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Refining the Classification Parameters for the Bely Island (Kara Sea) Terrain Larger-Scale image Interpretation with the Support Vector Method

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IZVESTIYA ATMOSPHERIC AND OCEANIC PHYSICS 2020年第12期56卷 1652-1663页

作者： Sizov, O. S. Idrisov, I. R. Yurtaev, A. A. Russian Acad Sci Oil & Gas Res Inst Moscow Russia Gubkin Russian State Univ Oil & Gas Moscow Russia Tyumen State Univ Tyumen Russia

In this study, we analyzed automated landscape mapping using Bely Island, the Kara Sea as an example. The island was selected since the Yamal-Nenets District local administration established a research station for continuous fundamental research of human activity affects the Arctic ecosystems. With a small test site that was field studied in summer, 2016, we considered the relevant approaches to landscape mapping based on object-based image analysis (OBIA) and support vector machine (SVM) classification. A combined method that uses independent processing of initial data with subsequent statistical analysis of the results is proposed. The study shows that this approach gives more detailed differentiation of landscape features in the area of interest. HR images from the Resurs-P satellite and Arctic DTM digital terrain models of the Bely Island were used for the first time. The object-based image analysis makes it possible to further expand the initial dataset by adding UAV survey images, while the SVM classification can be improved by adding dedicated layers intended for morphometric analysis of digital terrain models. The proposed technology applies to large-scale landscape mapping of the entire Bely Island.

关键词： Bely Island SVM classification remote sensing data Resurs-P satellite landscape mapping segmentation object-based image analysis

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Multi-Spectral image Classification based on an object-based Active Learning Approach

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REMOTE SENSING 2020年第3期12卷 504-504页

作者： Su, Tengfei Zhang, Shengwei Liu, Tingxi Inner Mongolia Agr Univ Coll Water Conservancy & Civil Engn Hohhot 010018 Peoples R China

In remote sensing, active learning (AL) is considered to be an effective solution to the problem of producing sufficient classification accuracy with a limited number of training samples. Though this field has been extensively studied, most papers exist in the pixel-based paradigm. In object-based image analysis (OBIA), AL has been comparatively less studied. This paper aims to propose a new AL method for selecting object-based samples. The proposed AL method solves the problem of how to identify the most informative segment-samples so that classification performance can be optimized. The advantage of this algorithm is that informativeness can be estimated by using various object-based features. The new approach has three key steps. First, a series of one-against-one binary random forest (RF) classifiers are initialized by using a small initial training set. This strategy allows for the estimation of the classification uncertainty in great detail. Second, each tested sample is processed by using the binary RFs, and a classification uncertainty value that can reflect informativeness is derived. Third, the samples with high uncertainty values are selected and then labeled by a supervisor. They are subsequently added into the training set, based on which the binary RFs are re-trained for the next iteration. The whole procedure is iterated until a stopping criterion is met. To validate the proposed method, three pairs of multi-spectral remote sensing images with different landscape patterns were used in this experiment. The results indicate that the proposed method can outperform other state-of-the-art AL methods. To be more specific, the highest overall accuracies for the three datasets were all obtained by using the proposed AL method, and the values were 88.32%, 85.77%, and 93.12% for "T1," "T2," and "T3," respectively. Furthermore, since object-based features have a serious impact on the performance of AL, eight combinations of four feature types are investigated. T

关键词： object-based image analysis active learning random forest feature category

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Spectral features of dual-frequency multibeam echosounder data for benthic habitat mapping

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MARINE GEOLOGY 2020年 427卷 106239-106239页

作者： Trzcinska, Karolina Janowski, Lukasz Nowak, Jaroslaw Rucinska-Zjadacz, Maria Kruss, Aleksandra von Deimling, Jens Schneider Pocwiardowski, Pawel Tegowski, Jaroslaw Univ Gdansk Inst Oceanog Al Marszalka Pilsudskieg 4 PL-81378 Gdynia Poland Gdynia Maritime Univ Morska 81-87 Str PL-81225 Gdynia Poland MEWO SA Starogardzka 16 PL-83010 Straszyn Poland NORBIT Poland Sp Zoo Al Niepodleglosci 813-815-24 PL-81810 Sopot Poland Christian Albrechts Univ Kiel Inst Geosci Otto Hahn Pl 1 D-24118 Kiel Germany

Automatic methods of seafloor mapping are still in their early stage of development, despite the technical progress made in recent years. A serious imperfection is the limited types of predictor features available for seabed classification. It is therefore desirable to introduce new class of spectral features to benthic habitat mapping. In this study, we introduced eight spectral features of a rough seafloor surface that were indicative of better seabed classification. We compared them with traditional secondary features, like terrain variables and textural features. The suitability of 48 variables was tested, and the most important features were identified. The selected variables were used to perform a supervised object-based image analysis using four machine learning algorithms. We found that backscatter was the strongest predictor, followed by several spectral features from bathymetry that appeared more predictive than bathymetry itself. The highest overall accuracy of predictive model reached approximately 86% using the support vector machine classifier. The innovative results of this study suggest further application of the spectral features for predictive benthic habitat mapping, including research based on multi-frequency multibeam echosounder datasets. The utilisation of spectral features derived from bathymetry provide an important step towards more accurate maps of benthic habitats and seabed sediments composition.

