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第一届中国大地测量和地球物理学学术大会

作者： Xianwei Wang Mei Liu School of Geography and Planning Sun Yat-sen University GuangzhouChina

The Tien Shan Mountains are known as the water tower of the arid Central *** mountain glaciers are rapidly retreating under the context of global and regional *** works have been done to analyze the retreating rate of... 详细信息

关键词： Glacier Landsat image object-oriented classification Tianshan

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Toward an Optimal object-oriented Image classification Using SVM and MLLH Approaches 1

Toward an Optimal Object-Oriented Image Classification Using...

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1st International Conference on New Technologies of Information and Communication NTIC

作者： Malik, Ryad Kheddam, Radja Belhadj-Aissa, Aichouche USTHB Image Proc & Radiat Lab Algiers Algeria

ISBN: (纸本)9781467366854

Support Vector Machines (SVM) and Maximum Likelihood (MLLH) are the most popular remote sensing image classification approaches. In the past, SVM and MLLH have been tested and evaluated only as pixel-based image classifiers. Moving from pixel-based analysis to object-based analysis, a fuzzy classification concept is used through eCognition software [1]. In this paper, SVM and MLLH are separately adopted and compared for multi-class object-oriented classification process where the input features vector contains primitive image objects produced by a multi-resolution segmentation algorithm. In this study, the determination of suitable object segmentation scale leading to an improved object-oriented classification result is also discussed and performed. Comparative analysis clearly revealed that higher overall classification accuracy (97%) was observed in the object-based classification using the optimal segmentation scale.

关键词： Satellite image object-oriented classification SVM MLLH Multi-scale segmentation

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object-oriented crop classification based on UAV remote sensing imagery

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Global Geology 2022年第1期25卷 60-68页

作者： ZHANG Lan ZHANG Yanhong College of Geo-Exploration Science and Technology Jilin UniversityChangchun 130026China

UAV remote sensing images have the advantages of high spatial resolution,fast speed,strong real-time performance,and convenient operation,etc.,and have become a recently developed,vital means of acquiring surface *** is an important research task for precision agriculture to make full use of the spectrum,texture,color and other characteristic information of crops,especially the spatial arrangement and structure information of features,to explore effective methods for the classification of multiple varieties of *** order to explore the applicability of the object-oriented method to achieve accurate classification of UAV high-resolution images,the paper used the object-oriented classification method in ENVI to classify the UAV high-resolution remote sensing image obtained from the orderly structured 28 species of crops in the test field,which mainly includes image segmentation and object *** results showed that the plots obtained after classification were continuous and complete,basically in line with the actual situation,and the overall accuracy of crop classification was 91.73%,with Kappa coefficient of *** with the crop planting area based on remote sensing interpretation and field survey,the area error of 17 species of crops in this study was controlled within 15%,which provides a basis for object-oriented crop classification of UAV remote sensing images.

关键词： object-oriented classification UAV remote sensing imagery crop classification

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Toward an Optimal object-oriented Image classification Using SVM and MLLH Approaches

Toward an Optimal Object-Oriented Image Classification Using...

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International Conference on New Technologies of Information and Communication

作者： Ryad Malik Radja Kheddam Aichouche Belhadj-Aissa Image Processing and Radiation Laboratory USTHB

ISBN: (纸本)9781467366861

关键词： Satellite image object-oriented classification SVM MLLH Multi-scale segmentation

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A Hybrid object-oriented Conditional Random Field classification Framework for High Spatial Resolution Remote Sensing Imagery

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2014年第11期52卷 7023-7037页

作者： Zhong, Yanfei Zhao, Ji Zhang, Liangpei Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China

