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IEEE 3rd International Conference on Signal and image Processing (ICSIP)

作者： Wan, Ling Zhang, Tao You, Hongjian Key Lab Technol Geospatial Informat Proc & Applic Beijing 100190 Peoples R China Chinese Acad Sci Inst Elect Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100039 Peoples R China

ISBN: (纸本)9781538663967

This paper introduces an object-based method based on a new statistical distance for SAR image change detection. Firstly, multi-temporal segmentation is carried out to segment two temporal SAR images simultaneously. It considers the homogeneity in two temporal images, and could generate homogeneous objects in spectral, spatial and temporal. In addition, through setting different segmentation parameters, the multi-temporal images can be segmented in a set of scales. This process exploits the advantages of OBIA that could effectively reduce spurious changes, and considers the scale of change detection task. Secondly, a multiplicative noise model called Nakagami-Rayleigh distribution is employed to describe SAR data, and then applied to Bayesian formulation. Thus, a new statistical distance that is insensitive to speckles is derived to measure the distances between pairs of parcels. Then, cluster ensemble algorithm is utilized to improve accuracy of individual result in each scale to obtain the final change detection map. Finally, multi-temporal Radarsat-2 images are employed to verify the effectiveness of the proposed method compared with other four methods.

关键词： synthetic aperture radar (SAR) change detection object-based image analysis multi-scale analysis

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MAPPING OF HIGHLY HETEROGENEOUS URBAN STRUCTURE TYPE FOR FLOOD VULNERABILITY ASSESSMENT

MAPPING OF HIGHLY HETEROGENEOUS URBAN STRUCTURE TYPE FOR FLO...

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International Conference on Geomatic and Geospatial Technology (GGT) - Geospatial and Disaster Risk Managemen

作者： Tam, T. H. Rahman, M. Z. A. Harun, S. Kaoje, I. U. Univ Teknol Malaysia TropicalMap Res Grp Johor Baharu Malaysia Univ Teknol Malaysia Dept Hydraul & Hydrol Johor Baharu Malaysia

Vulnerability plays an important role in risk assessment. For flood vulnerability assessment, the map and characteristics of elements-at-risk at different scales are strongly required depending on the risk and vulnerability assessment requirements. This study proposes a methodology to classify urban structure type by combining object-based image classification and different high resolution remote sensing data. In this study, a high resolution satellite image and LiDAR have been acquired over Kota Bharu, Kelantan which consists of highly heterogeneous urban structure type (UST) classes. The first stage is data pre-processing that includes orthorectification and pansharpening of Geoeye satellite image, image resampling for normalised Digital Surface Model (nDSM) and followed by image segmentation for creating meaningful objects. The second stage comprises of derivation of image features, generation of training and testing datasets, and classification of UST. The classification was based on three types of machine learning classifiers, i.e. Random Forest (RF), Support Vector Machine (SVM) and Classification and Regression Tree (CART). The results obtained from the classification processes were compared using individual omission and commission error, overcall accuracy and Kappa coefficient. The results show that Random Forest classifier with all image features achieved the highest overall accuracy (93.5%) and Kappa coefficient (0.94). This is followed by CART classifier with overall accuracy of 93.7% and Kappa coefficient of 0.92. Finally, SVM classifier produced the lowest overall accuracy and Kappa coefficient with 88.6% and 0.86, respectively. The UST classification result can be further used to assist detailed building characterisation for large scale flood vulnerability assessment.

关键词： Urban Structure Type object-based image analysis Random forest Support vector machine CART Flood vulnerability

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Comparing the Different Seasonal CBERS 04 images to Map the Quasi-circular Vegetation Patches in the Yellow River Delta, China 1

Comparing the Different Seasonal CBERS 04 Images to Map the ...

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1st Annual International Conference on Sensor Networks and Signal Processing (SNSP)

作者： Liu, Qingsheng Chinese Acad Sci Inst Geog Sci & Nat Resources Res State Key Lab Resources & Environm Informat Syst Beijing Peoples R China

ISBN: (纸本)9781538674130

This study compared five different seasonal CBERS-04 images for identifying the quasi-circular vegetation patches (QVPs) in the Yellow River Delta, China. Example-based feature extraction method in ENVI software 5.1 version, one of object-based image analysis, was adopted to segment the images based on edge algorithm, and K near neighbors method was used to identify the QVPs based on the training samples from visual interpretation with segmentation boundary. The research results from the present study showed that the seasonal impacts of multi-temporal CBERS-04 images for mapping the QVPs was important. The experimental results indicated that compared with other seasonal four images the higher precision rate, recall rate and F measure can be achieved by using the late spring season (May) image. Thus, it is significant to select proper seasonal images for monitoring the QVPs.