关键词： Rough seafloor surface Spectral features Multibeam echosounder object-based image analysis Benthic habitat mapping

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Large-scale spatial variability in loess landforms and their evolution, Luohe River Basin, Chinese Loess Plateau

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GEOMORPHOLOGY 2022年 415卷

作者： Wei, Hong Xiong, Liyang Zhao, Fei Tang, Guoan Lane, Stuart N. Nanjing Normal Univ School of Geog Nanjing 210023 Peoples R China Nanjing Normal Univ Key Lab Virtual Geog Environm Minist Educ Nanjing 210023 Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Resou Nanjing 210023 Peoples R China Univ Lausanne Inst Earth Surface Dynam IDYST CH-1015 Lausanne Switzerland

Loess landform variability across large spatial extents needs to be analyzed to understand the formation and evolution of loess landscapes. This is becoming increasingly possible via the automated analysis of remotely-sensed data. Here, we quantify loess landforms using an object-based image analysis (OBIA) method and use this classification to describe the spatial variability of loess landforms. Quantitative indicators are used to drive the spatial variability analysis of loess landforms and explain their spatio-temporal evolution. Moreover, the hypsometric integral (HI) and topographic interpolation are employed to investigate soil erosion and develop-ment patterns of loess landscape. Results show that the OBIA method classified loess landforms to an accuracy of 88.7 %. The derived metrics in terms of the area, slope and complexity of landform shape allow the determi-nation of the spatial structure of the loess landscapes. The HI value of the entire basin is 0.486, representing the mature stage of landform development, with relatively severe surface erosion. Correlation analysis of HI values and related indicators in the sub-basins shows that HI is poorly correlated with the area proportion of loess landform types and the total erosion volume in the basin but shows a relatively strong correlation with the volume of erosion per unit area.

关键词： Loess landform variability Hypsometric integral object-based image analysis Landscape pattern indices Chinese Loess Plateau

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Rainfall-induced landslide inventories for Lower Mekong based on Planet imagery and a semi-automatic mapping method

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GEOSCIENCE DATA JOURNAL 2022年第2期9卷 315-327页

作者： Amatya, Pukar Kirschbaum, Dalia Stanley, Thomas Univ Space Res Assoc Columbia MD USA Goddard Earth Sci Technol & Res Columbia MD USA NASA Hydrol Sci Lab Goddard Space Flight Ctr Greenbelt MD USA

Fatal landslides occur every year during the rainy season (June-November) in the Lower Mekong Region (LMR). There is an urgent need to develop a landslide early warning system in the LMR. In collaboration with the Asian Disasters Preparedness Center and NASA's SERVIR Programme, we are regionalizing the global Landslide Hazard Assessment System for Situational Awareness model for the LMR (LHASA-Mekong). A robust set of landslide inventories are needed to effectively train the machine learning-based LHASA-Mekong model. In this study, the Semi-Automatic Landslide Detection (SALaD) system was modified by incorporating a change detection module (SALaD-CD) to produce rainfall event-based landslide inventories using pre- and post-imagery from RapidEye and PlanetScope for various locations in the LMR that were identified based on media and government reports. These rainfall-induced landslides are published as initiation points for ease of use. In total, we created 22 inventories: 2 in Laos, 4 in Myanmar, 1 in Thailand and 15 in Vietnam. These inventories are being used to train the LHASA-Mekong model and quantify the effects of Land use/Land cover change on landslide susceptibility. These open data will be a valuable resource for advancing landslide studies in this region.

关键词： landslides Lower Mekong Region object-based image analysis SALaD SALaD-CD SERVIR-Mekong

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Modeling strawberry biomass and leaf area using object-based analysis of high-resolution images

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2020年 163卷 171-186页

作者： Guan, Zhen Abd-Elrahman, Amr Fan, Zhen Whitaker, Vance M. Wilkinson, Benjamin Univ Florida Geomat Sch Forest Resources & Conservat Geomat Inst Food & Agr Sci Gainesville FL 32611 USA Univ Florida Plant City Ctr Gulf Coast Res & Educ Ctr Inst Food & Agr Sci Gainesville FL 32611 USA Univ Florida Balm Ctr Gulf Coast Res & Educ Ctr Inst Food & Agr Sci Gainesville FL 32611 USA

