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Conference on Digital image Computing - Techniques and Applications (DICTA)

作者： McLaughlin, Connor Woodley, Alan Geva, Shlomo Chappell, Timothy Kelly, Wayne Boles, Wageeh De Vine, Lance Hutson, Holly Queensland Univ Technol Sci & Engn Faulty Brisbane Qld Australia ARC Ctr Excellence Math & Stat Frontiers Brisbane Qld Australia

ISBN: (纸本)9781728191089

Identifying changes on the Earth's surface is one of the most fundamental aspects of Earth observation from satellite images. Historically, the predominant form of analysis has measured change at a pixel level. Here, we present a new strategy that conducts the analysis based on objects. The objects are placed inside a random forest regressor. We have tested our approach in Queensland, Australia using Sentinel data. We find that the use of object-based approach either outperforms or is comparable to alternative approaches.

关键词： remote sensing land change object-based image analysis

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Using Uncrewed Aerial Vehicles for Identifying the Extent of Invasive Phragmites australis in Treatment Areas Enrolled in an Adaptive Management Program

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REMOTE SENSING 2021年第10期13卷 1895-1895页

作者： Brooks, Colin Weinstein, Charlotte Poley, Andrew Grimm, Amanda Marion, Nicholas Bourgeau-Chavez, Laura Hansen, Dana Kowalski, Kurt Michigan Technol Univ Michigan Tech Res Inst 3600 Green CourtSuite 100 Ann Arbor MI 48105 USA Alaska Reg Off Natl Pk Serv 240 W 5th Ave Anchorage AK 99501 USA US Geol Survey Great Lakes Sci Ctr 1451 Green Rd Ann Arbor MI 48105 USA

Higher spatial and temporal resolutions of remote sensing data are likely to be useful for ecological monitoring efforts. There are many different treatment approaches for the introduced European genotype of Phragmites australis, and adaptive management principles are being integrated in at least some long-term monitoring efforts. In this paper, we investigated how natural color and a smaller set of near-infrared (NIR) images collected with low-cost uncrewed aerial vehicles (UAVs) could help quantify the aboveground effects of management efforts at 20 sites enrolled in the Phragmites Adaptive Management Framework (PAMF) spanning the coastal Laurentian Great Lakes region. We used object-based image analysis and field ground truth data to classify the Phragmites and other cover types present at each of the sites and calculate the percent cover of Phragmites, including whether it was alive or dead, in the UAV images. The mean overall accuracy for our analysis with natural color data was 91.7% using four standardized classes (Live Phragmites, Dead Phragmites, Other Vegetation, Other Non-vegetation). The Live Phragmites class had a mean user's accuracy of 90.3% and a mean producer's accuracy of 90.1%, and the Dead Phragmites class had a mean user's accuracy of 76.5% and a mean producer's accuracy of 85.2% (not all classes existed at all sites). These results show that UAV-based imaging and object-based classification can be a useful tool to measure the extent of dead and live Phragmites at a series of sites undergoing management. Overall, these results indicate that UAV sensing appears to be a useful tool for identifying the extent of Phragmites at management sites.

关键词： Phragmites australis uncrewed drone monitoring invasive adaptive management mapping object-based image analysis

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object-based Predictive Modeling (OBPM) for Archaeology: Finding Control Places in Mountainous Environments

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REMOTE SENSING 2021年第6期13卷 1197-1197页

作者： Magnini, Luigi Bettineschi, Cinzia Univ Sassari Dept Hist Human Sci & Educ I-07100 Sassari Italy Univ Padua Dept Cultural Heritage Archaeol & Hist Art Cinema I-35139 Padua Italy

This contribution examines the potential of object-based image analysis (OBIA) for archaeological predictive modeling starting from elevation data, by testing a ruleset for the location of "control places" on two test areas in the Alpine environment (northern Italy). The ruleset was developed on the western Asiago Plateau (Vicenza Province, Veneto) and subsequently re-applied (semi)automatically in the Isarco Valley (South Tirol). Firstly, we considered the physiographic, climatic, and morphological characteristics of the selected areas and we applied 3 DTM processing techniques: Slope, local dominance, and solar radiation. Subsequently, we employed an object-based approach to classification. Solar radiation, local dominance, and slope were visualized as a three-layer RGB image that was segmented with the multiresolution algorithm. The classification was implemented with a ruleset that selected only image-objects with high local dominance and solar radiation, but low slope, which were considered more suitable parameters for human occupation. The classification returned five areas on the Asiago Plateau that were remotely and ground controlled, confirming anthropic exploitation covering a time span from protohistory (2nd-1st millennium BC) to the First World War. Subsequently, the same model was applied to the Isarco Valley to verify the replicability of the method. The procedure resulted in 36 potential control places which find good correspondence with the archaeological sites discovered in the area. Previously unknown contexts were further controlled using very high-resolution (VHR) aerial images and digital terrain model (DTM) data, which often suggested a possible (pre-proto)historic human frequentation. The outcomes of the analysis proved the feasibility of the approach, which can be exported and applied to similar mountainous landscapes for site predictivity analysis.

