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Application and Evaluation of the AI-Powered Segment Anything Model (SAM) in Seafloor Mapping: A Case Study from Puck Lagoon, Poland

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REMOTE SENSING 2024年第14期16卷 2638页

作者： Janowski, Lukasz Wroblewski, Radoslaw Gdynia Maritime Univ Maritime Inst Roberta Plelo 20 PL-80548 Gdansk Poland Univ Gdansk Dept Geophys Pilsudskiego 46 PL-81378 Gdynia Poland MEWO SA Starogardzka 17A PL-83010 Straszyn Poland

The digital representation of seafloor, a challenge in UNESCO's Ocean Decade initiative, is essential for sustainable development support and marine environment protection, aligning with the United Nations' 2030 program goals. Accuracy in seafloor representation can be achieved through remote sensing measurements, including acoustic and laser sources. Ground truth information integration facilitates comprehensive seafloor assessment. The current seafloor mapping paradigm benefits from the object-based image analysis (OBIA) approach, managing high-resolution remote sensing measurements effectively. A critical OBIA step is the segmentation process, with various algorithms available. Recent artificial intelligence advancements have led to AI-powered segmentation algorithms development, like the Segment Anything Model (SAM) by META AI. This paper presents the SAM approach's first evaluation for seafloor mapping. The benchmark remote sensing dataset refers to Puck Lagoon, Poland and includes measurements from various sources, primarily multibeam echosounders, bathymetric lidar, airborne photogrammetry, and satellite imagery. The SAM algorithm's performance was evaluated on an affordable workstation equipped with an NVIDIA GPU, enabling CUDA architecture utilization. The growing popularity and demand for AI-based services predict their widespread application in future underwater remote sensing studies, regardless of the measurement technology used (acoustic, laser, or imagery). Applying SAM in Puck Lagoon seafloor mapping may benefit other seafloor mapping studies intending to employ AI technology.

关键词： Segment Anything object-based image analysis underwater remote sensing multibeam echosounder bathymetric LiDAR satellite-derived bathymetry seafloor mapping geomorphological bedforms Puck Lagoon

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Comparative Assessment of Five Machine Learning Algorithms for Supervised object-based Classification of Submerged Seagrass Beds Using High-Resolution UAS imagery

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REMOTE SENSING 2023年第14期15卷 3600-3600页

作者： Thomasberger, Aris Nielsen, Mette Moller Flindt, Mogens Rene Pawar, Satish Svane, Niels Tech Univ Denmark Natl Inst Aquat Resources Sect Coastal Ecol DK-2800 Lyngby Denmark Univ Southern Denmark Dept Biol DK-5230 Odense Denmark

Knowledge about the spatial distribution of seagrasses is essential for coastal conservation efforts. imagery obtained from unoccupied aerial systems (UAS) has the potential to provide such knowledge. Classifier choice and hyperparameter settings are, however, often based on time-consuming trial-and-error procedures. The presented study has therefore investigated the performance of five machine learning algorithms, i.e., Bayes, Decision Trees (DT), Random Trees (RT), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM) when used for the object-based classification of submerged seagrasses from UAS-derived imagery. The influence of hyperparameter tuning and training sample size on the classification accuracy was tested on images obtained from different altitudes during different environmental conditions. The Bayes classifier performed well (94% OA) on images obtained during favorable environmental conditions. The DT and RT classifier performed better on low-altitude images (93% and 94% OA, respectively). The kNN classifier was outperformed on all occasions, while still producing OA between 89% and 95% in five out of eight scenarios. The SVM classifier was most sensitive to hyperparameter tuning with OAs ranging between 18% and 97%;however, it achieved the highest OAs most often. The findings of this study will help to choose the appropriate classifier and optimize related hyperparameter settings.

