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object-based classification of IKONOS Data for Endemic Torreya Mapping

Procedia Environmental Sciences 2011年 10卷 1887-1891页

作者： Xiaoming Wang Ke Wang Benzhi Zhou Research Institute of Subtropical Forestry the Chinese Acadamy of Forestry Fuyang 311400 Zhejiang China Institution of Remote Sensing & Information Technology Application Zhejiang University Hangzhou 310029 Zhejiang 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.

关键词： Vegetation mapping object-based classification Texture IKONOS

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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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2011 3rd International Conference on Environmental Science and Information Application Technology(ESIAT 2011)

作者： Xiaoming Wang Ke Wang Benzhi Zhou Research Institute of Subtropical Forestry the Chinese Acadamy of Forestry Institution of Remote Sensing & Information Technology Application Zhejiang University

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.

关键词： Vegetation mapping object-based classification Texture IKONOS

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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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object-based Plastic-Mulched Landcover Extraction Using Integrated Sentinel-1 and Sentinel-2 Data

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REMOTE SENSING 2018年第11期10卷 1820-1820页

作者： Lu, Lizhen Tao, Yuan Di, Liping Zhejiang Univ Sch Earth Sci Hangzhou 310027 Zhejiang Peoples R China George Mason Univ Ctr Spatial Informat Sci & Syst Fairfax VA 22030 USA

Plastic mulching on farmland has been increasing worldwide for decades due to its superior advantages for improving crop yields. Monitoring Plastic-Mulched Land-cover (PML) can provide essential information for making agricultural management decisions and reducing PML's eco-environmental impacts. However, mapping PML with remote sensing data is still challenging and problematic due to its complicated and mixed characteristics. In this study, a new object-based Image Analysis (OBIA) approach has been proposed to investigate the potential for combined use of Sentinel-1 (S1) SAR and Sentinel-2 (S2) Multi-spectral data to extract PML. based on the ESP2 tool (Estimation of Scale Parameter 2) and ED2 index (Euclidean Distance 2), the optimal Multi-Resolution Segmentation (MRS) result is chosen as the basis of following object-based classification. Spectral and backscattering features, index features and texture features from S1 and S2 are adopted in classifying PML and other land-cover types. Three machine-learning classifiers known as the-classification and Regression Tree (CART), the Random Forest (RF) and the Support Vector Machine (SVM) are carried out and compared in this study. The best classification result with an overall accuracy of 94.34% is achieved by using spectral, backscattering, index and textural information from integrated S1 and S2 data with the SVM classifier. Texture information is demonstrated to contribute positively to PML classifications with SVM and RF classifiers. PML mapping using SAR information alone has been greatly improved by the object-based approach to an overall accuracy of 87.72%. By adding SAR data into optical data, the accuracy of object-based PML classifications has also been improved by 1-3%.

关键词： plastic-mulched landcover (PML) Sentinel-1 Sentinel-2 object-based classification multi-resolution segmentation (MRS) SVM RF CART

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Intact rock bridge breakage and rock mass fragmentation upon failure: quantification using remote sensing techniques

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PHOTOGRAMMETRIC RECORD 2017年第160期32卷 513-536页

作者： Spreafico, Margherita C. Franci, Francesca Bitelli, Gabriele Borgatti, Lisa Ghirotti, Monica Univ Milano Bicocca Milan Italy Univ Bologna Bologna Italy Univ Ferrara Ferrara Italy

In this work, two processes related to the onset and the evolution of large-scale rockfalls have been analysed by means of remote sensing techniques. First, the number of rock bridges as a proportion of pre-existing fractures has been estimated in a newly formed landslide scarp by analysing the spectral information of the rock mass in terrestrial laser scanner (TLS) and unmanned aerial vehicle (UAV) photogrammetric point clouds. Second, the fragmentation of the rock mass after failure has been assessed by comparing the percentage of newly formed fractures in the scarp and in the deposit. For this purpose, an orthophoto of the landslide deposit was classified using an object-based method. The presented workflow gained new data on the intact rock breakage during and after failure, and consequently deepened knowledge about rock failure and fragmentation mechanisms. Resume Dans cette etude, deux processus lies au declenchement et a l'evolution de grands eboulements ont ete analyses au moyen de techniques de modelisation 3D. Dans un premier temps, le nombre de ponts rocheux est estime en proportion des fractures preexistantes dans un escarpement de glissement de terrain nouvellement forme en analysant l'information spectrale de la masse rocheuse dans des nuages de points generes par balayage laser terrestre et par drone photogrammetrique. La fragmentation de la masse rocheuse apres rupture est ensuite evaluee en comparant le pourcentage de fractures nouvellement formees dans l'escarpement et dans le depot. cette fin, une orthophotographie de l'eboulis est traitee par une classification orientee objet. La methode presentee a permis d'obtenir de nouvelles donnees sur la rupture de la roche intacte pendant et apres la fracturation, et par consequent d'approfondir les connaissances sur les mecanismes de fracture et de fragmentation des roches. Zusammenfassung Die vorgestellte Arbeit behandelt zwei Prozesse in Bezug auf die Entstehung und die Entwicklung gro ss flachiger Fels