关键词： predictive modeling object-based image analysis control places archaeological remote sensing northern Italy hilltop settlements protohistory

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RISK ASSESSMENT OF DRINKING WATER SOURCE based ON HIGH SPATIAL RESOLUTION REMOTE SENSING

RISK ASSESSMENT OF DRINKING WATER SOURCE BASED ON HIGH SPATI...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Zheng, Yalan Shen, Qian Tao, Shikang Cao, Qi Feng, Chenyang Wang, Min Nanjing Normal Univ Sch Geog Nanjing 210023 Peoples R China Nanjing Normal Univ Minist Educ Key Lab Virtual Geog Environm Nanjing 210023 Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Reso Nanjing 210023 Peoples R China State Key Lab Cultivat Base Geog Environm Evolut Nanjing 210023 Peoples R China Minist Nat Resources Key Lab Urban Land Resources Monitoring & Simulat Shenzhen 518034 Peoples R China

ISBN: (纸本)9781728163741

This study combines object-based image analysis (OBIA) and deep learning (DL) techniques for automatic risk source extraction and risk assessment in drinking water sources using high-spatial-resolution (HSR) imagery. HSR images are first segmented to obtain the object primitives (OP), and the DL training samples are collected based on the OPs. U-Net semantic segmentation and object-based classification methods are then used to extract the risk source targets. Analytic hierarchy process (AHP) method is used to obtain the weights of risk sources. The risk assessment model is then established which comprehensively evaluates the safety level of drinking water source environment. Experiments at the Jiajiang water source of Nanjing city are carried out based on china's GaoFen-2 (GF-2) imagery, in which the risk sources such as farmlands, woodlands, grasslands, roads, and industrial and residential buildings are extracted and evaluated of their risk influences by the comprehensive risk assessment index (RAI). It shows that on December 12, 2018, the environment of Jiajiang drinking water source is at a safety level of "high".

关键词： risk source drinking water source high-spatial-resolution remote sensing deep learning object-based image analysis

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Remote Sensing image Classification with a Graph-based Pre-Trained Neighborhood Spatial Relationship

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SENSORS 2021年第16期21卷 5602-5602页

作者： Guan, Xudong Huang, Chong Yang, Juan Li, Ainong Chinese Acad Sci Res Ctr Digital Mt & Remote Sensing Applicat Inst Mt Hazards & Environm Chengdu 610041 Peoples R China Chinese Acad Sci State Key Lab Resources & Environm Informat Syst Inst Geog Sci & Nat Resources Res Beijing 100101 Peoples R China Shaanxi Energy Inst Xianyang 712000 Peoples R China

Previous knowledge of the possible spatial relationships between land cover types is one factor that makes remote sensing image classification "smarter". In recent years, knowledge graphs, which are based on a graph data structure, have been studied in the community of remote sensing for their ability to build extensible relationships between geographic entities. This paper implements a classification scheme considering the neighborhood relationship of land cover by extracting information from a graph. First, a graph representing the spatial relationships of land cover types was built based on an existing land cover map. Empirical probability distributions of the spatial relationships were then extracted using this graph. Second, an image was classified based on an object-based fuzzy classifier. Finally, the membership of objects and the attributes of their neighborhood objects were joined to decide the final classes. Two experiments were implemented. Overall accuracy of the two experiments increased by 5.2% and 0.6%, showing that this method has the ability to correct misclassified patches using the spatial relationship between geo-entities. However, two issues must be considered when applying spatial relationships to image classification. The first is the "siphonic effect" produced by neighborhood patches. Second, the use of global spatial relationships derived from a pre-trained graph loses local spatial relationship in-formation to some degree.

关键词： remote sensing image classification SVM (Support Vector Machine) knowledge graph object-based image analysis fuzzy classification graph theory

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Post-processing of Pixel and object-based Land Cover Classifications of Very High Spatial Resolution images 20th

Post-processing of Pixel and Object-Based Land Cover Classif...