关键词： object-based image analysis OBIA unoccupied aerial systems UAS drones photogrammetry machine learning hyperparameter seagrasses coastal zone mapping Limfjorden

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Landscape-Level Associations of Wintering Waterbird Diversity and Abundance from Remotely Sensed Wetland Characteristics of Poyang Lake

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REMOTE SENSING 2016年第6期8卷 462-462页

作者： Dronova, Iryna Beissinger, Steven R. Burnham, James W. Gong, Peng Univ Calif Berkeley Dept Landscape Architecture & Environm Planning Coll Environm Design Berkeley CA 94720 USA Univ Calif Berkeley Div Ecosyst Sci Dept Environm Sci Policy & Management Coll Nat Resources Berkeley CA 94720 USA Univ Wisconsin Dept Forest & Wildlife Ecol Coll Agr & Life Sci Madison WI 53706 USA Int Crane Fdn Baraboo WI 53913 USA Tsinghua Univ Ctr Earth Syst Sci Minist Educ Key Lab Earth Syst Modeling Beijing 100084 Peoples R China Poyang Lake Ecol Res Stn Environm & Hlth Duchang 332600 Peoples R China

Poyang Lake, the largest freshwater wetland in China, provides critical habitat for wintering waterbirds from the East Asian Flyway;however, landscape drivers of non-uniform bird diversity and abundance are not yet well understood. Using a winter 2006 waterbird survey, we examined the relationships among metrics of bird community diversity and abundance and landscape characteristics of 51 wetland sub-lakes derived by an object-based classification of Landsat satellite data. Relative importance of predictors and their sets was assessed using information-theoretic model selection and the Akaike Information Criterion. Ordinary least squares regression models were diagnosed and corrected for spatial autocorrelation using spatial autoregressive lag and error models. The strongest and most consistent landscape predictors included Normalized Difference Vegetation Index for mudflat (negative effect) and emergent grassland (positive effect), total sub-lake area (positive effect), and proportion of submerged vegetation (negative effect). Significant spatial autocorrelation in linear regression was associated with local clustering of response and predictor variables, and should be further explored for selection of wetland sampling units and management of protected areas. Overall, results corroborate the utility of remote sensing to elucidate potential indicators of waterbird diversity that complement logistically challenging ground observations and offer new hypotheses on factors underlying community distributions.

关键词： wetlands lakes remote sensing waterbird biodiversity conservation spatial autocorrelation object-based image analysis ecology habitat

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Comparison and Assessment of Data Sources with Different Spatial and Temporal Resolution for Efficiency Orchard Mapping: Case Studies in Five Grape-Growing Regions

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REMOTE SENSING 2023年第3期15卷 655-655页

作者： Yao, Zhiying Zhao, Yuanyuan Wang, Hengbin Li, Hongdong Yuan, Xinqun Ren, Tianwei Yu, Le Liu, Zhe Zhang, Xiaodong Li, Shaoming China Agr Univ Coll Land Sci & Technol Beijing 100083 Peoples R China Minist Agr & Rural Affairs Key Lab Remote Sensing Agrihazards Beijing 100083 Peoples R China Minist Nat Resources Peoples Republ China Key Lab Agr Land Qual Beijing 100083 Peoples R China Tsinghua Univ Inst Global Change Studies Dept Earth Syst Sci Minist EducKey Lab Earth Syst Modelling Beijing 100084 Peoples R China Tsinghua Univ Dept Earth Syst Sci Minist Educ Ecol Field Stn East Asian Migratory Bi Beijing 100084 Peoples R China

As one of the most important agricultural production types in the world, orchards have high economic, ecological, and cultural value, so the accurate and timely mapping of orchards is highly demanded for many applications. Selecting a remote-sensing (RS) data source is a critical step in efficient orchard mapping, and it is hard to have a RS image with both rich temporal and spatial information. A trade-off between spatial and temporal resolution must be made. Taking grape-growing regions as an example, we tested imagery at different spatial and temporal resolutions as classification inputs (including from Worldview-2, Landsat-8, and Sentinel-2) and compared and assessed their orchard-mapping performance using the same classifier of random forest. Our results showed that the overall accuracies improved from 0.6 to 0.8 as the spatial resolution of the input images increased from 58.86 m to 0.46 m (simulated from Worldview-2 imagery). The overall accuracy improved from 0.7 to 0.86 when the number of images used for classification was increased from 2 to 20 (Landsat-8) or approximately 60 (Sentinel-2) in one year. The marginal benefit of increasing the level of details (LoD) of temporal features on accuracy is higher than that of spatial features, indicating that the classification ability of temporal information is higher than that of spatial information. The highest accuracy of using a very high-resolution (VHR) image can be exceeded only by using four to five medium-resolution multi-temporal images, or even two to three growing season images with the same classifier. Combining the spatial and temporal features from multi-source data can improve the overall accuracies by 5% to 7% compared to using only temporal features. It can also compensate for the accuracy loss caused by missing data or low-quality images in single-source input. Although selecting multi-source data can obtain the best accuracy, selecting single-source data can improve computational efficiency and

