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object-based multiscale method for SAR image change detection

JOURNAL OF APPLIED REMOTE SENSING 2018年第2期12卷

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

This paper proposed an object-based multiscale method for synthetic aperture radar (SAR) image change detection based on the statistical model. Rather than the pixel-based analysis conducted in the traditional way, the object-based image analysis was employed to take a collection of pixels as the unit of analysis, which reduced small spurious changes and was less strict relative to registration. In addition, a multiscale concept was adopted to exhibit the inherent multiscale characteristics of the target. To achieve object-based, multiscale change detection results, the multidate segmentation was performed on two temporal SAR images and extended to a set of suitable scales. Then, the Edgeworth series expansion was employed to estimate the probability density function, and the Kullback-Leibler divergence was adopted to calculate the distance between pairs of pixel collections. Next, the divergence index maps were divided into changed and unchanged classes to obtain change detection results for each scale. Finally, the subresults were combined to obtain a more accurate detection result. The experimental results obtained using real data demonstrated the effectiveness of the proposed method. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： change detection synthetic aperture radar object-based image analysis multiscale analysis statistical model

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Monitoring flood extent in the lower Amazon River floodplain using ALOS/PALSAR ScanSAR images

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REMOTE SENSING OF ENVIRONMENT 2013年 130卷 51-61页

作者： Arnesen, Allan S. Silva, Thiago S. F. Hess, Laura L. Novo, Evlyn M. L. M. Rudorff, Conrado M. Chapman, Bruce D. McDonald, Kyle C. Inst Nacl Pesquisas Espaciais Div Sensoriamento Remoto BR-12201970 Sao Jose Dos Campos Brazil Univ Calif Santa Barbara Earth Res Inst Santa Barbara CA 93106 USA Univ Calif Santa Barbara Bren Sch Environm Sci & Management Santa Barbara CA 93106 USA CALTECH Jet Prop Lab Pasadena CA 91109 USA CUNY City Coll Dept Earth & Atmospher Sci CUNY Environm Crossrd Initiat New York NY 10031 USA CUNY City Coll CUNY CREST Inst New York NY 10031 USA

The Amazon River floodplain is subject to large seasonal variations in water level and flood extent, due to the large size and low relief of the basin, and the large amount of precipitation in the region. Synthetic Aperture Radar (SAR) data can be used to map flooded area in these wetlands, given its ability to provide continuous information without being heavily affected by cloud cover. As part of JAXA's Kyoto & Carbon Initiative, extensive wide-swath, multi-temporal SAR coverage of the Amazon basin has been obtained using the ScanSAR mode of ALOS PALSAR This study presents a method for monitoring flood extent variation using ALOS ScanSAR images, tested at the Curuai Lake floodplain, in the lower Amazon River, Brazil. Twelve ScanSAR scenes were acquired between 2006 and 2010, including seven during the 2007 hydrological year. Water level records, field photographs, optical images (Landsat-5/TM and MODIS/Ferra and Aqua) and topographic data were used as auxiliary information. A data mining algorithm allowed the implementation of a hierarchical, object-based classification algorithm, able to map land cover types and flooding status in the study area for all available dates. land cover based on the entire time series (classification levels 1 and 2) had overall accuracies of 90% and 83%, respectively. Level 3 classifications (one map per image date) were validated only for the lowest and highest water stages, with overall accuracies of 76% and 78%, respectively. Total flood extent (Level 4) was mapped with 84% and 94% accuracies, for the low and high water stages, respectively. Regression models were fitted between mapped flooded area and water levels at the Curuai gauge to predict flood extent. A polynomial model had R-2=0.95 (p<0.05) and an overall root mean square error (RMSE) of 241 km(2), while a logistic model had R-2=0.98 (p<0.05) and RMSE = 127 km(2). (C) 2012 Elsevier Inc. All rights reserved.