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20th International Conference on Computational Science and Its Applications (ICCSA)

作者： Sarzana, Tommaso Maltese, Antonino Capolupo, Alessandra Tarantino, Eufemia Univ Palermo I-91011 Palermo PA Italy Politecn Bari I-70125 Bari BA Italy

ISBN: (纸本)9783030588113;9783030588106

The state of the art is plenty of classification methods. Pixel-based methods include the most traditional ones. Although these achieved high accuracy when classifying remote sensing images, some limits emerged with the advent of very high-resolution images that enhanced the spectral heterogeneity within a class. Therefore, in the last decade, new classification methods capable of overcoming these limits have undergone considerable development. Within this research, we compared the performances of an object-based and a Pixel-based classification method, the Random Forests (RF) and the object-based image analysis (OBIA), respectively. Their ability to quantify the extension and the perimeter of the elements of each class was evaluated through some performance indices. Algorithm parameters were calibrated on a subset, then, applied on the whole image. Since these algorithms perform accurately in quantifying the elements areas, but worse if we consider the perimeters length, hence, the aim of this research was to setup some post-processing techniques to improve, in particular, this latter performance. Algorithms were applied on peculiar classes of an area comprising the Isole dello Stagnone di Marsala oriented natural reserve, in north-western corner of Si-cily, salt pans and agricultural settlements. The area was covered by a World View-2 multispectral image consisting of eight spectral bands spanning from visible to near-infrared wavelengths and characterized by a spatial resolution of two meters. Both classification algorithms did not quantify accurately object perimeters;especially RF. Post-processing algorithm improved the estimates, which however remained more accurate for OBIA than for RF.

关键词： Random forest object-based image analysis Vector based generalization

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Using Unmanned Aerial Vehicles to assess the rehabilitation performance of open cut coal mines

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JOURNAL OF CLEANER PRODUCTION 2019年 209卷 819-833页

作者： Johansen, Kasper Erskine, Peter D. McCabe, Matthew F. King Abdullah Univ Sci & Technol Water Desalinat & Reuse Ctr Al Jazri Bldg West Thuwal 239556900 Saudi Arabia Univ Queensland Sustainable Minerals Inst Ctr Mined Land Rehabil Brisbane Qld 4072 Australia

Mine sites are routinely required to rehabilitate their post-mining landforms with a safe, stable and sustainable land-cover. To assess these post-mining landforrns, traditional on-ground field monitoring is generally undertaken. However, these labour intensive and time-consuming measurements are generally insufficient to catalogue land rehabilitation efforts across the large scales typical of mining sites (>100 ha). As an alternative, information derived from Unmanned Aerial Vehicles (UAV) can be used to map rehabilitation success and provide evidence of achieving rehabilitation site requirements across a range of scales. UAV based sensors have the capacity to collect information on rehabilitation sites with extensive spatial coverage in a repeatable, flexible and cost-effective manner. Here, we present an approach to automatically map indicators of safety, stability and sustainability of rehabilitation efforts, and demonstrate this framework across three coalmine sites. Using multi-spectral UAV imagery together with geographic object-based image analysis, an empirical classification system is proposed to convert these indicators into a status category based on a number of criteria related to land-cover, landform, erosion, and vegetation structure. For this study, these criteria include: mapping tall trees (Eucalyptus species);vegetation extent;senescent vegetation;extent of bare ground;and steep slopes. Converting these land-cover indicators into appropriate mapping categories on a polygon basis indicated the level of rehabilitation success and how these varied across sites and age of the rehabilitation activity. This work presents a framework and workflow for undertaking a UAV based assessment of safety, stability and sustainability of mine rehabilitation and also provides a set of recommendations for future rehabilitation assessment efforts. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Open cut mine Rehabilitation Monitoring UAV object-based image analysis Indicators

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Incorporating crop phenological trajectory and texture for paddy rice detection with time series MODIS, HJ-1A and ALOS PALSAR imagery

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EUROPEAN JOURNAL OF REMOTE SENSING 2019年第1期52卷 73-87页

作者： Singha, Mrinal Sarmah, Sangeeta Chinese Acad Sci Inst Geog Sci & Nat Resources Res Beijing 100100 Peoples R China Univ Chinese Acad Sci Inst Atmospher Phys Beijing Peoples R China