High spatial resolution (HSR) remote sensing imagery provides abundant geometric and detailed information, which is important for classification. In order to make full use of the spatial contextual information, object-oriented classification and pairwise conditional random fields (CRFs) are widely used. However, the segmentation scale choice is a challenging problem in object-oriented classification, and the classification result of pairwise CRF always has an oversmooth appearance. In this paper, a hybrid object-oriented CRF classification framework for HSR imagery, namely, CRF + OO, is proposed to address these problems by integrating object-oriented classification and CRF classification. In CRF + OO, a probabilistic pixel classification is first performed, and then, the classification results of two CRF models with different potential functions are used to obtain the segmentation map by a connected-component labeling algorithm. As a result, an object-level classification fusion scheme can be used, which integrates the object-oriented classifications using a majority voting strategy at the object level to obtain the final classification result. The experimental results using two multispectral HSR images (QuickBird and IKONOS) and a hyperspectral HSR image (HYDICE) demonstrate that the proposed classification framework has a competitive quantitative and qualitative performance for HSR image classification when compared with other state-of-the-art classification algorithms.

关键词： classification fusion conditional random fields (CRFs) high spatial resolution (HSR) object-oriented classification remote sensing

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object-oriented Mangrove Species classification Using Hyperspectral Data and 3-D Siamese Residual Network

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2020年第12期17卷 2150-2154页

作者： He, Zhi Shi, Qian Liu, Kai Cao, Jingjing Zhan, Wen Cao, Beifen Sun Yat Sen Univ Sch Geog & Planning Guangdong Prov Key Lab Urbanizat & Geosimulat Guangzhou 510275 Peoples R China Sun Yat Sen Univ Ctr Integrated Geog Informat Anal Guangzhou 510275 Peoples R China Southern Marine Sci & Engn Guangdong Lab Zhuhai 519082 Peoples R China

Mangrove species classification is of particular importance for coastal conservation and restoration. However, it is challenging to distinguish species-level differences with limited training data. In this letter, we propose an object-oriented classification method for mangrove forests by using the hyperspectral image (HSI) and the 3-D Siamese residual network. First, superpixel segmentation is utilized to obtain objects with various shapes and scales. Second, 3-D patches of each object are extracted from the original HSI, and those patches containing training samples are adopted to pairwise train the network. The 3-D spatial pyramid pooling (3-D-SPP) is added in the network to extract features in multiple scales. Finally, the abstract features of test samples are learned by the trained network, and the labels are determined by the nearest neighbor classifier within the metric space. Experiments on real mangrove hyperspectral data demonstrate the effectiveness of the proposed method in species classification of mangroves.

关键词： Training Hyperspectral imaging Feature extraction Forestry Geography Hyperspectral image (HSI) mangrove object-oriented classification residual network superpixel

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object-oriented Random Forest classification for Enteromorpha Prolifera Detection with SAR Images 6

Object-Oriented Random Forest Classification for Enteromorph...

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6th International Conference on Virtual Reality and Visualization (ICVRV)

作者： Xie, Cui Dong, Junyu Sun, Fangfang Bing, Lei Ocean Univ China Dept Comp Sci & Technol Qingdao Peoples R China Yantai Maritime Safety Adm Emergency Response Tech Ctr Yantai Peoples R China

ISBN: (纸本)9781509051885

In this paper, a novel framework, called object oriented random forest classification (OORFC), is proposed for Entheromorpha prolifera (E. prolifera) detection based on RADARSAT-2 Synthetic Aperture Radar (SAR) imagery obtained from 2011 to 2013 in the coastal areas in the Yellow Sea. Firstly, each SAR image is processed by multi-scale segmentation to generate patches with homogeneous attributes. Then a random forest classifier is trained based on the features of these patches. Finally, for a new coming SAR images, E. prolifera can be detected by the generated random forest classifier and the operational monitoring of E. prolifera blooms is achieved. Experiments on *** detection from multi temporal SAR images are conducted for evaluation and results demonstrate our OORFC method's higher accuracy compared with the popular supervised classification methods (minimum distance and maximum likelihood). The capability of OORFC for E. prolifera bloom detection even during cloudy summer from SAR images can potentially facilitate the hazard management on the coastal environment.

关键词： enteromorpha prolifera detection synthetic aperture radar (SAR) object-oriented classification random forest

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Land Cover classification based on object-oriented with Airborne Lidar and High Spectral Resolution Remote Sensing Image

Land Cover Classification based on Object-oriented with Airb...

