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Mapping dynamic cover types in a large seasonally flooded wetland using extended principal component analysis and object-based classification

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REMOTE SENSING OF ENVIRONMENT 2015年 158卷 193-206页

作者： Dronova, Iryna Gong, Peng Wang, Lin Zhong, Liheng Univ Calif Berkeley Dept Landscape Architecture & Environm Planning Coll Environm Design Berkeley CA 94720 USA Univ Calif Berkeley Dept Environm Sci Policy & Management Div Ecosyst Sci Coll Nat Resources Berkeley CA 94720 USA Chinese Acad Sci Inst Remote Sensing Applicat State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China Beijing Normal Univ Beijing 100101 Peoples R China Tsinghua Univ Ctr Earth Syst Sci Beijing 100084 Peoples R China Poyang Lake Ecol Res Stn Environm & Hlth Duchang 332600 Peoples R China Joint Ctr Global Change Studies Beijing 100875 Peoples R China Chinese Acad Fishery Sci Beijing 100141 Peoples R China

Periodically inundated wetlands with high short-term surface variation require special approaches to assess their composition and long-term change. To circumvent high uncertainty in single-date analyses of such areas, we propose to characterize them as dynamic cover types (DCTs), or sequences of wetland states and transitions informed by physically and ecologically plausible surface processes. This study delineated DCTs for one 2007-2008 flood cycle at Poyang Lake, the largest freshwater wetland in China, using spatial and temporal orientation modes of extended principal components analysis (EPCA) and supervised object-based classification of multi-spectral and radar image series. Classification accuracy was compared among three sets of attributes selected by machine-learning optimization from object-level mean and standard deviations of: 1) image time series alone;2) the most informative EPCA outputs alone and 3) image time series and EPCA results together. Classification uncertainty was additionally assessed as low values of object's maximum class membership (<0.5). The highest accuracy was achieved with a larger set of 33 attributes selected from combined time series and EPCA results (overall accuracy 95.0%, kappa 0.94);however, accuracies with smaller sets of variables from input image series or EPCA results alone were comparably high (93.1% and 94.7%, respectively). All three selected attribute sets included standard deviations of image and/or EPCA values, suggesting the utility of object texture in dynamic class discrimination. The highest classification uncertainty was observed primarily along the mapped class boundaries, in some cases indicating minor change trajectories for which prior reference data were not available. Results indicate that DCTs provide a reasonable classification framework for complex and variable Poyang Lake wetlands that can be facilitated by EPCA transformation of complementary remote sensing time series. Future work should test this a

关键词： Dynamic cover types Wetland remote sensing object-based image analysis PR China Extended PCA Phenology Change trajectory analysis

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Quantifying Spatial Heterogeneity in Urban Landscapes: Integrating Visual Interpretation and object-based Classification

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REMOTE SENSING 2014年第4期6卷 3369-3386页

作者： Zhou, Weiqi Cadenasso, Mary. L. Schwarz, Kirsten Pickett, Steward T. A. Chinese Acad Sci State Key Lab Urban & Reg Ecol Ecoenvironm Sci Res Ctr Beijing 100085 Peoples R China Univ Calif Davis Dept Plant Sci Davis CA 95616 USA No Kentucky Univ Highland Hts KY 41099 USA Cary Inst Ecosyst Studies Millbrook NY 12545 USA

Describing and quantifying the spatial heterogeneity of land cover in urban systems is crucial for developing an ecological understanding of cities. This paper presents a new approach to quantifying the fine-scale heterogeneity in urban landscapes that capitalizes on the strengths of two commonly used approachesvisual interpretation and object-based image analysis. This new approach integrates the ability of humans to detect pattern with an object-based image analysis that accurately and efficiently quantifies the components that give rise to that pattern. Patches that contain a mix of built and natural land cover features were first delineated through visual interpretation. These patches served as pre-defined boundaries for finer-scale segmentation and classification of within-patch land cover features which were classified using object-based image analysis. Patches were then classified based on the within-patch proportion cover of features. We applied this approach to the Gwynns Falls watershed in Baltimore, Maryland, USA. The object-based classification approach proved to be effective for classifying within-patch land cover features. The overall accuracy of the classification maps of 1999 and 2004 were 92.3% and 93.7%, respectively. This exercise demonstrates that by integrating visual interpretation with object-based classification, the fine-scale spatial heterogeneity in urban landscapes and land cover change can be described and quantified in a more efficient and ecologically meaningful way than either purely automated or visual methods alone. This new approach provides a tool that allows us to quantify the structure of the urban landscape including both built and non-built components that will better accommodate ecological research linking system structure to ecological processes.

