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Comparing random forests and convoluted neural networks for mapping ghost crab burrows using imagery from an unmanned aerial vehicle

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ESTUARINE COASTAL AND SHELF SCIENCE 2019年 224卷 84-93页

作者： Bycroft, Rachel Leon, Javier X. Schoeman, David Univ Sunshine Coast Sch Sci & Engn Global Change Ecol Res Grp Sippy Downs Qld 4556 Australia Nelson Mandela Univ Dept Zool Ctr African Conservat Ecol Port Elizabeth South Africa

Sandy beaches are important ecosystems that line a third of the world's ice-free coastlines. Unfortunately, these environments and the life they support are often threatened by various anthropogenic and natural factors. Monitoring sandy beach health is important to aid in appropriate management decisions. One such method of quantifying environmental health is using bioindicators. Ghost crabs are a commonly used sandy beach bioindicator. Current techniques for assessing ghost crab abundance and distribution data involve manually counting each individual burrow opening, which can intrusive and timely for a large area. The aim of this study was to assesses the use of imagery from an unmanned aerial vehicle and machine-learning algorithms as an alternative approach to monitoring ghost crab burrows. The accuracy and transferability of random forest (RF) and convolutional neural networks (CNN) classifiers within an object-based image analysis (OBIA) framework were tested using hyper-resolution (0.04 m) orthomosaics from four different dates. CNN was a more robust classifier with higher accuracies (max F-score 0.84). Transferability of rule sets and models was limited for both classifiers, particularly when applied to sub-optimal imagery. Overall, we present a feasible workflow that provides ecologist and environmental managers with a cost-effective and less invasive alternative to mapping ghost crab burrows.

关键词： Machine-learning Sandy beach object-based image analysis Drones

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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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Incorporating road and parcel data for object-based classification of detailed urban land covers from NAIP images

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GISCIENCE & REMOTE SENSING 2014年第5期51卷 498-520页

作者： Qiu, Xiaomin Wu, Shuo-Sheng Miao, Xin Missouri State Univ Dept Geog Geol & Planning Springfield MO 65897 USA

A map showing various urban features, such as buildings, roads, and vegetation, is useful for a variety of urban planning applications. The objective of this study was to incorporate road and parcel GIS data as well as relevant expert knowledge to classify different urban land covers from 1-meter, 4-band NAIP images. based on a hybrid simultaneous-classification and one-by-one-classification approach, a total of 14 urban classes are classified. The classification map has an overall accuracy of 90%, demonstrating a noticeable improvement over past comparable studies on detailed urban land cover classification.

关键词： OBIA urban land cover classification object-based image analysis Feature Analyst((R)) NAIP

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A local approach to optimize the scale parameter in multiresolution segmentation for multispectral imagery

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GEOCARTO INTERNATIONAL 2015年第8期30卷 937-961页

作者： Canovas-Garcia, F. Alonso-Sarria, F. Univ Cuenca Dept Ingn Civil Cuenca Ecuador Univ Murcia Inst Univ Agua & Medio Ambiente Murcia Spain

The results obtained using the object-based image analysis approach for remote sensing image analysis depend strongly on the quality of the segmentation step. In this paper, to optimize the scale parameter in a multiresolution segmentation, we analyse a high-resolution image of a large and heterogeneous agricultural area. This approach is based on using a set of agricultural plots extracted from official maps as uniform spatial units. The scale parameter is then optimized in each uniform spatial unit. Intra-object and inter-object heterogeneity measurements are used to evaluate each segmentation. To avoid subsegmentation, some oversegmentation is allowed, but is attenuated in a second step using the spectral difference segmentation algorithm. The statistical distribution of the scale parameter is not equal in all land uses, indicating the soundness of this local approach. A quantitative assessment of the results was also conducted for the different land covers. The results indicate that the spectral contrast between objects is larger with the local approach than with the global approach. These differences were statistically significant in all land uses except irrigated fruit trees and greenhouses. In the absence of subsegmentation, this suggests that the objects will be placed far apart in the space of variables, even if they are very close in the physical space. This is an obvious advantage in a subsequent classification of the objects.

