检索结果-内蒙古大学图书馆

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

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

来源：评论

学校读者我要写书评

暂无评论

Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification

引用

REMOTE SENSING OF ENVIRONMENT 2010年第6期114卷 1141-1154页

作者： Ke, Yinghai Quackenbush, Lindi J. Im, Jungho SUNY Syracuse Coll Environm Sci & Forestry Dept Environm Resources & Forest Engn Syracuse NY 13210 USA

This study evaluated the synergistic use of high spatial resolution multispectral imagery (i.e., QuickBird, 2.4 m) and low-posting-density LIDAR data (3 m) for forest species classification using an object-based approach. The integration of QuickBird multispectral imagery and LIDAR data was considered during image segmentation and the subsequent object-based classification. Three segmentation schemes were examined: (1) segmentation based solely on the spectral image layers;(2) segmentation based solely on MAR-derived layers;and (3) segmentation based on both the spectral and LIDAR-derived layers. For each segmentation scheme, objects were generated at twelve different scales in order to determine optimal scale parameters. Six categories of classification metrics were generated for each object based on spectral data alone, LIDAR data alone and the combination of both data sources. Machine learning decision trees were used to build classification rule sets. Quantitative segmentation quality assessment and classification accuracy results showed the integration of spectral and LIDAR data, in both image segmentation and object-based classification, improved the forest classification compared to using either data source independently. Better segmentation quality led to higher classification accuracy. The highest classification accuracy (Kappa 91.6%) was acquired when using both spectral- and LIDAR-derived metrics based on objects segmented from both spectral and LIDAR layers at scale parameter 250, where best segmentation quality was achieved. Optimal scales were analyzed for each segmentation-classification scheme. Statistical analysis of classification accuracies at different scales revealed that there was a range of optimal scales that provided statistically similar accuracy. (C) 2010 Elsevier Inc. All rights reserved.

关键词： High spatial resolution multispectral imagery LIDAR object-based classification Decision tree Forest classification

来源：评论

学校读者我要写书评

暂无评论

Hybrid model to optimize object-based land cover classification by meta-heuristic algorithm:an example for supporting urban management in Ha Noi,Viet Nam

引用

International Journal of Digital Earth 2019年第10期12卷 1118-1132页

作者： Quang-Thanh Bui Manh Pham Van Nguyen Thi Thuy Hang Quoc-Huy Nguyen Nguyen Xuan Linh Pham Minh Hai Tran Anh Tuan Pham Van Cu Center for Applied Research in Remote sensing and GIS(CARGIS) VNU University of ScienceHa NoiViet Nam VNU University of Science Ha NoiViet Nam Vietnam Institute of Geodesy and Cartography Ha NoiViet Nam Ministry of Education and Training Ha NoiViet Nam

This study proposed a novel object-based hybrid classification model named GMNN that combines Grasshopper Optimization Algorithm(GOA)and the multiple-class Neural network(MNN)for urban pattern detection in Hanoi,*** bands of SPOT 7 image and derivable NDVI,NDWI were used to generate image segments with associated attributes by PCI Geomatics *** segments were classified into four urban surface types(namely water,impervious surface,vegetation and bare soil)by the proposed ***,three training and validation datasets of different sizes were used to verify the robustness of this *** all tests,the overall accuracies of the classification were approximately 87%,and the Area under Receiver Operating Characteristic curves for each land cover type was ***,the performance of this model was examined by comparing several statistical indicators with common benchmark *** results showed that GMNN out-performed established methods in all comparable *** results suggested that our hybrid model was successfully deployed in the study area and could be used as an alternative classification method for urban land cover *** a broader sense,classification methods will be enriched with the active and fast-growing contribution of metaheuristic algorithms.

关键词： Urban remote sensing object-based classification neural network grasshopper optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

An object-based Method for Urban Land Cover classification Using Airborne Lidar Data

引用

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2014年第10期7卷 4243-4254页

作者： Chen, Ziyue Gao, Bingbo Univ Cambridge Cambridge England Beijing Normal Univ Coll Global Change & Earth Syst Sci Beijing 100875 Peoples R China Beijing Acad Agr & Forestry Sci Beijing Res Ctr Informat Technol Agr Beijing 100097 Peoples R China

Airborne Lidar (Light detection and ranging) data have been widely used for classifying different land cover types. However, few researchers have conducted urban land cover classification using discrete airborne Lidar data as the sole data source. This research explores the possibility of applying airborne Lidar data to land cover classification in urban areas. The elevation difference and intensity difference between the first and last return, which may not work efficiently in pixel-based classification, were employed as two key attributes at the object level. Since tree objects have a much larger proportion of returns which show the elevation and intensity difference, the two indicators were used to classify the most indistinguishable land cover types, buildings and trees. In addition, height and intensity information were integrated to classify other land cover types. A case study was conducted in the city of Cambridge and eight urban land cover types were classified with an overall accuracy of 93.6%. Each land cover type was classified with an accuracy of between 80% and 100% and among these types, the accuracy of more than 90% for trees and buildings was satisfactory.

