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object-based classification approach for greenhouse mapping using Landsat-8 imagery

International Journal of Agricultural and Biological Engineering 2016年第1期9卷 79-88,I0005页

作者： Wu Chaofan Deng Jinsong Wang Ke Ma Ligang Amir Reza Shah Tahmassebi Institute of Applied Remote Sensing&Information Technology Zhejiang UniversityHangzhou 310058China

Suburban greenhouses with intensive agricultural productivity have increasingly influenced the daily diet and vegetable supply in Chinese *** their enormous input of fertilizers and pesticides,greenhouses have considerably changed the local soil quality and environmental risk *** ability to obtain timely and accurate information regarding the spatial distribution of greenhouses could make an important contribution to local agricultural management and soil *** paper attempts to present a practical framework for extracting suburban greenhouses,integrating remote sensing data from Landsat-8 and object-oriented *** classification was implemented,and various properties,including texture and neighborhood features in addition to spectral information,were investigated through the popular random forest technique for feature selection prior to SVM classification to improve the mapping *** results demonstrated that object-based classification incorporating non-spectral features yielded a significant improvement compared with the classification results obtained using only the spectral information in traditional per-pixel *** the producer’s and user’s accuracy were higher than 85%for greenhouse *** it remained a challenge to completely distinguish greenhouses from sparse plants,the final greenhouse map indicated that the proposed object-based classification scheme,providing multiple feature selections and multi-scale analysis,yielded worthwhile information when applied to a continuous series of the freely available Landsat-8 imagery data.

关键词： greenhouse mapping Landsat-8 object-based classification feature selection multi-scale

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object-based classification of hyperspectral images based on weighted genetic algorithm and deep learning model

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APPLIED GEOMATICS 2023年第1期15卷 227-238页

作者： Akbari, Davood Akbari, Vahid Univ Zabol Fac Engn Dept Geomat Engn Zabol Iran Univ Stirling Fac Nat Sci Dept Comp Sci & Math Stirling Scotland

Numerous uses of the hyperspectral remote sensing technology exist for identifying land cover and tracking its evolution. The classification of hyperspectral images must now take into account both spectral and spatial information due to recent advancements and the production of images with high spatial resolution. Convolutional neural networks (CNNs) have much employed in recent years to enhance the classification precision of hyperspectral images. The simultaneous use of spatial feature extraction methods in CNNs has not received significant attention in prior studies. In this study, a novel CNN architecture has been developed for classifying hyperspectral images. The weighted genetic (WG) algorithm is used in the proposed technique to minimize the hyperspectral image's dimensions. The WG algorithm keeps every band in the image and gives each one weight between zero and one based on how much information it contains. Following the expectation maximization (EM) method to the collected features, the segmented objects are then categorized using the CNN algorithm. Three benchmark hyperspectral images, Pavia, DC Mall, and Indiana Pine, were used to assess the proposed approach. The trials' findings demonstrate the proposed approach's superiority over the multilayer perceptron (MLP) algorithm in the Pavia, DC Mall, and Indiana Pine images by 14, 16, and 8% in the overall accuracy parameter, respectively.

关键词： Hyperspectral image object-based classification Weighted genetic (WG) Expectation maximization (EM) segmentation Deep learning

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object-based classification USING ULTRACAM-D IMAGES FOR TREE SPECIES DISCRIMINATION (CASE STUDY: HYRCANIAN FOREST-IRAN)

OBJECT-BASED CLASSIFICATION USING ULTRACAM-D IMAGES FOR TREE...

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Conference on Geographic object-based Image Analysis (GEOBIA)

作者： Rafieyan, O. Darvishsefat, A. A. Babaii, S. Mataji, A. Islamic Azad Univ Tabriz Branch Tehran Iran Univ Tehran Nat Resources Fac Dept Forestry Karaj Iran Islamic Azad Univ Sci & Res Branch Tehran Iran

This research has been conducted to evaluate the proposed object-based method with high spatial resolution airborne remote sensing data in tree species identification and mapping. It has been done in 2 areas of total 10 separate areas;include the disturbed natural broadleaved forest and the broadleaved and coniferous mixed forestation in the Northern forests of Iran. After pre-processing the imagery a trial and error method was employed to reach the ideal segmentation results. Subsequent to class definition, sample objects were selected as representative of defined classes and NN classifier was accomplished using integration of a broad spectrum of different object features. Accuracy assessment of the produced maps, comparing with field reference data shows the overall accuracies and Kappa statistics of 0.79, 0.61 (Area1) and 0.76, 0.69 (Area2) respectively. Relatively low accuracy in both areas demonstrated that the standalone optical remote sensing methods are insufficient for classification of such complex forest structures.

关键词： object-based classification Image segmentation Ground truth UltraCamD High spatial resolution

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object-based classification of URBAN AIRBORNE LIDAR POINT CLOUDS with MULTIPLE ECHOES USING SVM 22

OBJECT-BASED CLASSIFICATION of URBAN AIRBORNE LIDAR POINT CL...