关键词： orchard mapping spatial features temporal features data selection object-based image analysis Google Earth Engine

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Extraction of Broad-Leaved Tree Crown based on UAV Visible images and OBIA-RF Model: A Case Study for Chinese Olive Trees

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REMOTE SENSING 2022年第10期14卷 2469页

作者： Yang, Kaile Zhang, Houxi Wang, Fan Lai, Riwen Fujian Agr & Forestry Univ Forestry Coll Fuzhou 350002 Peoples R China State Forestry & Grassland Adm Soil & Water Conse Key Lab Fuzhou 350002 Peoples R China Cross Strait Collaborat Innovat Ctr Soil & Water Fuzhou 350002 Peoples R China Univ Key Lab Geomat Technol & Optimized Resources Fuzhou 350002 Peoples R China

Chinese olive trees (Canarium album L.) are broad-leaved species that are widely planted in China. Accurately obtaining tree crown information provides important data for evaluating Chinese olive tree growth status, water and fertilizer management, and yield estimation. To this end, this study first used unmanned aerial vehicle (UAV) images in the visible band as the source of remote sensing (RS) data. Second, based on spectral features of the image object, the vegetation index, shape, texture, and terrain features were introduced. Finally, the extraction effect of different feature dimensions was analyzed based on the random forest (RF) algorithm, and the performance of different classifiers was compared based on the features after dimensionality reduction. The results showed that the difference in feature dimensionality and importance was the main factor that led to a change in extraction accuracy. RF has the best extraction effect among the current mainstream machine learning (ML) algorithms. In comparison with the pixel-based (PB) classification method, the object-based image analysis (OBIA) method can extract features of each element of RS images, which has certain advantages. Therefore, the combination of OBIA and RF algorithms is a good solution for Chinese olive tree crown (COTC) extraction based on UAV visible band images.

关键词： remote sensing unmanned aerial vehicle object-based image analysis random forest feature selection tree crown extraction

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Towards periodic and time-referenced flood risk assessment using airborne remote sensing

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JOURNAL OF HYDROLOGY AND HYDROMECHANICS 2016年第4期64卷 438-447页

作者： Brenner, Claire Meisch, Claude Apperl, Benjamin Schulz, Karsten Univ Nat Resources & Life Sci Inst Water Management Hydrol & Hydraul Engn IWHW Muthgasse 18 A-1190 Vienna Austria

Flood risk management is founded on the regular assessment of damage potential. A significant parameter for assessing damage potential is the number of at-risk objects. However, data sets on exposure are often incomplete and/or lack time-references. Airborne remote sensing data, such as orthophotos, offers a regularly-updated, time-referenced depiction of land use. As such, remote sensing data compensates for incomplete data sources (such as digital cadastral maps). Orthophotos can even be used to analyze the temporal dynamics of flood damage potential, providing that time-referenced information is available for multiple time points. This paper describes a method for integrating orthophotos into flood risk analyses. In Austria, orthophotos are updated every three years, allowing their integration into cyclical flood risk assessments. The results of a case study presented in this paper demonstrate that orthophotos are most useful where other data sources, such as digital cadastral maps, are incomplete. In such situations, orthophotos lead to a significant increase in estimated damage potential. Orthophoto analysis allows damage potentials to be re-assessed at regular intervals, another major advantage over digital cadastral maps. Orthophoto analysis thus supports the evaluation of flood risk management options.

关键词： Flood risk management Damage potential analysis object-based image analysis Orthophoto analysis Building detection

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Uncertainty analysis for object-based Change Detection in Very High-Resolution Satellite images Using Deep Learning Network

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REMOTE SENSING 2020年第15期12卷 2345-2345页

