image for mining land classification and the accuracy of monitoring. The results show that the classification of reclaimed mining land has higher precision, the overall accuracy and kappa coefficient of the classification of Map2012 were 0.89 and 0.87, and the change region map were 0.87 and 0.84. It's obvious that object-based classification and change vector analysis which has a great significance to improve the monitoring accuracy can be used to monitor mining land, especially reclaiming mining land.

关键词： land use/cover monitoring object-based classification change vector analysis open-pit coal mine land reclamation

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