关键词： object-based image analysis Multi-temporal analysis Incidence angle Wetlands Synthetic aperture radar Kyoto & Carbon Initiative

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Application of Satellite Remote Sensing Techniques to Quantify Terminus and Ice Melange Behavior at Helheim Glacier, East Greenland

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MARINE TECHNOLOGY SOCIETY JOURNAL 2014年第5期48卷 81-91页

作者： Foga, Steve Stearns, Leigh A. van der Veen, C. J. Univ Kansas Dept Geog Lawrence KS 66045 USA Univ Kansas Dept Geol Lawrence KS 66045 USA Univ Kansas Ctr Remote Sensing Ice Sheets CReSIS Lawrence KS 66045 USA

Iceberg calving is an efficient mechanism for ice mass loss, and rapidly calving glaciers are often considered to be inherently unstable. However, the Physical controls on calving are not well understood. Recent studies hypothesize that the presence of a rigid ice malange (composed of icebergs, bergy bits, and sea ice), can reduce iceberg calving by providing "backstress" to the terminus. To test this hypothesis we use remote sensing techniques to construct a time series model of calving rate and size and composition of the adjacent ice melange. We describe a semi-automated routine for expediting the digitization process and illustrate the method's for Helheim Glacier, East Greenland, using 2008 data. Ice velocities of the glacier terminus and ice melange are derived with feature-tracking software applied to radar imagery, which is successfully tracked year-round. object-based image analysis (OBIA) is used to inventory icebergs and sea ice within the ice melange. We find that the model succesefully identifies the calving rate and ice melange response trends associated with seasonal increases in terminus retreat and advance and shows seasonal trends of ice melange potentially providing seasonal backstress on the glacier terminus.

关键词： glaciology iceberg calving object-based image analysis feature tracking ice melange

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Evaluation of advanced time series similarity measures for object-based cropland mapping

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2023年第12期44卷 3777-3800页

作者： He, Weiqiang Ma, Lei Yan, Ziyun Lu, Heng Nanjing Univ Sch Geog & Ocean Sci Jiangsu Prov Key Lab Geog Informat Sci & Technol Key Lab Land Satellite Remote Sensing ApplicatMin Nanjing Peoples R China Sichuan Univ State Key Lab Hydraul & Mt River Engn Chengdu Peoples R China Nanjing Univ Sch Geog & Ocean Sci Jiangsu Prov Key Lab Geog Informat Sci & Technol Key Lab Land Satellite Remote Sensing ApplicatMin Nanjing 210023 Peoples R China Sichuan Univ State Key Lab Hydraul & Mt River Engn Chengdu 610065 Peoples R China

With more and more high-resolution remote-sensing data becoming available, satellite image time series (SITS) analysis is often used to map crops at the pixel level. Although the object-based image analysis paradigm has proven superior to pixel-based image analysis for overcoming salt-and-pepper effects, object-based time series crop classification is still rare. Except for time-weighted dynamic time warping (TWDTW), other time series similarity measuring algorithms popular in the signal processing domain, such as shape-based distance (SBD) and global alignment kernel (GAK), remain poorly explored at the object level. Accordingly, this study aimed to explore better object-based time series classification frameworks by investigating similarity measures, including SBD, GAK and TWDTW, and thus experiments were designed to analyse the response of these methods to various parameters, including crop types, time series density, processing efficiency and so on. The results show that GAK is superior to SBD and TWDTW in general. Specifically, GAK performs better than TWDTW with the limited available images, e.g. in cloudy and rainy climate regions. Also, GAK outperforms SBD in identifying crops with a complex phenological pattern, such as alfalfa. Furthermore, TWDTW has the lowest processing efficiency. This study addresses the scarcity of similarity measurement methods in the object-based time series paradigm, and it is expected that this study could provide some guidance for the selection of cropland mapping methods.