关键词： object-based image analysis visual interpretation spatial heterogeneity land cover classification urban landscape Baltimore

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Greenery change and its impact on human-elephant conflict in Sri Lanka: a model-based assessment using Sentinel-2 imagery

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2023年第16期44卷 5121-5146页

作者： Gunawansa, Thakshila D. Perera, Kithsiri Apan, Armando Hettiarachchi, Nandita K. Bandara, Dananjana Y. Univ Southern Queensland Fac Hlth Engn & Sci Sch Surveying & Build Environm Toowoomba Qld Australia Uva Wellassa Univ Fac Technol Studies Dept Engn Technol Badulla Sri Lanka Univ Ruhuna Fac Engn Dept Mech & Mfg Engn Galle Sri Lanka Minist Water Supply & Estate Infrastruct Dev Water Supply & Sanitat Improvement Project Battaramulla Sri Lanka Univ Southern Queensland Fac Hlth Engn & Sci Sch Surveying & Built Environm West St Toowoomba Qld 4350 Australia

Human-elephant conflict (HEC) is a significant conservation issue for Asian elephants (Elephas maximus) and an environmental and socioeconomic crisis in elephant range countries, including Sri Lanka. Approximately 14,897 HEC incidents were recorded in Sri Lanka between 2015 and 2021. In this study, we present a Sri Lanka-wide analysis to explore the impact of greenery change on HEC. Our sources were official government data, and land use and land cover maps developed using Sentinel-2 satellite imagery. We applied the support vector machine (SVM), random forest (RF), and object-based image analysis classifications to classify land cover into six categories. This classification scheme also considered the differences observed in Sri Lanka's woody vegetation, consisting of forest, open forest, paddy fields, homestead gardens, and other crops. analysis of the accuracies of the three types of classifiers confirmed that the supervised classification with two machine learning algorithms, RF and SVM, delivered a higher level of precision in land cover classification. RF was the best option, with a 97.34% overall accuracy and a 0.94 kappa coefficient, while SVM recorded a 94.68% overall accuracy and a 0.89 kappa coefficient. According to the findings, most HEC incidences were recorded in open forests (54%), while 62% were recorded within 2 km of the forest edge. Results indicated that HEC coincides with the human-occupied changed landscape adjacent to forest reservations and patches. The findings could be valuable for HEC management by identifying areas where elephants are most likely to conflict with humans, and the government may declare these as protected areas. Also, we propose an early warning system as an effective approach that helps detect and monitor elephant herds' movement. Therefore, implementing long-term land use planning is crucial for protecting the forest and natural habitats, restoring elephant habitats, and mitigating HEC by minimizing human encroachment an

关键词： Human elephant conflict remote sensing random forest support vector machine object-based image analysis greenery changes

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Remote Sensing image Retrieval by Scene Semantic Matching

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2013年第5期51卷 2874-2886页

作者： Wang, Min Song, Tengyi Nanjing Normal Univ Key Lab Virtual Geog Environm Minist Educ Nanjing 210046 Jiangsu Peoples R China

This paper proposes a remote sensing (RS) image retrieval scheme by using image scene semantic (SS) matching. The low-level image visual features (VFs) are first mapped into multilevel spatial semantics via VF extraction, object-based classification of support vector machines, spatial relationship inference, and SS modeling. Furthermore, a spatial SS matching model that involves the object area, attribution, topology, and orientation features is proposed for the implementation of the sample-scene-based image retrieval. Moreover, a prototype system that uses a coarse-to-fine retrieval scheme is implemented with high retrieval accuracy. Experimental results show that the proposed method is suitable for spatial SS modeling, particularly geographic SS modeling, and performs well in spatial scene similarity matching.

关键词： Attributed relational graph (ARG) image retrieval object-based image analysis remote sensing (RS) image scene matching semantic semantic gap

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Automatic Moving Vehicles Information Extraction From Single-Pass WorldView-2 imagery

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2012年第1期5卷 135-145页

作者： Salehi, Bahram Zhang, Yun Zhong, Ming Univ New Brunswick Dept Geodesy & Geomat Engn Fredericton NB Canada Univ New Brunswick Dept Civil Engn Fredericton NB E3B 5A3 Canada Univ Calgary Inst Adv Policy Res IAPR Calgary AB T2N 1N4 Canada Univ Regina Regina SK S4S 0A2 Canada