关键词： fragmentation object-based classification rock bridges spectral information terrestrial laser scanner UAV

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Fine crop mapping by combining high spectral and high spatial resolution remote sensing data in complex heterogeneous areas

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2017年 139卷 1-9页

作者： Wu, Mingquan Huang, Wenjiang Niu, Zheng Wang, Yu Wang, Changyao Li, Wang Hao, Pengyu Yu, Bo Chinese Acad Sci Inst Remote Sensing & Digital Earth State Key Lab Remote Sensing Sci POB 9718Datun Rd Beijing 100101 Peoples R China Chinese Acad Sci Inst Remote Sensing & Digital Earth Lab Digital Earth Sci Beijing Peoples R China Beijing Twenty First Century Sci & Technol Dev Co Beijing 100083 Peoples R China

In complex heterogeneous areas, it is difficult to map crops with high accuracy using only high spatial resolution or only high spectral resolution remote sensing data. Because the spectral resolution of high spatial resolution data is too low, the spectral differentiations of different vegetation types are very small in high spatial resolution data. It is hard to distinguish between different vegetation types using high spatial resolution data. For high spectral resolution remote sensing data, it is hard to exclude linear objects like roads, bridges and drains from crops due to the low spatial resolution of these data. To address this problem, a novel object-based fine crop mapping method by combining high spatial and high spectral resolution remote sensing data for heterogeneous areas was proposed and validated in Suzhou city, Jiangsu province, China. First, pure crop polygons were derived from a 0.5 m aerial data. Due to the high spatial resolution, non-cultivated land could be easily isolated from arable land. Then, a Hyperion data was used to classify crops for each of the pure crop polygons. The results show that this method can map crops in complex heterogeneous areas with an overall accuracy higher than 95%, which is much higher than the accuracy of maps classified using only high spatial resolution data or only high spectral resolution data, which have an overall accuracy of 58.78% and 77.54%, respectively. (C) 2017 Elsevier B.V. All rights reserved.

关键词： GF-1 Hyperion data Remote sensing Crop mapping Heterogeneous area object-based classification

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Crop classification in a Heterogeneous Arable Landscape Using Uncalibrated UAV Data

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REMOTE SENSING 2018年第8期10卷 1282-1282页

作者： Bohler, Jonas E. Schaepman, Michael E. Kneubuhler, Mathias Univ Zurich Remote Sensing Labs Dept Geog Winterthurerstr 190 CH-8057 Zurich Switzerland

Land cover maps are indispensable for decision making, monitoring, and management in agricultural areas, but they are often only available after harvesting. To obtain a timely crop map of a small-scale arable landscape in the Swiss Plateau, we acquired uncalibrated, very high-resolution data, with a spatial resolution of 0.05 m and four spectral bands, using a consumer-grade camera on an unmanned aerial vehicle (UAV) in June 2015. We resampled the data to different spatial and spectral resolutions, and evaluated the method using textural features (first order statistics and mathematical morphology), a random forest classifier for best performance, as well as number and size of the structuring elements. Our main findings suggest the overall best performing data consisting of a spatial resolution of 0.5 m, three spectral bands (RGBred, green, and blue), and five different sizes of the structuring elements. The overall accuracy (OA) for the full set of crop classes based on a pixel-based classification is 66.7%. In case of a merged set of crops, the OA increases by similar to 7% (74.0%). For an object-based classification based on individual field parcels, the OA increases by similar to 20% (OA of 86.3% for the full set of crop classes, and 94.6% for the merged set, respectively). We conclude the use of UAV to be most relevant at 0.5 m spatial resolution in heterogeneous arable landscapes when used for crop classification.

关键词： consumer-grade camera land cover very high resolution (VHR) random forest (RF) classifier object-based classification pixel-based classification small-scaled agricultural fields texture spatial feature

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Species classification using Unmanned Aerial Vehicle (UAV)-acquired high spatial resolution imagery in a heterogeneous grassland

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2017年 128卷 73-85页

作者： Lu, Bing He, Yuhong Univ Toronto Mississauga Dept Geog 3359 Mississauga Rd Mississauga ON L5L 1C6 Canada