关键词： Crop classification satellite image time series object-based image analysis time series similarity measures

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An evaluation of digital elevation models (DEMs) for delineating land components

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GEODERMA 2014年 213卷 312-319页

作者： Mashimbye, Zama Eric de Clercq, Willem Petrus Van Niekerk, Adriaan Univ Stellenbosch Dept Soil Sci ZA-7602 Matieland South Africa Univ Stellenbosch Dept Geog & Environm Studies ZA-7602 Matieland South Africa

Land component boundaries often coincide with transitions in environmental land properties such as soil, climate and biology. image segmentation is an effective method for delineating terrain morphological units from digital elevation models (DEMs). This paper compares the land components derived from five DEMs. The second version of the 30-m advanced spaceborne thermal emission and reflection radiometer global DEM (ASTER GDEM2), the 90-m shuttle radar topography mission DEM (SRTM DEM), two versions of the 5-m Stellenbosch University DEMs (SUDEM L1 and L2) and a 5-m DEM (GEOEYE DEM) derived from GeoEye stereo-images were considered. The SRTM DEM and the ASTER GDEM2 were upsampled to 5-m resolution for comparison purposes. Land components were delineated using the slope gradient and aspect derivatives of each DEM. The resulting land components were visually inspected and quantitatively analyzed using the slope gradient standard deviation (SGSD) measure and the mean slope gradient local variance (MSGLV) ratio. The results show that the GEOEYE DEM and SUDEM 12 yielded land components with relatively low SGSD values and that their boundaries often coincide with morphological discontinuities. The GEOEYE DEM produced land components with the highest MSGLV ratio, followed by SUDEM 12, ASTER GDEM2, SRTM DEM and SUDEM L1. Although the land components derived from SRTM DEM and SUDEM L1 were relatively homogeneous internally, their boundaries did not always trace morphological discontinuities. The ASTER GDEM2 failed to incorporate many of the morphological discontinuities in the study area. It is concluded that, although the SRTM DEM is more suitable than the ASTER GDEM2 for generating land components, higher-resolution DEMs such as the GEOEYE DEM and SUDEML2 are required for delineating meaningful land components. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Digital soil mapping object-based image analysis Land components Multi-scale digital terrain analysis Terrain units

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Rapid mapping and impact estimation of illegal charcoal production in southern Somalia based on WorldView-1 imagery

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ENERGY FOR SUSTAINABLE DEVELOPMENT 2015年 25卷 40-49页

作者： Bolognesi, M. Vrieling, A. Rembold, F. Gadain, H. FAO Somalia Water & Land Informat Management SWALIM P Food & Agr Org United Nations Nairobi Kenya Univ Twente Fac Geoinformat Sci & Earth Observat ITC NL-7500 AE Enschede Netherlands European Commiss Joint Res Ctr MARS Unit I-21027 Ispra VA Italy

Following more than 20 years of civil unrest, environmental information for southern Somalia is scarce. Wood charcoal production and export is a major activity supporting war regimes in this area such as the extreme Islamist group Al-Shabaab. However, little quantitative information exists on the extent of this charcoal production. In this study, we developed a semi-automatic detection method to identify charcoal production sites from very high resolution (0.5 m) satellite imagery. We then applied it to a 4700 km(2) area along the Juba River in southern Somalia using 2011 and 2013 WorldView-1 imagery. based on the sites detected exclusively for 2013 we estimated an average production of 24,000 tonnes of charcoal and 2.7% tree loss for the two-year interval, using literature- and local-knowledge-based assumptions on likely ranges of kiln and tree parameters. Our large-area assessment helps to better understand the dimension and impact of charcoal production in southern Somalia and reveals a rapid depletion of tree cover. The analysis provides a first step towards the development of a charcoal production monitoring system that could be extended to other parts of the country. (C) 2015 International Energy Initiative. Published by Elsevier Inc. All rights reserved.