作者： Song, Ahram Kim, Yongil Han, Youkyung Seoul Natl Univ Dept Civil & Environm Engn 1 Gwanak Ro Seoul 08826 South Korea Kyungpook Natl Univ Sch Convergence & Fus Syst Engn Sangju 37224 South Korea

object-based image analysis (OBIA) is better than pixel-based image analysis for change detection (CD) in very high-resolution (VHR) remote sensing images. Although the effectiveness of deep learning approaches has recently been proved, few studies have investigated OBIA and deep learning for CD. Previously proposed methods use the object information obtained from the preprocessing and postprocessing phase of deep learning. In general, they use the dominant or most frequently used label information with respect to all the pixels inside an object without considering any quantitative criteria to integrate the deep learning network and object information. In this study, we developed an object-based CD method for VHR satellite images using a deep learning network to denote the uncertainty associated with an object and effectively detect the changes in an area without the ground truth data. The proposed method defines the uncertainty associated with an object and mainly includes two phases. Initially, CD objects were generated by unsupervised CD methods, and the objects were used to train the CD network comprising three-dimensional convolutional layers and convolutional long short-term memory layers. The CD objects were updated according to the uncertainty level after the learning process was completed. Further, the updated CD objects were considered as the training data for the CD network. This process was repeated until the entire area was classified into two classes, i.e., change and no-change, with respect to the object units or defined epoch. The experiments conducted using two different VHR satellite images confirmed that the proposed method achieved the best performance when compared with the performances obtained using the traditional CD approaches. The method was less affected by salt and pepper noise and could effectively extract the region of change in object units without ground truth data. Furthermore, the proposed method can offer advantages associated with

关键词： change detection object-based image analysis very high-resolution satellite images deep learning network

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Optimized Segmentation based on the Weighted Aggregation Method for Loess Bank Gully Mapping

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REMOTE SENSING 2020年第5期12卷 793-793页

作者： Ding, Hu Liu, Kai Chen, Xiaozheng Xiong, Liyang Tang, Guoan Qiu, Fang Strobl, Josef Nanjing Normal Univ Key Lab Virtual Geog Environm Minist Educ Nanjing 210023 Peoples R China South China Normal Univ Sch Geog Guangzhou 510631 Peoples R China Chinese Acad Sci Nanjing Inst Geog & Limnol Key Lab Watershed Geog Sci Nanjing 210008 Peoples R China Nanjing Normal Univ Sch Geog Nanjing 210023 Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Reso Nanjing 210023 Peoples R China Univ Texas Dallas Dept Geospatial Sci Richardson TX 75080 USA Univ Salzburg Dept Geoinformat Z GIS A-5020 Salzburg Austria

The Chinese Loess Plateau suffers severe gully erosion. Gully mapping is a fundamental task for gully erosion monitoring in this region. Among the different gully types in the Loess Plateau, the bank gully is usually regarded as the most important source for the generation of sediment. However, approaches for bank gully extraction are still limited. This study put forward an integrated framework, including segmentation optimization, evaluation and Extreme Gradient Boosting (XGBoost)-based classification, for the bank gully mapping of Zhifanggou catchment in the Chinese Loess Plateau. The approach was conducted using a 1-m resolution digital elevation model (DEM), based on unmanned aerial vehicle (UAV) photogrammetry and WorldView-3 imagery. The methodology first divided the study area into different watersheds. Then, segmentation by weighted aggregation (SWA) was implemented to generate multi-level segments. For achieving an optimum segmentation, area-weighted variance (WV) and Moran's I (MI) were adopted and calculated within each sub-watershed. After that, a new discrepancy metric, the area-number index (ANI), was developed for evaluating the segmentation results, and the results were compared with the multi-resolution segmentation (MRS) algorithm. Finally, bank gully mappings were obtained based on the XGBoost model after fine-tuning. The experiment results demonstrate that the proposed method can achieve superior segmentation compared to MRS. Moreover, the overall accuracy of the bank gully extraction results was 78.57%. The proposed approach provides a credible tool for mapping bank gullies, which could be useful for the catchment-scale gully erosion process.