关键词： component CBERS-04 imagery object-based image analysis quasi-circular vegetation patch different season Yellow River Delta

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INTEGRATION OF SAR AND GEOBIA FOR THE analysis OF TIME-SERIES DATA 38

INTEGRATION OF SAR AND GEOBIA FOR THE ANALYSIS OF TIME-SERIE...

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38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Amitrano, D. Cecinati, F. Di Martino, G. Iodice, A. Mathieu, P. -P. Riccio, D. Ruello, G. Univ Napoli Federico II Naples Italy Univ Bath Bath Avon England ESA ESRIN Frascati Italy

ISBN: (纸本)9781538671504

In this work, we present a new architecture for the analysis multitemporal SAR data combining classic synthetic aperture radar processing and geographical object-based image analysis. The architecture exploits the characteristics of the recently introduced RGB products of the Level-1 alpha and Level-1 beta families, employing self-organizing map clustering and object-based image analysis aiming at the definition of opportune layers measuring scattering and geometric properties of candidate objects to classify. The obtained results have been compared with those given by literature and turned out to provide high degree of accuracy and negligible false alarms. The discussion is supported by an example concerning small reservoir mapping in semi-arid environment.

关键词： multitemporal synthetic aperture radar classification self-organizing map clustering object-based image analysis

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A Very High Resolution Satellite imagery Classification Algorithm 38

A Very High Resolution Satellite Imagery Classification Algo...

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IEEE 38th International Conference on Electronics and Nanotechnology (ELNANO)

作者： Shedlovska, Y. I. Hnatushenko, V. V. Oles Honchar Dnipro Natl Univ Dept Comp Sci & Informat Technol Dnipro Ukraine

ISBN: (纸本)9781538663837

This work is devoted to high-resolution WorldView-2 and WorldView-3 satellite imagery processing. We have developed a satellite imagery automatic classification algorithm based on the object-based approach. Three different segmentation methods are investigated in order to determine which is the most appropriate for our task. The experimental results show a good accuracy of the proposed algorithm.

关键词： segmentation object-based image analysis classification image features Kappa index

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analysis of recent changes in natural habitat types in the Apuseni Mountains (Romania), using multi-temporal Landsat satellite imagery (1986-2015)

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APPLIED GEOGRAPHY 2018年 97卷 161-175页

作者： Chetan, Marinela Adriana Dornik, Andrei Urdea, Petru West Univ Timisoara Dept Geog Blvd V Parvan 4 Timisoara 300223 Timis Romania

This study analysed the dynamics of natural habitat types in the last 30 years, in the three largest Natura 2000 sites in the Apuseni Mountains area in Romania. The Apuseni Mountains area records one of the highest percentages of protected areas among Romanian mountains, exceeding 50%, characterised by high biodiversity with faunal and floral endemics and rare species across distinctive karstic landscapes. Habitat type maps were derived from Landsat satellite images acquired in 1986, 1993, 2000, 2007 and 2015 using an object-based classification. based on habitat type maps, 18 landscape metrics at the class level and 16 metrics at the landscape level have been derived to quantify the structure, composition and spatial pattern of the landscape, correlating them with habitat type area changes. Results indicate that in the last 30 years, the deciduous and coniferous forests continuously improved in the Apuseni-Vladeasa Mountains, but surprisingly deteriorated after the site was declared a Special Protection Area (SPA) in 2007. The mixed forest habitat type has recorded a tendency of deterioration since 1986, however, this trend stopped in 2007, probably due to the decrease of human interventions. In this site, the pastures and meadows habitat type deteriorated until 2000 due to agriculture development (before 1993) and natural growth of vegetation, improving considerably after that with a surprising atenuation after 2007. The deciduous forests habitats within the Defileul MureNOT AVAILABLEului Inferior - Dealurile Lipovei site deteriorated until 2007, but it appears that human interventions have diminished after the site was declared a SPA, while within the Drocea-Zarand site the deciduous forests have not suffered deterioration. In these two sites, pastures recorded a constant deterioration in the last three decades, mainly due to land abandonment. Results are potentially useful for authorities to improve management and to develop appropriate strategies for habitat co