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22nd Congress of the International Society for Photogrammetry and Remote Sensing: Imaging a Sustainable Future, ISPRS 2012

作者： Zhang, J.X. Lin, X.G. Lin, X.G. Key Laboratory of Mapping from Space of State Bureau of Surveying and Mapping Chinese Academy of Surveying and Mapping No. 28 Lianhuachixi Road Haidian District Beijing100830 China

Airborne LiDAR point clouds classification is meaningful for various applications. In this paper, an object-based analysis method is proposed to classify the point clouds in urban areas. In the process of classification, outliers in the point clouds are first removed. Second, surface growing algorithm is employed to segment the point clouds into different clusters. The above point cloud segmentation is helpful to derive useful features such as average height, size/area, proportion of multiple echoes, slope/orientation, elevation difference, rectangularity, ratio of length to width, and compactness. At last, SVM-based classification is performed on the segmented point clouds with radial basis function as kernel. Two datasets with high point densities are employed to test the proposed method, and three classes are predefined. The results suggest that our method will produce the overall classification accuracy larger than 97% and the Kappa coefficient larger than 0.95.

关键词： Airborne LiDAR Multiple echoes object-based classification Point cloud Segmentation Surface growing

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object-based classification for mangrove with VHR remotely sensed image

Object-based classification for mangrove with VHR remotely s...

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15th International Conference on Geoinformatics

作者： Liu, Zhigang Li, Jing Lim, Boonleong Seng, Chungyueh Inbaraj, Suppiah State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China Beijing Normal Univ Sch Geog & Remote Sensing Sci Beijing 100875 Peoples R China Beijing Normal Univ Coll Resource Sci & Technol Beijing Peoples R China Cilix Corp Sdn Bhd Kuala Lumpur 57000 Malaysia

ISBN: (纸本)9780819469120

In remotely sensed imagery with high spatial resolution, more detail spatial information of mangrove forest can be shown. It is important to find a method to effectively use the spatial information so as to improve the accuracy of mangrove forest classification. In the study, different classification schemes (including pixel-based classification and object-based classification), different classifiers, and different texture features have been conducted. The classification results of SPOT-5 image of Matang Mangrove Forest Reserve in Malaysia show that the performances of object-based classifications are better than that of pixel-based classifications. However, the classifier type is important for object-based classification. The accuracies of nearest neighborhood classifiers, which are widely used in object-based classifications, were obviously lower that that of maximum likelihood classifiers and support vector Machines. It is also shown that the involvement of second-order texture features can't effectively improve the classification accuracy of neither object-based classifications nor pixel-based classifications.

关键词： mangrove SPOT-5 object-based classification

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object-based classification of IKONOS data for endemic Torreya mapping

Object-based classification of IKONOS data for endemic Torre...

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3rd International Conference on Environmental Science and Information Application Technology (ESIAT)

作者： Wang, Xiaoming Wang, Ke Zhou, Benzhi Chinese Acad Forestry Res Inst Subtrop Forestry Fuyang 311400 Zhejiang Peoples R China Zhejiang Univ Inst Remote Sensing Informat Technol Applica Hangzhou 310029 Zhejiang Peoples R China

Torreya (T. grandis 'Merrillii') is a rare cash crop tree endemic to Kuaiji mountains of southern China. The present research demonstrated the use of spatial information in object-based classification for improving the identification of Torreya from IKONOS images. Pixel-based and object-based classifications on a combination of spectral and textural features were implemented and their accuracies were compared. The object-based classification proved more accurate than the pixel-based classification. The overall classification accuracy and Kappa coefficients were 75.7% and 0.676 for the pixel-based classification;81.7% and 0.746 for the object-based classification. The overall results demonstrate that object-based classification of very high resolution satellite images using additional features is a practical tool for vegetation mapping in the study area. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Conference ESIAT2011 Organization Committee.

关键词： Vegetation mapping object-based classification Texture IKONOS

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object-based classification USING REGION GROWING SEGMENTATION

OBJECT-BASED CLASSIFICATION USING REGION GROWING SEGMENTATIO...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Lee, Sang-Hoon Kyungwon Univ Dept Ind Engn Songnam South Korea

ISBN: (纸本)9781457710056

A region merging segmentation technique is suggested in this paper for the object-based classification of high-spatial resolution imagery. It employs a region growing scheme based on the region adjacency graph (RAG). The proposed algorithm uses directional neighbor-line average feature vectors to improve the quality of segmentation. The feature vector consists of 9 components which includes an observation and 8 directional averages. The merging coefficients of the segmentation process use a part of the feature components according to a given merging coefficient order. This study performed the extensive experiments using simulation data and a real high-spatial resolution data of IKONOS. The experimental results show that the new approach proposed in this study is quite effective to provide segments of high quality for the object-based analysis of high-spatial resolution images.