关键词： Airborne Lidar elevation difference intensity difference object-based classification urban

来源：评论

学校读者我要写书评

暂无评论

classification of Polarimetric SAR Data based on object-based Multiple Classifiers for Urban Land-Cover

引用

JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2016年第6期44卷 855-863页

作者： Habibi, Masoud Sahebi, Mahmod Reza Maghsoudi, Yasser Ghayourmanesh, Shaheen KN Toosi Univ Technol Dept Geomat & Geodesy Engn ValiAsr St Tehran Iran Univ New Brunswick Dept Geodesy & Geomat Engn Fredericton NB Canada

In this study, we used an object-oriented method for merging pixel-based classification and image segments to get an optimal classification result for an urban land-cover classification which is one of the important applications of Polarimetric SAR (PolSAR) remote-sensing. Because of the nature of PolSAR images, various features can be extracted and used in a classification. To achieve an improved classification accuracy, an optimal subset of features should be used. For this purpose, we used a class-based multiple classifier with a support vector machine (SVM) as a pixel-based classification with the class accuracy as the criterion in feature selection. We proposed the SVM margin as a new distance criterion for feature selection. In addition, to overcome the degradation of pixel-based classification results due to presence of speckle noise in PolSAR images, thematic features were used in image segmentation. In general, the proposed method consisted of three steps: feature selection, pixel-based classification, and polarimetric spatial classification. The pixel-based classification result was merged with a set of segments that were obtained from multi-resolution segmentation and the overall accuracy of the results was evaluated. The objectives of the study were to improve the accuracy of classification and to introduce margin as the new criterion for feature selection. Our results showed that the overall accuracy of the proposed method (90.07 %) was improved compared with the single SVM classifier (80.46 %) and pixel-based multiple classifier (83.61 %). Lastly, the SVM margin distance as well as class accuracy could be considered as appropriate criteria in feature selection.

关键词： PolSAR Multiple classifier object-based classification SVM

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification

引用

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2018年 139卷 154-170页

作者： Liu, Tao Abd-Elrahman, Amr Univ Florida Sch Forest Resources & Conservat Gainesville FL 32611 USA Univ Florida Gulf Coast Res Ctr Plant City FL 33563 USA

Deep convolutional neural network (DCNN) requires massive training datasets to trigger its image classification power, while collecting training samples for remote sensing application is usually an expensive process. When DCNN is simply implemented with traditional object-based image analysis (OBIA) for classification of Unmanned Aerial systems (UAS) orthoimage, its power may be undermined if the number training samples is relatively small. This research aims to develop a novel OBIA classification approach that can take advantage of DCNN by enriching the training dataset automatically using multi-view data. Specifically, this study introduces a Multi-View object-based classification using Deep convolutional neural network (MODe) method to process UAS images for land cover classification. MODe conducts the classification on multi-view UAS images instead of directly on the orthoimage, and gets the final results via a voting procedure. 10-fold cross validation results show the mean overall classification accuracy increasing substantially from 65.32%, when DCNN was applied on the orthoimage to 82.08% achieved when MODe was implemented. This study also compared the performances of the support vector machine (SVM) and random forest (RF) classifiers with DCNN under traditional OBIA and the proposed multi-view OBIA frameworks. The results indicate that the advantage of DCNN over traditional classifiers in terms of accuracy is more obvious when these classifiers were applied with the proposed multi-view OBIA framework than when these classifiers were applied within the traditional OBIA framework. (C) 2018 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

关键词： Deep learning Convolutional neural network object-based classification Random forest Support vector machine small UAS OBIA

来源：评论

学校读者我要写书评

暂无评论

object-based Land Cover classification Using Airborne Lidar and Different Spectral Images

引用

TERRESTRIAL ATMOSPHERIC AND OCEANIC SCIENCES 2016年第4期27卷 491-504页

作者： Teo, Tee-Ann Huang, Chun-Hsuan Natl Chiao Tung Univ Dept Civil Engn Hsinchu Taiwan

Both land cover spectral information and 3D surface information can be obtained efficiently via remote sensing technologies. Spectral images provide spectral features whereas lidar point clouds contain 3D spatial features. Therefore, the multi sensor data can be integrated to obtain useful information for different applications. This study integrates lidar with different spectral features for land cover classification. Because different spectral images have different characteristics, this study used hyperspectral images, 4- and 8-band WorldView-2 multispectral images, to distinguish different land covers. The major works include features selection, object-based classification, and evaluation. In features selection appropriate features were selected according to the land cover characteristics. object-based classification was implemented using image segmentation and supervised classification. Finally, different combinations were evaluated using reference data to provide comprehensive analyses. We use ITRES CASI-1500 airborne hyperspectral images, WorldView-2 multispectral images and Optech ALTM Pegasus in this study. The experiment compared the results with and without data fusion. The importance of different spectral features is also discussed. In summary, different land covers with similar spectral features can be identified using lidar spatial features. Spectral image integration with lidar data may improve land cover classification accuracy.

关键词： Lidar Hyperspectral image WorldView-2 object-based classification

来源：评论

学校读者我要写书评

暂无评论

A New Method for object-based Hyperspectral Image classification

引用

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

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：