Quantifying canopy biophysical parameters is critical to agricultural research and farm management. In this study, strawberry dry biomass and leaf area were modeled statistically using high spatial and temporal resolution imagery. A mobile field data acquisition system was used to acquire thousands of very high resolution (similar to 0.5 mm) close-range images seven times throughout the strawberry growing season. Ortho-mosaics and dense point clouds were generated through Structure from Motion (SfM) and used in object-based image analysis (OBIA) at the sub-leaf level to extract canopy structure variables such as planimetric canopy area, canopy average height, and canopy smoothness metric. Regression analysis was carried out using these image-derived canopy variables as predictors to model leaf area (R-2 = 0.79;ten-fold cross-validation RMSE = 0.056 m(2)) and dry biomass (R-2 = 0.84;ten-fold cross-validation RMSE = 7.72 g) obtained through destructive measurements. Results indicate consistent predictive power through the season and across 17 strawberry genotypes. The study showed that the canopy smoothness metric developed in this study as an indicator of canopy density could complement other variables (planimetric canopy area, canopy average height) that describe canopy geometric properties.

关键词： Precision agriculture Phenotyping close-range Remote Sensing Biomass modeling Leaf area modeling Smoothness metric object-based image analysis

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Mapping urban tree species by integrating multi-seasonal high resolution pleiades satellite imagery with airborne LiDAR data

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URBAN FORESTRY & URBAN GREENING 2020年 53卷 126675-126675页

作者： Pu, Ruiliang Landry, Shawn Univ S Florida Sch Geosci 4202 E Fowler AveNES 107 Tampa FL 33620 USA

Mapping individual tree species is critical to understand the ecosystem services value of the urban forest and improve management. The main aim of this study is to assess the effectiveness of integrating multi-seasonal high resolution Pleiades satellite images with airborne LiDAR data for improving urban tree species mapping in the City of Tampa, FL, USA. In this study, we evaluated and compared the abilities of multi-seasonal individual and combined Pleiades satellite images with airborne LiDAR data for classifying seven urban tree species to understand effects of season and tree canopy height information on tree species mapping accuracy. A multi-level classification system was adopted to classify image objects of the tree species and groups. To efficiently utilize seasonal change information of tree species, a seasonal trajectory difference index (STDI) was developed with multi-seasonal images. Species mapping accuracy was compared and evaluated among individual seasonal Pleiades images and combined dry-wet season images, and between feature types: (i) 6 canonical variables only, transformed by canonical discriminant analysis algorithm;ii) 6 canonical variables plus 2 normalized Digital Surface Model (nDSM) derived variables;and iii) 6 canonical variables plus 2 nDSM variables and 4 STDI band indices. The 6 canonical variables were extracted from all spectral and textural features (SFs) including tree species fraction features. Random Forest and Support Vector Machine classifiers were used to classify the seven tree species with tree crown image objects. The research results demonstrate that (1) with additional 2 nDSM variables and extracted 4 STDI band indices, the accuracy of mapping urban tree species could be further improved (p < 0.09);and (2) with the 6 canonical variables, tree species mapping accuracies could be significantly improved using late spring season (April) image compared to all other seasonal images (p < 0.01), combined dry-wet season images per

关键词： Canonical discriminant analysis Normalized DSM model object-based image analysis Pleiades imagery Random forest Seasonal trajectory different index (STDI) Support vector machine Urban forest

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object Oriented Classification for Mapping Mixed and Pure Forest Stands Using Very-High Resolution imagery

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REMOTE SENSING 2021年第13期13卷 2508-2508页

作者： Oreti, Loredana Giuliarelli, Diego Tomao, Antonio Barbati, Anna Univ Tuscia Dept Innovat Biol Agrofood & Forestry Syst DIBAF Via San Camillo Lellis I-01100 Viterbo Italy Council Agr Res & Econ Res Ctr Forestry & Wood Viale S Margherita 80 I-52100 Arezzo Italy

The importance of mixed forests is increasingly recognized on a scientific level, due to their greater productivity and efficiency in resource use, compared to pure stands. However, a reliable quantification of the actual spatial extent of mixed stands on a fine spatial scale is still lacking. Indeed, classification and mapping of mixed populations, especially with semi-automatic procedures, has been a challenging issue up to date. The main objective of this study is to evaluate the potential of object-based image analysis (OBIA) and Very-High-Resolution imagery (VHR) to detect and map mixed forests of broadleaves and coniferous trees with a Minimum Mapping Unit (MMU) of 500 m(2). This study evaluates segmentation-based classification paired with non-parametric method K- nearest-neighbors (K-NN), trained with a dataset independent from the validation one. The forest area mapped as mixed forest canopies in the study area amounts to 11%, with an overall accuracy being equal to 85% and K of 0.78. Better levels of user and producer accuracies (85-93%) are reached in conifer and broadleaved dominated stands. The study findings demonstrate that the very high resolution images (0.20 m of spatial resolutions) can be reliably used to detect the fine-grained pattern of rare mixed forests, thus supporting the monitoring and management of forest resources also on fine spatial scales.