关键词： Natural habitat types Change detection Landscape metrics object-based image analysis Apuseni mountains

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Improving tree crown identification

Improving tree crown identification

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作者： Stijn Wijdeven Wageningen University and Research

学位级别：硕士

There is need for a standardized method for automated tree crown identification. In this study, two methods are analysed to identify their best practices and to develop a new, hybrid method. Method one is based on a Digital Surface Model (DSM), which determines the presence of trees based on their height compared to their neighbouring pixels. Method two is based on object-based image analysis (OBIA), which determines the presence of trees based on pixel characteristics of high resolution aerial images. based on the best practices of both methods the hybrid method is created with the aim to improve tree crown identification accuracy. The results of the DSM-, OBIA- and hybrid methods are compared to a validation dataset by their correct number of crowns counted and crown area size. This is done for single solitary trees, for trees standing in rows and for trees as part of a group. For identifying individual tree crowns, the results show that single trees are more easily identified than trees in rows and groups. Furthermore, the performances of both the DSM-method and OBIA-method increase when using input data of a higher resolution. based on the results it is not possible to conclude that the hybrid method is an overall improvement compared to the DSM- and OBIA-methods. Rather, it depends on what somebody wants to achieve with the crown identification method to determine what method is most suitable. The hybrid method has proven to be generally more useful for crown area estimation, but is relatively less accurate for crown count estimation.

关键词： Tree crown identification crown delineation Digital Surface Models DSM Airborne Laser Scanning LiDAR OBIA object-based image analysis Remote Sensing pointclouds Boomregister AHN

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object based BUILDING EXTRACTION and BUILDING PERIOD ESTIMATION from UNMANNED AERIAL VEHICLE DATA

OBJECT BASED BUILDING EXTRACTION and BUILDING PERIOD ESTIMAT...

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2018 ISPRS TC III Mid-Term Symposium on Developments, Technologies and Applications in Remote Sensing

作者： Comert, Resul Kaplan, Onur Department of Geomatics Engineering Faculty of Engineering and Natural Sciences Gumushane University Gumushane Turkey Earth and Space Sciences Institute Anadolu University Eskisehir26555 Turkey

The aim of this study is to examine whether it is possible to estimate the building periods with respect to the building heights in the urban scale seismic performance assessment studies by using the building height retrieved from the unmanned aerial vehicle (UAV) data. For this purpose, a small area, which includes eight residential reinforced concrete buildings, was selected in Eskisehir (Turkey) city center. In this paper, the possibilities of obtaining the building heights that are used in the estimation of building periods from UAV based data, have been investigated. The investigations were carried out in 3 stages;(i) Building boundary extraction with object based image analysis (OBIA), (ii) height calculation for buildings of interest from nDSM and accuracy assessment with the terrestrial survey. (iii) Estimation of building period using height information. The average difference between the periods estimated according to the heights obtained from field measurements and from the UAV data is 2.86 % and the maximum difference is 13.2 %. Results of this study have shown that the building heights retrieved from the UAV data can be used in the building period estimation in the urban scale vulnerability assessments. © Authors 2018.

关键词： Building Height Building Period object-based image analysis UAV

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Rapid Invasion of Spartina alterniflora in the Coastal Zone of Mainland China: New Observations from Landsat OLI images

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REMOTE SENSING 2018年第12期10卷 1933-1933页

作者： Liu, Mingyue Mao, Dehua Wang, Zongming Li, Lin Man, Weidong Jia, Mingming Ren, Chunying Zhang, Yuanzhi Chinese Acad Sci Northeast Inst Geog & Agroecol Key Lab Wetland Ecol & Environm Changchun 130102 Jilin Peoples R China North China Univ Sci & Technol Coll Min Engn Tangshan 063210 Peoples R China Indiana Univ Purdue Univ Dept Earth Sci Indianapolis IN 46202 USA Chinese Univ Hong Kong Ctr Housing Innovat Shatin Hong Kong Peoples R China Chinese Acad Sci Key Lab Lunar Sci & Deep Explorat Natl Astron Observ Beijing 100101 Peoples R China