关键词： Woodfuel Forest degradation Al-Shabaab Remote sensing object-based image analysis

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Rockfall mapping and susceptibility evaluation based on UAV high-resolution imagery and support vector machine method

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OPEN GEOSCIENCES 2024年第1期16卷

作者： Zhao, Longhui Qin, Yigen Yang, Genlan Zhang, Wuzhou Lu, Kunpeng Guizhou Water Resources & Hydropower Survey Design Guiyang 550004 Peoples R China Sun Yat Sen Univ Sch Earth Sci & Engn Guangdong Prov Key Lab Geodynam & Geohazards Guangzhou Peoples R China Guizhou Univ Coll Resource & Environm Engn Guiyang 550025 Peoples R China Guizhou Zhiheng Engn Survey & Design Consulting Co Guiyang 550004 Peoples R China

This study aims to develop a comprehensive method for automated rockfall mapping and susceptibility assessment using unmanned aerial vehicle (UAV) tilt photography and the support vector machine (SVM) algorithm. By employing Jinzhong Town in the mountainous forest landscapes of Southwest China as a case study, we leverage photogrammetry principles and computer vision algorithms to generate high-precision, high-resolution digital surface models (DSMs), and digital orthophoto maps through a UAV remote sensing system. The rockfall inventory is accurately and automatically mapped using the object-based classification method and SVM algorithm. The automated rockfall identification method achieves a 93% accuracy with a Kappa coefficient of 0.7967. Statistical analyses of spatial distribution characteristics reveal a significant correlation between rockfall volume and area with a correlation coefficient (R-2) of 0.92316 under logarithmic coordinates. In addition, a power function relationship describes the link between rockfall volume and slope height (R-2 = 0.87), while the relationship with sliding distance is characterized by a weaker linear correlation (R-2 = 0.65). Rockfall runout distance also shows a significant linear correlation with slope height (R-2 = 0.79) but exhibits a less-pronounced association with rockfall volume (R-2 = 0.58). The SVM model employed to assess rockfall susceptibility indicates high accuracy (area under the curve = 0.896), affirming its efficacy in rockfall susceptibility assessment. Our findings underscore the utility of UAV remote sensing for rockfall information extraction and susceptibility evaluation, particularly in challenging mountainous forest environments characterized by intricate topography and geological complexities.

关键词： UAV object-based image analysis SVM algorithm rockfall susceptibility evaluation mountainous forest

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Random forest wetland classification using ALOS-2 L-band, RADARSAT-2 C-band, and TerraSAR-X imagery

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2017年 130卷 13-31页

作者： Mandianpari, Masoud Salehi, Bahram Mohammadimanesh, Fariba Motagh, Mandi Mem Univ Newfoundland C CORE St John NF A1B 3X5 Canada Mem Univ Newfoundland Dept Elect Engn St John NF A1B 3X5 Canada GFZ German Res Ctr Geosci Dept Geodesy Sect Remote Sensing D-14473 Potsdam Germany Leibniz Univ Hannover Inst Photogrammetry & GeoInformat D-30167 Hannover Germany

Wetlands are important ecosystems around the world, although they are degraded due both to anthropogenic and natural process. Newfoundland is among the richest Canadian province in terms of different wetland classes. Herbaceous wetlands cover extensive areas of the Avalon Peninsula, which are the habitat of a number of animal and plant species. In this study, a novel hierarchical object-based Random Forest (RF) classification approach is proposed for discriminating between different wetland classes in a sub-region located in the north eastern portion of the Avalon Peninsula. Particularly, multi polarization and multi -frequency SAR data, including X-band TerraSAR-X single polarized (HH), L-band ALOS-2 dual polarized (HH/HV), and C-band RADARSAT-2 fully polarized images, were applied in different classification levels. First, a SAR backscatter analysis of different land cover types was performed by training data and used in Level-I classification to separate water from non-water classes. This was followed by Level-II classification, wherein the water class was further divided into shallow-and deep water classes, and the non-water class was partitioned into herbaceous and non-herbaceous classes. In Level-III classification, the herbaceous class was further divided into bog, fen, and marsh classes, while the non-herbaceous class was subsequently partitioned into urban, upland, and swamp classes. In Level-II and -III classifications, different polarimetric decomposition approaches, including Cloude-Pottier, Freeman-Durden, Yamaguchi decompositions, and Kennaugh matrix elements were extracted to aid the RF classifier. The overall accuracy and kappa coefficient were determined in each classification level for evaluating the classification results. The importance of input features was also determined using the variable importance obtained by RF. It was found that the Kennaugh matrix elements, Yamaguchi, and Freeman-Durden decompositions were the most important parameters f