关键词： mixed forests very-high-resolution imagery object-based image analysis multiresolution segmentation semi-automatic classification forest mapping Italy

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Conversion of Natural Wetland to Farmland in the Tumen River Basin: Human and Environmental Factors

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REMOTE SENSING 2021年第17期13卷 3498-3498页

作者： Liu, Yuyan Jin, Ri Zhu, Weihong Yanbian Univ Coll Geog & Ocean Sci Hunchun 133300 Peoples R China Jilin Prov Joint Key Lab Changbai Mt Wetland & Ec Changchun 130102 Peoples R China Jilin Prov Key Lab Changbai Mt Wetland Ecosyst Fu Hunchun 133300 Peoples R China Tumen River Basin Wetland & Ecol Int Joint Res Ct Hunchun 133300 Peoples R China Yanbian Univ Wetland Res Ctr Hunchun 133300 Peoples R China

Wetlands play an important role in the terrestrial ecosystem. However, agricultural activities have resulted in a significant decrease in natural wetlands around the world. In the Tumen River Basin (TRB), a border area between China, the Democratic People's Republic of Korea (DPRK), and Russia, natural wetlands have been reclaimed and converted into farmland, primarily due to the migration practices of Korean-Chinese. To understand the spatial and temporal patterns of this conversion from wetlands to farmland, Landsat remote sensing images from four time periods were analyzed. Almost 30 years of data were extracted using the object-oriented classification method combined with random forest classification. In addition, statistical analysis was conducted on the conversion from natural wetland to farmland and from farmland to wetland, as well as on the relationship between the driving factors. The results revealed that a loss of 49.2% (12,540.1 ha) of natural wetlands in the Chinese portion of the TRB was due to agricultural encroachment for grain production. At the sub-basin scale, the largest area of natural wetland converted into farmland in the past 30 years was in the Hunchun River Basin (HCH), which accounts for 22.0% (2761.2 ha) of the total. Meanwhile, 6571.4 ha of natural wetlands, mainly in the Gaya River Basin (GYH), have been restored from farmland. These changes are closely related to the migration of the agricultural populations.

关键词： natural wetland farmland Tumen river basin multi-scale analysis object-based image analysis

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Gradient Boosting Machine and object-based CNN for Land Cover Classification

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REMOTE SENSING 2021年第14期13卷 2709-2709页

作者： Bui, Quang-Thanh Chou, Tien-Yin Hoang, Thanh-Van Fang, Yao-Min Mu, Ching-Yun Huang, Pi-Hui Pham, Vu-Dong Nguyen, Quoc-Huy Do Thi Ngoc Anh Pham, Van-Manh Meadows, Michael E. Vietnam Natl Univ VNU Univ Sci Fac Geog Ctr Appl Res Remote Sensing & GIS CARGIS 334 Nguyen Trai Hanoi 11416 Vietnam Feng Chia Univ Geog Informat Syst Res Ctr Taichung 40724 Taiwan VNU Univ Sci Fac Geog 334 Nguyen Trai Hanoi 11416 Vietnam Univ Capetown Dept Environm & Geog Sci ZA-7701 Rondebosh South Africa East China Normal Univ Sch Geog Sci Shanghai 200241 Peoples R China Zhejiang Normal Univ Coll Geog & Environm Sci Jinhua 321004 Zhejiang Peoples R China

In regular convolutional neural networks (CNN), fully-connected layers act as classifiers to estimate the probabilities for each instance in classification tasks. The accuracy of CNNs can be improved by replacing fully connected layers with gradient boosting algorithms. In this regard, this study investigates three robust classifiers, namely XGBoost, LightGBM, and Catboost, in combination with a CNN for a land cover study in Hanoi, Vietnam. The experiments were implemented using SPOT7 imagery through (1) image segmentation and extraction of features, including spectral information and spatial metrics, (2) normalization of attribute values and generation of graphs, and (3) using graphs as the input dataset to the investigated models for classifying six land cover classes, namely House, Bare land, Vegetation, Water, Impervious Surface, and Shadow. The results show that CNN-based XGBoost (Overall accuracy = 0.8905), LightGBM (0.8956), and CatBoost (0.8956) outperform the other methods used for comparison. It can be seen that the combination of object-based image analysis and CNN-based gradient boosting algorithms significantly improves classification accuracies and can be considered as alternative methods for land cover analysis.

关键词： object-based image analysis gradient boosting convolutional neural network land cover

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