Plant invasion imposes significant threats to biodiversity and ecosystem function. Thus, monitoring the spatial pattern of invasive plants is vital for effective ecosystem management. Spartina alterniflora (S. alterniflora) has been one of the most prevalent invasive plants along the China coast, and its spread has had severe ecological consequences. Here, we provide new observation from Landsat operational land imager (OLI) images. Specifically, 43 Landsat-8 OLI images from 2014 to 2016, a combination of object-based image analysis (OBIA) and support vector machine (SVM) methods, and field surveys covering the whole coast were used to construct an up-to-date dataset for 2015 and investigate the spatial variability of S. alterniflora in the coastal zone of mainland China. The classification results achieved good estimation, with a kappa coefficient of 0.86 and 96% overall accuracy. Our results revealed that there was approximately 545.80 km(2) of S. alterniflora distributed in the coastal zone of mainland China in 2015, from Hebei to Guangxi provinces. Nearly 92% of the total area of S. alterniflora was distributed within four provinces: Jiangsu, Shanghai, Zhejiang, and Fujian. Seven national nature reserves invaded by S. alterniflora encompassed approximately one-third (174.35 km(2)) of the total area of S. alterniflora over mainland China. The Yancheng National Nature Reserve exhibited the largest area of S. alterniflora (115.62 km(2)) among the reserves. Given the rapid and extensive expansion of S. alterniflora in the 40 years since its introduction and its various ecological effects, geospatially varied responding decisions are needed to promote sustainable coastal ecosystems.

关键词： invasive plants Spartina alterniflora CAS S alterniflora object-based image analysis Landsat OLI

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Monitoring Land-Use/Land-Cover Changes at a Provincial Large Scale Using an object-Oriented Technique and Medium-Resolution Remote-Sensing images

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REMOTE SENSING 2018年第12期10卷 2012-2012页

作者： Luo, Kaisheng Li, Bingjuan Moiwo, Juana P. Nanjing Univ Informat Sci & Technol Sch Remote Sensing & Geomat Engn Nanjing 210044 Jiangsu Peoples R China Chinese Acad Sci Inst Soil Sci Nanjing 210008 Peoples R China Univ Chinese Acad Sci Nanjing 210008 Peoples R China Njala Univ Sch Technol Dept Agr Engn Freetown Sierra Leone

An object-based image analysis (OBIA) technique is replacing traditional pixel-based methods and setting a new standard for monitoring land-use/land-cover changes (LUCC). To date, however, studies have focused mainly on small-scale exploratory experiments and high-resolution remote-sensing images. Therefore, this study used OBIA techniques and medium-resolution Chinese HJ-CCD images to monitor LUCC at the provincial scale. The results showed that while woodland was mainly distributed in the west, south, and east mountain areas of Hunan Province, the west had the largest area and most continuous distribution. Wetland was distributed mainly in the northern plain area, and cultivated land was distributed mainly in the central and northern plains and mountain valleys. The largest impervious surface was the Changzhutan urban agglomerate in the northeast plain area. The spatial distribution of land cover in Hunan Province was closely related to topography, government policy, and economic development. For the period 2000-2010, the areas of cultivated land transformed into woodland, grassland, and wetland were 183.87 km(2), 5.57 km(2), and 70.02 km(2), respectively, indicating that the government-promoted ecologically engineered construction was yielding some results. The rapid economic growth and urbanization, high resource development intensity, and other natural factors offset the gains made in ecologically engineered construction and in increasing forest and wetland areas, respectively, by 229.82 km(2) and 132.12 km(2) from 2000 to 2010 in Hunan Province. The results also showed large spatial differences in change amplitude (LUCCA), change speed (LUCCS), and transformation processes in Hunan Province. The Changzhutan urban agglomerate and the surrounding prefectures had the largest LUCCA and LUCCS, where the dominant land cover accounted for the conversion of some 189.76 km(2) of cultivated land, 129.30 km(2) of woodland, and 6.12 km(2) of wetland into impervious surfac

关键词： HJ-CCD images object-based image analysis change monitoring provincial scale

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