关键词： Wetland classification Polarimetric Synthetic Aperture Radar Random Forest object-based image analysis Kennaugh matrix

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An object based framework for building change analysis using 2D and 3D information of high resolution satellite images

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ADVANCES IN SPACE RESEARCH 2020年第6期66卷 1386-1404页

作者： Mohammadi, Hamid Samadzadegan, Farhad Univ Tehran Coll Engn Sch Surveying & Geospatial Engn North Kargar Ave Tehran *** Iran

With the rapid change of urban areas in developing countries, construction areas are constantly appearing in different parts of cities. Those changed areas require timely monitoring to provide up-to-date information for of urban information systems. As a result, it is a challenge to develop an effective change analysis of different objects, especially buildings in cities. This paper presents an object-based framework for analyzing building changes from high-resolution satellite stereo images (HRSSI). The disparity information extracted from stereo images and spectral information including visible vegetation index (VVI) help extract the buildings in a hierarchical approach. Evaluations show the accuracy of higher than 98% and F1-Score of higher than 87% for the building extraction step. Then, each building object is classified into three main categories including "Remained Building", "Removed Building" or "Added Building". Also, the "Remained Building" objects are categorized into four change states including "Only 2D Change", "Only 3D Change", "2D with 3D Change" and "No Change". This is done by utilizing the object-based similarity analysis of the spectral information as well as the similarity analysis of the disparity information using CNN and their integration. Evaluations demonstrate the accuracy of higher than 97% and F1-Score of higher than 90% for this step. (C) 2020 Published by Elsevier Ltd on behalf of COSPAR.

关键词： Building extraction Building change analysis High resolution satellite stereo images 2D and 3D information object-based image analysis Convolutional neural network

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object-based Flood Mapping and Affected Rice Field Estimation with Landsat 8 OLI and MODIS Data

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REMOTE SENSING 2015年第5期7卷 5077-5097页

作者： Dao, Phuong D. Liou, Yuei-An Natl Cent Univ Ctr Space & Remote Sensing Res Taoyuan 320 Taiwan Taiwan Grp Earth Observat Zhubei City 30274 Hsinchu County Taiwan

Cambodia is one of the most flood-prone countries in Southeast Asia. It is geographically situated in the downstream region of the Mekong River with a lowland floodplain in the middle, surrounded by plateaus and high mountains. It usually experiences devastating floods induced by an overwhelming concentration of rainfall water over the Tonle Sap Lake's and Mekong River's banks during monsoon seasons. Flood damage assessment in the rice ecosystem plays an important role in this region as local residents rely heavily on agricultural production. This study introduced an object-based approach to flood mapping and affected rice field estimation in central Cambodia. In this approach, image segmentation processing was conducted with optimal scale parameter estimation based on the variation of objects' local variances. The inundated area was identified by using Landsat 8 images with an overall accuracy of higher than 95% compared to those derived from finer spatial resolution images. Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index products were utilized to identify the paddy rice field based on seasonal inter-variation between vegetation and water index during the transplanting stage. The rice classification result was well correlated with the statistical data at a commune level (R-2 = 0.675). The flood mapping and affected rice estimation results are useful to provide local governments with valuable information for flooding mitigation and post-flooding compensation and restoration.

关键词： object-based image analysis segmentation scale parameter estimation flood mapping paddy rice Landsat Moderate Resolution Imaging Spectroradiometer （MODIS） Cambodia

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