Because of the sub-meter spatial resolution of very high resolution (VHR) optical satellite imagery, vehicles can be identified in this type of imagery. Further, because there is a time lag in image collection between the Panchromatic (Pan) and multispectral (MS) sensors onboard VHR satellites, a moving vehicle is observed by the satellite at slightly different times. Consequently, its velocity information including speed and direction can be determined. The higher spatial resolution and more spectral bands of WorldView-2 (WV2) imagery, compared to those of previous VHR satellites such as QuickBird and GeoEye-1, together with the new sensors' configuration of WV2, i.e., 4 bands on each side of the Pan sensor (MS1 and MS2), adds an opportunity to improve both moving vehicles extraction and the velocity estimation. In this paper, a novel processing framework is proposed for the automatic extraction of moving vehicles and determination of their velocities using single-pass WV2 imagery. The approach contains three major components: a) object-based road extraction, b) moving vehicle extraction from MS1 and MS2, and c) velocity estimation. The method was tested on two different areas of a WV2 image, a high speed and a low speed traffic zone. The method resulted in a correctness of 92% and a completeness of 77% for the extraction of moving vehicles. Furthermore, the estimated speeds and directions are very realistic and are consistent with the speed limits posted on the roads. The results demonstrate a promising potential for automatic and accurate traffic monitoring using a single image of WV2.

关键词： Moving vehicle extraction object-based image analysis road extraction speed determination time lag WorldView-2

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Inventorying ponds through novel size-adaptive object mapping using Sentinel-1/2 time series

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REMOTE SENSING OF ENVIRONMENT 2024年 315卷

作者： Liu, Denghong Zhu, Xiaolin Holgerson, Meredith Bansal, Sheel Xu, Xiangtao Hong Kong Polytech Univ Dept Land Surveying & Geoinformat Hong Kong Peoples R China Cornell Univ Ecol & Evolutionary Biol Ithaca NY 14850 USA Northern Prairie Wildlife Res Ctr US Geol Survey Jamestown ND USA

Ponds are an important source of greenhouse gases (GHGs) to the atmosphere, yet evaluating their role in global biogeochemical cycling is currently hampered by limitations in quantifying their global distribution. Existing satellite-derived estimates of lake distributions have difficulty identifying small lakes (5-10 ha) and ponds (<5 ha) due to limitations in satellite resolution and challenges extracting individual small waterbodies from low-albedo surfaces, vegetated water, and lotic water systems including rivers and streams. In this study, we developed generalizable pond mapping strategies based on their spatial-temporal-spectral characteristics to fully exploit accessible medium-resolution optical and synthetic aperture radar (SAR) time series to identify ponds. Our novel approach entails: (1) making full use of ponds' characteristics from an object-based perspective;(2) extracting pond objects using seeds of prominent water pixels defined by the SAR VH signal;(3) constructing training samples of ponds with high representativeness;and (4) improving inter-class discrimination by combining features from optical and SAR data. We designed a novel Optical-SAR Pond object Mapper (OptiSAR-POM) to achieve an improved estimate of pond size distribution by incorporating mapping strategies into the object-based image analysis framework. We generated landscape objects through an elaborate water-focused segmentation approach, which adaptively aligned the segmentation parameters with the size and distribution patterns of ponds to identify small waterbodies and increase inter-class variability. We further introduced an interactive learning process to construct random forests for object-based classification, which incorporated adaptive empirical thresholds to identify potential pond objects and select representative training samples of varying sizes. We tested the OptiSAR-POM framework using Sentinel-1/2 time series at three county-level study sites and three supplementary wa

关键词： Ponds Optical-SAR Mapping object-based image analysis Sentinel-1/2 imagery

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object-based spatiotemporal analysis of vine canopy vigor using an inexpensive unmanned aerial vehicle remote sensing system

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JOURNAL OF APPLIED REMOTE SENSING 2014年第1期8卷

作者： Mathews, Adam J. Oklahoma State Univ Dept Geog Stillwater OK 74078 USA

Remotely sensed imagery provides a rapid assessment of spatial variability in grapevine canopy vigor that correlates with crop performance. Unmanned aerial vehicles (UAVs) provide a low-cost image acquisition platform with high spatial and temporal resolutions. Using a UAV and digital cameras, aerial images of a Texas vineyard were captured at postflowering, veraison, and harvest. imagery was processed to generate orthophotos in units of reflectance, which were then segmented to extract per-vine estimates of canopy area (planimetric extent) and normalized difference vegetation index (NDVI)-based canopy density. Derived canopy area and density values were compared to the harvest variables of number of clusters, cluster size, and yield to explore correlations. Planimetrically derived canopy area yielded significant, positive relationships, whereas NDVI-based canopy density exhibited no significant relationships due to sensor-related radiometric inaccuracy. A vine performance index was calculated to map spatial variation in canopy vigor for the entire growing season. Future management zones were delineated using spatial grouping analysis. (C) 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： unmanned aerial vehicle remote sensing precision viticulture canopy vigor object-based image analysis

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Parameter optimization of image classification techniques to delineate crowns of coppice trees on UltraCam-D aerial imagery in woodlands

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JOURNAL OF APPLIED REMOTE SENSING 2014年第1期8卷