关键词： object-based image analysis gully mapping segmentation optimization unmanned aerial vehicle (UAV) XGBoost

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UAV-based Land Cover Classification for Hoverfly (Diptera: Syrphidae) Habitat Condition Assessment: A Case Study on Mt. Stara Planina (Serbia)

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REMOTE SENSING 2021年第16期13卷 3272页

作者： Ivosevic, Bojana Lugonja, Predrag Brdar, Sanja Radulovic, Mirjana Vujic, Ante Valente, Joao Univ Novi Sad BioSense Inst Res Inst Informat Technol Biosyst Novi Sad 21101 Serbia Univ Novi Sad Dept Biol & Ecol Fac Sci Novi Sad 21102 Serbia Wageningen Univ & Res Informat Technol Grp NL-6706 KN Wageningen Netherlands

Habitat degradation, mostly caused by human impact, is one of the key drivers of biodiversity loss. This is a global problem, causing a decline in the number of pollinators, such as hoverflies. In the process of digitalizing ecological studies in Serbia, remote-sensing-based land cover classification has become a key component for both current and future research. object-based land cover classification, using machine learning algorithms of very high resolution (VHR) imagery acquired by an unmanned aerial vehicle (UAV) was carried out in three different study sites on Mt. Stara Planina, Eastern Serbia. UAV land cover classified maps with seven land cover classes (trees, shrubs, meadows, road, water, agricultural land, and forest patches) were studied. Moreover, three different classification algorithms-support vector machine (SVM), random forest (RF), and k-NN (k-nearest neighbors)-were compared. This study shows that the random forest classifier performs better with respect to the other classifiers in all three study sites, with overall accuracy values ranging from 0.87 to 0.96. The overall results are robust to changes in labeling ground truth subsets. The obtained UAV land cover classified maps were compared with the Map of the Natural Vegetation of Europe (EPNV) and used to quantify habitat degradation and assess hoverfly species richness. It was concluded that the percentage of habitat degradation is primarily caused by anthropogenic pressure, thus affecting the richness of hoverfly species in the study sites. In order to enable research reproducibility, the datasets used in this study are made available in a public repository.

关键词： unmanned aerial vehicle object-based image analysis Orfeo ToolBox QGIS random forest hoverfly Map of the Natural Vegetation of Europe

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Mapping Vegetation at Species Level with High-Resolution Multispectral and Lidar Data Over a Large Spatial Area: A Case Study with Kudzu

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REMOTE SENSING 2020年第4期12卷 609-609页

作者： Liang, Wanwan Abidi, Mongi Carrasco, Luis McNelis, Jack Tran, Liem Li, Yingkui Grant, Jerome Univ Tennessee Dept Entomol & Plant Pathol Knoxville TN 37996 USA North Carolina State Univ Ctr Geospatial Analyt Raleigh NC 27695 USA Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA Natl Inst Math & Biol Synth Knoxville TN 37996 USA Univ Tennessee Dept Ecol & Evolutionary Biol Knoxville TN 37996 USA Oak Ridge Natl Lab Oak Ridge TN 37831 USA Univ Tennessee Dept Geog Knoxville TN 37996 USA

Mapping vegetation species is critical to facilitate related quantitative assessment, and mapping invasive plants is important to enhance monitoring and management activities. Integrating high-resolution multispectral remote-sensing (RS) images and lidar (light detection and ranging) point clouds can provide robust features for vegetation mapping. However, using multiple sources of high-resolution RS data for vegetation mapping on a large spatial scale can be both computationally and sampling intensive. Here, we designed a two-step classification workflow to potentially decrease computational cost and sampling effort and to increase classification accuracy by integrating multispectral and lidar data in order to derive spectral, textural, and structural features for mapping target vegetation species. We used this workflow to classify kudzu, an aggressive invasive vine, in the entire Knox County (1362 km(2)) of Tennessee (U.S.). object-based image analysis was conducted in the workflow. The first-step classification used 320 kudzu samples and extensive, coarsely labeled samples (based on national land cover) to generate an overprediction map of kudzu using random forest (RF). For the second step, 350 samples were randomly extracted from the overpredicted kudzu and labeled manually for the final prediction using RF and support vector machine (SVM). Computationally intensive features were only used for the second-step classification. SVM had constantly better accuracy than RF, and the producer's accuracy, user's accuracy, and Kappa for the SVM model on kudzu were 0.94, 0.96, and 0.90, respectively. SVM predicted 1010 kudzu patches covering 1.29 km(2) in Knox County. We found the sample size of kudzu used for algorithm training impacted the accuracy and number of kudzu predicted. The proposed workflow could also improve sampling efficiency and specificity. Our workflow had much higher accuracy than the traditional method conducted in this research, and could be easily im

关键词： detailed vegetation mapping kudzu mapping coarse label two-step classification object-based image analysis lidar point clouds sampling specificity

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