Investigating spatio-temporal variations of species composition in grassland is an essential step in evaluating grassland health conditions, understanding the evolutionary processes of the local ecosystem, and developing grassland management strategies. Space-borne remote sensing images (e.g., MODIS, Landsat, and Quickbird) with spatial resolutions varying from less than 1 m to 500 m have been widely applied for vegetation species classification at spatial scales from community to regional levels. However, the spatial resolutions of these images are not fine enough to investigate grassland species composition, since grass species are generally small in size and highly mixed, and vegetation cover is greatly heterogeneous. Unmanned Aerial Vehicle (UAV) as an emerging remote sensing platform offers a unique ability to acquire imagery at very high spatial resolution (centimetres). Compared to satellites or airplanes, UAVs can be deployed quickly and repeatedly, and are less limited by weather conditions, facilitating advantageous temporal studies. In this study, we utilize an octocopter, on which we mounted a modified digital camera (with near-infrared (NIR), green, and blue bands), to investigate species composition in a tall grassland in Ontario, Canada. Seven flight missions were conducted during the growing season (April to December) in 2015 to detect seasonal variations, and four of them were selected in this study to investigate the spatio-temporal variations of species composition. To quantitatively compare images acquired at different times, we establish a processing flow of UAV-acquired imagery, focusing on imagery quality evaluation and radiometric correction. The corrected imagery is then applied to an object-based species classification. Maps of species distribution are subsequently used for a spatio-temporal change analysis. Results indicate that UAV-acquired imagery is an incomparable data source for studying fine-scale grassland species composition, owing

关键词： UAV Grassland Species composition object-based classification

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classification of Tundra Vegetation in the Krkonose Mts. National Park Using APEX, AISA Dual and Sentinel-2A Data

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EUROPEAN JOURNAL OF REMOTE SENSING 2017年第1期50卷 29-46页

作者： Kupkova, Lucie Cervena, Lucie Sucha, Renata Jakesova, Lucie Zagajewski, Bogdan Brezina, Stanislav Albrechtova, Jana Charles Univ Prague Dept Appl Geoinformat & Cartog Fac Sci Albertov 6 Prague 12843 2 Czech Republic Univ Warsaw Fac Geog & Reg Studies Dept Geoinformat Cartog & Remote Sensing Krakowskie Przedmiescie 30 Warsaw Poland Krkonose Mt Natl Pk Adm Dobrovskeho 3 Vrchlabi Czech Republic Charles Univ Prague Dept Expt Plant Biol Fac Sci Vinicna 5 Prague 2 Czech Republic

The aim of this study was to evaluate and compare suitability of aerial hyperspectral data (AISA Dual and APEX sensors) and Sentinel-2A data for classification of tundra vegetation cover in the Krkonose Mts. National Park. We compared classification results (accuracy, maps) of pixel-based (Maximum Likelihood, Suport Vector Machine and Neural Net) and object-based approaches. The best classification results (overall accuracy 84.3%, Kappa coefficient = 0.81) were achieved for AISA Dual data using per-pixel SVM classifier for 40 PCA bands. The best classification results of APEX though were only 1.7 percentage points lower. To get comparable results for Sentinel-2A classification legend had to be simplified. With the simplified legend the accuracy using MLC classifier reached 77.7%.

关键词： Tundra vegetation The Krkonose Mountains Per-pixel classification object-based classification Hyperspectral data Sentiel-2A

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Testing random forest classification for identifying lava flows and mapping age groups on a single Landsat 8 image

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JOURNAL OF VOLCANOLOGY AND GEOTHERMAL RESEARCH 2017年第Oct.1期345卷 109-124页

作者： Li, Long Solana, Carmen Canters, Frank Kervyn, Matthieu China Univ Min & Technol Sch Environm Sci & Spatial Informat Daxue Rd 1 Xuzhou 221116 Peoples R China Vrije Univ Brussel Earth Syst Sci Dept Geog Pl Laan 2 B-1050 Brussels Belgium Univ Portsmouth Sch Earth & Environm Sci Burnaby BldgBurnaby Rd Portsmouth PO1 3QL Hants England Vnje Univ Brussel Dept Geog Cartog & GIS Res Grp Pl Laan 2 B-1050 Brussels Belgium

Mapping lava flows using satellite images is an important application of remote sensing in volcanology. Several volcanoes have been mapped through remote sensing using a wide range of data, from optical to thermal infrared and radar images, using techniques such as manual mapping, supervised/unsupervised classification, and elevation subtraction. So far, spectral-based mapping applications mainly focus on the use of traditional pixel-based classifiers, without much investigation into the added value of object-based approaches and into advantages of using machine learning algorithms. In this study, Nyamuragira, characterized by a series of >20 overlapping lava flows erupted over the last century, was used as a case study. The random forest classifier was tested to map lava flows based on pixels and objects. Image classification was conducted for the 20 individual flows and for 8 groups of flows of similar age using a Landsat 8 image and a DEM of the volcano, both at 30-meter spatial resolution. Results show that object-based classification produces maps with continuous and homogeneous lava surfaces, in agreement with the physical characteristics of lava flows, while lava flows mapped through the pixel-based classification are heterogeneous and fragmented including much "salt and pepper noise". In terms of accuracy, both pixel-based and object-based classification performs well but the former results in higher accuracies than the latter except for mapping lava flow age groups without using topographic features. It is concluded that despite spectral similarity, lava flows of contrasting age can be well discriminated and mapped by means of image classification. The classification approach demonstrated in this study only requires easily accessible image data and can be applied to other volcanoes as well if there is sufficient information to calibrate the mapping. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Lava flows Nyamuragira Pixel-based classification object-based classification Random forest

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