作者： Erfanifard, Yousef Sterenczak, Krzysztof Behnia, Negin Shiraz Univ Coll Agr Dept Nat Resources & Environm Shiraz *** Iran Forest Res Inst IBL PL-05090 Warsaw Poland

Estimating the optimal parameters of some classification techniques becomes their negative aspect as it affects their performance for a given dataset and reduces classification accuracy. It was aimed to optimize the combination of effective parameters of support vector machine (SVM), artificial neural network (ANN), and object-based image analysis (OBIA) classification techniques by the Taguchi method. The optimized techniques were applied to delineate crowns of Persian oak coppice trees on UltraCam-D very high spatial resolution aerial imagery in Zagros semiarid woodlands, Iran. The imagery was classified and the maps were assessed by receiver operating characteristic curve and other performance metrics. The results showed that Taguchi is a robust approach to optimize the combination of effective parameters in these image classification techniques. The area under curve (AUC) showed that the optimized OBIA could well discriminate tree crowns on the imagery (AUC = 0.897), while SVM and ANN yielded slightly less AUC performances of 0.819 and 0.850, respectively. The indices of accuracy (0.999) and precision (0.999) and performance metrics of specificity (0.999) and sensitivity (0.999) in the optimized OBIA were higher than with other techniques. The optimization of effective parameters of image classification techniques by the Taguchi method, thus, provided encouraging results to discriminate the crowns of Persian oak coppice trees on UltraCam-D aerial imagery in Zagros semiarid woodlands. (C) 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： artificial neural network object-based image analysis Persian oak support vector machine UltraCam-D Zagros

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A novel per pixel and object-based ensemble approach for flood susceptibility mapping

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GEOMATICS NATURAL HAZARDS & RISK 2020年第1期11卷 2147-2175页

作者： Gudiyangada Nachappa, Thimmaiah Meena, Sansar Raj Univ Salzburg Dept Geoinformat Z GIS Salzburg Austria

Conducting flood susceptibility assessments is critical for the identification of flood hazard zones and the mitigation of the detrimental impacts of floods in the future through improved flood management measures. The significance of this study is that we create ensemble methods using the per-pixel approaches of frequency ratio (FR), analytical hierarchical process (AHP), and evidence belief function (EBF) used for weightings with the object-based 'geons' approach used for aggregation to create a flood susceptibility map for the East Rapti Basin in Nepal. We selected eight flood conditioning factors considered to be relevant in the study area. The flood inventory data for the East Rapti basin was derived from past flood inventory datasets held in the regional database system by the International Centre for Integrated Mountain Development (ICIMOD). The flood inventory was classified into training and validation datasets based on the widely used split ratio of 70/30. The Receiver Operating Characteristic (ROC) was used to determine the accuracy of the flood susceptibility maps. The AUC results indicated that the combined per-pixel and object-based geon approaches yielded better results than the per-pixel approaches alone. Our results showed that the object-based geon approach creates meaningful regional units that are beneficial for future planning.

关键词： Floods flood susceptibility object-based image analysis FR AHP EBF natural hazards

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Large-area settlement pattern recognition from Landsat-8 data

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2016年第0期119卷 294-308页

作者： Wieland, Marc Pittore, Massimiliano GFZ German Res Ctr Geosci Ctr Early Warning Syst D-14473 Potsdam Germany

The study presents an image processing and analysis pipeline that combines object-based image analysis with a Support Vector Machine to derive a multi-layered settlement product from Landsat-8 data over large areas. 43 image scenes are processed over large parts of Central Asia (Southern Kazakhstan, Kyrgyzstan, Tajikistan and Eastern Uzbekistan). The main tasks tackled by this work include built-up area identification, settlement type classification and urban structure types pattern recognition. Besides commonly used accuracy assessments of the resulting map products, thorough performance evaluations are carried out under varying conditions to tune algorithm parameters and assess their applicability for the given tasks. As part of this, several research questions are being addressed. In particular the influence of the improved spatial and spectral resolution of Landsat-8 on the SVM performance to identify built-up areas and urban structure types are evaluated. Also the influence of an extended feature space including digital elevation model features is tested for mountainous regions. Moreover, the spatial distribution of classification uncertainties is analyzed and compared to the heterogeneity of the building stock within the computational unit of the segments. The study concludes that the information content of Landsat-8 images is sufficient for the tested classification tasks and even detailed urban structures could be extracted with satisfying accuracy. Freely available ancillary settlement point location data could further improve the built-up area classification. Digital elevation features and pan-sharpening could, however, not significantly improve the classification results. The study highlights the importance of dynamically tuned classifier parameters, and underlines the use of Shannon entropy computed from the soft answers of the SVM as a valid measure of the spatial distribution of classification uncertainties. (C) 2016 International Society for Photogram

关键词： Landsat-8 object-based image analysis Machine learning Settlements Central Asia

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