关键词： object-based image analysis segmentation optimization eCognition(TM)

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Automatic detection of sinkhole collapses at finer resolutions using a multi-component remote sensing approach

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NATURAL HAZARDS 2015年第2期78卷 1021-1044页

作者： Dou, Jie Li, Xia Yunus, Ali P. Paudel, Uttam Chang, Kuan-Tsung Zhu, Zhongfan Pourghasemi, Hamid Reza Univ Tokyo Dept Nat Environm Studies Kashiwa Chiba 2778568 Japan Sun Yat Sen Univ Sch Geog & Planning Guangzhou 510275 Guangdong Peoples R China Minghsin Univ Sci & Technol Dept Civil Engn & Environm Informat Hsinchu Taiwan Beijing Normal Univ Coll Water Sci Beijing 100875 Peoples R China Shiraz Univ Coll Agr Dept Nat Resources & Environm Shiraz Iran

Sinkhole development is a typical geological disaster found in areas of carbonate bedrock. Compared with other geological disasters, sinkholes are considerably smaller and scattered according to scale and spatial distribution. Nevertheless, detecting and investigating sinkholes have become increasingly challenging. This study proposes a novel method by applying case-based reasoning (CBR) combined with object-based image analysis and genetic algorithms (GAs) to detect the sinkholes using high-resolution aerial images. This case study was performed in Paitan Town, Guangdong Province, China. The method comprises three major steps: (1) multi-image segmentation, (2) GA-based feature selection, and (3) application of CBR techniques. The detected sinkholes were categorized into three classes: buried, collapse type I, and collapse type II. The experiment demonstrated that the proposed method can obtain higher accuracy compared with the traditional supervised maximum likelihood classifier (MLC). The overall accuracy of CBR classification and MLC for the collapse area was 0.88 and 0.71, respectively. In addition, the kappa coefficient for CBR classification (0.81) was higher than that for MLC (0.5). A similar case library was also applied to another trial area for validation, the satisfactory results of which suggested that CBR is applicable for independently detecting sinkholes. The proposed method will be useful for preparing hazard maps that express the relative probability of a collapse in similar regions.

关键词： Sinkholes object-based image analysis image segmentation Hazard mapping

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object-based mapping of native vegetation and para grass (Urochloa mutica) on a monsoonal wetland of Kakadu NP using a Landsat 5 TM Dry-season time series

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JOURNAL OF SPATIAL SCIENCE 2013年第1期58卷 53-77页

作者： Boyden, James Joyce, Karen E. Boggs, Guy Wurm, Penny Charles Darwin Univ Sch Environm & Life Sci Res Inst Environm & Livelihoods Darwin NT 0909 Australia Wheatbelt Nat Resource Management Northam WA 6401 Australia

This paper evaluates the use of multi-temporal Landsat 5 TM for object-based classification of native wetland vegetation and the perennial aquatic weed para grass within Kakadu National Park, Northern Territory, Australia. Using identical training data and segmentation, a nearest-neighbour classification produced from a four-image (dry season) time-series was compared with four 'single-date' classifications produced from the individual images of the same series. A 15-class vegetation map generated from the multi-date classification produced an overall accuracy of 82 percent (kappa = 0.80). This was an average increase in accuracy of 25 percent (kappa = 0.28) compared to single-date classifications. The multi-date image composite also improved segmentation quality and spectral separability of vegetation classes. Reliable maps of wetland vegetation, potentially useful for strategic conservation, can be produced by integrated, object-based, analysis of multi-temporal Landsat.

关键词： object-based image analysis wetland vegetation mapping multi-temporal Landsat weeds

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Comparing object-based and pixel-based classifications for mapping savannas

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2011年第6期13卷 884-893页

作者： Whiteside, Timothy G. Boggs, Guy S. Maier, Stefan W. Bachelor Inst Indigenous Tertiary Educ Fac Hlth Business & Sci Batchelor NT 0845 Australia Charles Darwin Univ Sch Environm & Life Sci Darwin NT 0909 Australia

The development of robust object-based classification methods suitable for medium to high resolution satellite imagery provides a valid alternative to 'traditional' pixel-based methods. This paper compares the results of an object-based classification to a supervised per-pixel classification for mapping land cover in the tropical north of the Northern Territory of Australia. The object-based approach involved segmentation of image data into objects at multiple scale levels. objects were assigned classes using training objects and the Nearest Neighbour supervised and fuzzy classification algorithm. The supervised pixel-based classification involved the selection of training areas and a classification using the maximum likelihood classifier algorithm. Site-specific accuracy assessment using confusion matrices of both classifications were undertaken based on 256 reference sites. A comparison of the results shows a statistically significant higher overall accuracy of the object-based classification over the pixel-based classification. The incorporation of a digital elevation model (DEM) layer and associated class rules into the object-based classification produced slightly higher accuracies overall and for certain classes;however this was not statistically significant over the object-based using spectral information solely. The results indicate object-based analysis has good potential for extracting land cover information from satellite imagery captured over spatially heterogeneous land covers of tropical Australia. (C) 2011 Elsevier B.V. All rights reserved.