关键词： segmentation region growing high-spatial resolution neighbor-line average feature object-based classification

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Detection and object-based classification of offshore oil slicks using ENVISAT-ASAR images

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2011年第1-4期183卷 409-423页

作者： Akar, Sertac Suezen, Mehmet Lutfi Kaymakci, Nuretdin Middle E Tech Univ Geol Engn Dept TR-06531 Ankara Turkey

The aim of this study is to propose and test a multi-level methodology for detection of oil slicks in ENVISAT Advanced Synthetic Aperture Radar (ASAR) imagery, which can be used to support the identification of hydrocarbon seeps. We selected Andrusov Ridge in the Central Black Sea as the test study area where extensive hydrocarbon seepages were known to occur continuously. Hydrocarbon seepage from tectonic or stratigraphic origin at the sea floor causes oily gas plumes to rise up to the sea surface and form thin oil films called oil slicks. Microwave sensors like synthetic aperture radar (SAR) are very suitable for ocean remote sensing as they measure the backscattered radiation from the surface and show the roughness of the terrain. Oil slicks dampen the sea waves creating dark patches in the SAR image. The proposed and applied methodology includes three levels: visual interpretation, image filtering and object-based oil spill detection. Level I, after data preparation with visual interpretation, includes dark spots identification and subsets/scenes creation. After this process, the procedure continues with categorization of subsets/scenes into three cases based on contrast difference of dark spots to the surroundings. In level II, by image and morphological filtering, it includes preparation of subsets/scenes for segmentation. Level III includes segmentation and feature extraction which is followed by object-based classification. The object-based classification is applied with the fuzzy membership functions defined by extracted features of ASAR subsets/scenes, where the parameters of the detection algorithms are tuned specifically for each case group. As a result, oil slicks are discriminated from look-alikes with an overall classification accuracy of 83% for oil slicks and 77% for look-alikes obtained by averaging three different cases.

关键词： Synthetic aperture radar (SAR) Oil slick object-based classification Black Sea

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Estimation of biomass and carbon stock in Para rubber plantations using object-based classification from Thaichote satellite data in Eastern Thailand

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JOURNAL OF APPLIED REMOTE SENSING 2015年第1期9卷 096072-1-096072-14页

作者： Charoenjit, Kitsanai Zuddas, Pierpaolo Allemand, Pascal Pattanakiat, Sura Pachana, Katavut UPMC Univ Paris 04 Inst Sci Terre Paris F-75252 Paris France Univ Lyon 1 LGL TPE F-69622 Villeurbanne France Mahidol Univ Fac Environm & Resource Studies Geoinformat Resource & Environm Res & Training Ct Nakhon 73170 Pathom Thailand Burapha Univ Fac Sci Dept Chem Chon Buri 20131 Thailand

This paper deals with the efficiency of measurements of carbon stock by remote sensing techniques on Para rubber plantations in Thailand. These plantations could play an important role in carbon budget and thus are part of the Clean Development Mechanism of the Kyoto Protocol. Current methods of carbon stock estimations use middle resolution images and produce results with a large uncertainty. We use very high resolution images from the Thaichote satellite, associated with field measurements to estimate the carbon stock and its evolution in the Mae num Prasae watershed, Eastern Thailand. Using object-based classifications, the plantations have been mapped and their age has been estimated from a parametric model derived from both spectral and textural information and field data. The total biomass and carbon stocked are 2.23 and 0.99 Megaton with an uncertainty of 11%. One hundred and twenty one tons of carbon are sequestered annually in the Para rubber plantations of the studied area. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.

关键词： carbon stock estimation object-based classification Para rubber plantation Thaichote satellite Eastern Thailand

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An object-based classification Approach for Mapping Migrant Housing in the Mega-Urban Area of the Pearl River Delta (China)

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REMOTE SENSING 2011年第8期3卷 1710-1723页

作者： d'Oleire-Oltmanns, Sebastian Coenradie, Bodo Kleinschmit, Birgit Tech Univ Berlin D-10623 Berlin Germany Goethe Univ Frankfurt Dept Phys Geog Remote Sensing & GIS Res Grp D-60438 Frankfurt Germany

Urban areas develop on formal and informal levels. Informal development is often highly dynamic, leading to a lag of spatial information about urban structure types. In this work, an object-based remote sensing approach will be presented to map the migrant housing urban structure type in the Pearl River Delta, China. SPOT5 data were utilized for the classification (auxiliary data, particularly up-to-date cadastral data, were not available). A hierarchically structured classification process was used to create (spectral) independence from single satellite scenes and to arrive at a transferrable classification process. Using the presented classification approach, an overall classification accuracy of migrant housing of 68.0% is attained.

关键词： urban structure types object-based classification land-use change SPOT5 urban sprawl

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