Repeated monitoring of paddy rice is essential for government agencies and policy makers to maintain the balance of supply and demand for rice. Recent studies have mostly concentrated on the mapping of paddy rice with temporal satellite imagery during growing seasons. Given the phenological variation within paddy rice fields and spectral confusion between paddies and other vegetation classes, our ability to identify paddy rice fields with temporal imagery remains limited. The objective of this study is to develop new phenology and textural-based strategies to detect paddies with HJ-1A, MODIS and PALSAR FNF imagery. Two phenology-based strategies that track the seasonal trajectory of crops and one textural-based strategy that contains image surface characteristics are presented. With the proposed strategies, temporal, spectral and textural features were investigated for paddy rice detection. The results indicate that the phenology-based strategies could reveal the phenological variation within paddy rice and significantly improved the detection accuracy. Seasonal amplitude, grey level co-occurrence matrix entropy and spectral features of the heading stage were proven to be important in identifying paddy rice. It was concluded that the combination of HJ-1A, MODIS and PALSAR FNF imagery are promising in facilitating the rapid mapping of paddy rice at a regional scale.

关键词： Paddy rice phenology texture MODIS NDVI HJ-1A PALSAR object-based image analysis

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Combining random forest and support vector machines for object-based rural-land-cover classification using high spatial resolution imagery

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JOURNAL OF APPLIED REMOTE SENSING 2019年第1期13卷

作者： Xu, Saiping Zhao, Qianjun Yin, Kai Zhang, Feifei Liu, Dingbang Yang, Guang Chinese Acad Sci Inst Remote Sensing & Digital Earth Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China

Land-cover classification using remote sensing imagery is an important part of environmental research because it provides baseline information for ecological vulnerability and risk assessment, disaster management, landscape conservation, local and regional planning, and so on. Rural-land-cover classification is challenging for both object-based image analysis methods and classifiers. The objective of this study is to improve the object-oriented classification accuracy of rural land cover by combining two models based on high spatial resolution imagery. We apply the C5.0 algorithm in R to combine support vector machines (SVMs) and random forest (RF) to create the model RS_C5.0. The effectiveness of the model combination is assessed by comparing the classification results with the state-of-the-art machine learning algorithm, namely extreme gradient boosting (XGBoost). The comparisons are done based on the classification results of both the study area and the case area. Results show that in the classification of the study area, RF performs slightly better than SVM, and XGBoost performs worse than RF but better than SVM. However, in the classification of the case area, SVM performs slightly better than RF and both SVM and RF perform better than XGBoost. Furthermore, RS_C5.0 obtains the highest overall accuracies and kappa coefficients in the classifications of both the study area and the case area. In terms of training time, XGBoost runs the slowest in the classifications of both the study area and the case area. SVM and RF as well as the combined model (RS_C5.0) run much faster than XGBoost classifier. To summarize, the combination of SVM and RF classifiers using C5.0 algorithm is found to be a fast and effective way to improve rural-land-cover classification. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： random forest support vector machines C5.0 algorithm XGBoost algorithm object-based image analysis

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Detecting olive quick decline syndrome: A satellite-based dataset for a case study in Apulia Region

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Data in Brief 2025年 60卷

作者： Lorenzo Crecco Danilo Morelli Elisabetta Raparelli Mara Di Giulio Sofia Bajocco

The bacterium Xylella fastidiosa (Xf) is a plant pathogen first identified in Europe in 2013, specifically in olive groves in the Apulia region (south-eastern Italy). It is now spreading across the Mediterranean basin and poses a serious threat to the local economy by causing branch desiccation and the rapid death of olive trees, a condition known as olive quick decline syndrome (OQDS). Several studies have investigated the potential of remote sensing (RS) technology to monitor OQDS over time and space; however, accurate and reliable data on OQDS occurrence remain scarce. To enhance the distribution data of Xf-infected trees in the Apulia region, we investigated an infection hotspot of 25 km² area in the province of Brindisi, where records of infections were documented in 2019 and 2020. Three very high resolution, commercial WorldView-2 images were acquired and segmented, resulting in a dataset of 76637 olive trees. Through visual interpretation, 2340 trees were identified most likely as either infected or removed due to OQDS. This dataset provides a valuable resource for developing or validating RS techniques for early detection of OQDS. Furthermore, it could support studies aimed to evaluate spectral bands or indices most correlated with infection presence. Finally, the dataset can be integrated with other Xf-infection presence data to support species distribution model studies.

关键词： Xylella fastidiosa OQDS WorldView-2 object-based image analysis Visual interpretation

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