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International Symposium on Hyperspectral Remote Sensing Applications / International Symposium on Environmental Monitoring and Safety Testing Technology

作者： Li, Fangfang Liu, Zhengjun Xu, Qiangqiang Ren, Haicheng Zhou, Xingyu Yuan, Yonghua Chinese Acad Surveying & Mapping Beijing 100830 Peoples R China Liaoning Project Technol Univ Sch Geomat Fuxin 12300 Peoples R China Lanzhou Jiaotong Univ Gansu Prov Engn Lab Natl Geog State Monitoring Lanzhou 730070 Peoples R China

ISBN: (数字)9781510607712

ISBN: (纸本)9781510607705;9781510607712

In order to improve land cover classification accuracy of the coastal tidal wetland area in Dafeng, this paper take advantage of hyperspectral remote sensing image with high spatial resolution airborne Lidar data. The introduction of feature extraction, band selection and nDSM models to reduce the dimension of the original image. After segmentation process that combining FNEA segmentation with spectral differences segmentation method, the paper finalize the study area through the establishment of the rule set classification of land cover classification. The results show that the proposed classification for land cover classification accuracy has improved significantly, including housing, shadow, water, vegetation classification of high precision. That is to say that the method can meet the needs of land cover classification of the coastal tidal wetland area in Dafeng. This innovation is the introduction of principal component analysis, and the use of characteristic index, shape and characteristics of various types of data extraction nDSM feature to improve the accuracy and speed of land cover classification.

关键词： Multi-source remote sensing data object-oriented classification wetlands FNEA segmentation

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classification OF HIGH RESOLUTION OPTICAL AND SAR FUSION IMAGE USING FUZZY KNOWLEDGE AND object-oriented PARADIGM

CLASSIFICATION OF HIGH RESOLUTION OPTICAL AND SAR FUSION IMA...

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Conference on Geographic object-Based Image Analysis (GEOBIA)

作者： Xia, Junshi Du, Peijun Cao, Wen China Univ Min & Technol State Bur Surveying & Mapping China Key Lab Land Environm & Disaster Monitoring Xuzhou 221116 Jiangsu Peoples R China

The fusion image of IKONOS optical data and COSMO-SkyMed SAR image is used as the classifier inputs for land cover classification under an object-oriented paradigm. Firstly, Frost filter is selected to reduce speckle noises of SAR image. Three popular methods, including IHS, PCA and wavelet transform, are applied to fuse high resolution optical and SAR images. Comparing with other fusion methods, wavelet transform based fusion scheme is used for classification. Fuzzy knowledge is established based on the spectral, spatial and texture features derived from manual interpretation and expert knowledge. Finally, this proposed approach is compared with other traditional classification methods.

关键词： Fuzzy Knowledge object-oriented classification High Resolution Remote Sensing Image Fusion

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Vector Distance Algorithm for Optimal Segmentation Scale Selection of object-oriented Remote Sensing Image classification

Vector Distance Algorithm for Optimal Segmentation Scale Sel...

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17th International Conference on Geoinformatics

作者： Yu, Huan Zhang, Shuqing Kong, Bo Chinese Acad Sci Northeast Inst Geog & Agroecol Ctr Remote Sensing & Geosci Changchun Peoples R China Chinese Acad Sci Inst Mountain Hazards & Environm Ctr GIS & Remote Sensing Applicat Chengdu Peoples R China

ISBN: (纸本)9781424445622

Aiming at the optimal segmentation scale selection for object-oriented remote sensing image classification, thesis brought forward a new method named vector distance index. Research verified the validity and applicability of this method by carrying out experiment at wetland area with China Brazil Earth Resource Satellite image. Both two experiment showed that it could realize the optimal segmentation scale selection for object-oriented remote sensing image classification. Based on the basic theory of vector distance method, aiming at "submergence" and "fragmentation" phenomenon, research further brought forward a scale index, which could reflect the segmentation scale status for given object type, and provided a quantitative tool for assessing conflict degree between the two situations.

关键词： object-oriented classification wetland segmentation scale vector distance index

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