关键词： object-based image analysis Accuracy assessment Tropical savanna Northern Australia

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Monotemporal assessment of the population structure of Acacia tortilis (Forssk.) Hayne ssp raddiana (Savi) Brenan in Bou Hedma National Park, Tunisia: A terrestrial and remote sensing approach

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JOURNAL OF ARID ENVIRONMENTS 2016年第Jun.期129卷 80-92页

作者： van Coillie, Frieke Delaplace, Kevin Gabriels, Donald de Smet, Koen Ouessar, Mohammed Belgacem, Azaiez Ouled Taamallah, Houcine de Wulf, Robert Univ Ghent Lab Forest Management & Spatial Informat Tech Coupure Links 653 B-9000 Ghent Belgium Intergraph Belgium Tennessee HouseRiverside Business Pk B-1070 Brussels Belgium Univ Ghent Dept Soil Management Coupure Links 653 B-9000 Ghent Belgium Flemish Govt Environm Nat & Energy Dept Koning Albert 2 Laan 20Bus 8 B-1000 Brussels Belgium Inst Reg Arides Medenine Medenine 4119 Tunisia

Soil erosion and desertification are the main problems faced by the Bou-Hedma National Park in South Tunisia. Restoration of the original woodland cover, particularly by afforestation and reforestation with Acacia tortilis ssp. raddiatta, has been recognized as efficient to combat further degradation of the environment in this protected area. In order to study effects of woodland restoration and future trends in Bou-Hedma, it is essential to accurately assess its current situation. This paper addresses a mono temporal assessment of the population structure of A. tortilis. An extensive field inventory was performed to provide deeper insight into the dendrometric characteristics of this keystone species. Next, a spatially explicit and repeatable method is developed to model key tree attributes like crown diameter, volume and tree height from which the structural composition of the A. tortilis population in the Bou Hedma National Park is derived. This method involves analysis of a very high resolution (VHR) GeoEye-1 image within an OBIA (object-based image analysis) framework. The remote sensing (RS) results show that the population of A. tortilis is typified by an irregular population structure and confirm the findings of Noumi and Chaieb (2012) suggesting regressive population dynamics. The RS approach demonstrates potential for monitoring purposes in this particular setting of an arid environment. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Remote sensing object-based image analysis

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object-based crop classification in Hetao plain using random forest

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EARTH SCIENCE INFORMATICS 2021年第1期14卷 119-131页

作者： Su, Tengfei Zhang, Shengwei Inner Mongolia Agr Univ Coll Water Conservancy & Civil Engn 306 Zhaowuda Rd Hohhot Peoples R China Inner Mongolia Autonomous Reg Key Lab Big Data Re 306 Zhaowuda Rd Hohhot Peoples R China

Crop classification based on object-based image analysis (OBIA) is increasingly reported. However, it is still challenging to produce high-quality crop type maps by using recent techniques. This article introduces a new object-based crop classification algorithm which contains 4 steps. First, a random forest (RF) classifier is trained by using the initial training set, which tends to have a relatively small size. Second, importance scores for each feature variable are derived by using the RF model. Third, by treating the importance scores as weighting factors, a weighted Euclidean distance criterion is designed and used for sample creation to enlarge training set. Fourth, RF is re-trained by using the enlarged training set, and then it is employed for final classification. To validate the proposed strategy, a Worldview-2 image covering a part of Hetao plain is experimented. Results indicate that the new method yields the best overall accuracy, which equals 90.52%.

关键词： object-based image analysis Crop classification Random forest

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Joint use of remote sensing data and volunteered geographic information for exposure estimation: evidence from Valparaiso, Chile

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NATURAL HAZARDS 2017年第1-Sup期86卷 81-105页

作者： Geiss, Christian Schauss, Anne Riedlinger, Torsten Dech, Stefan Zelaya, Cecilia Guzman, Nicolas Hube, Mathias A. Arsanjani, Jamal Jokar Taubenboeck, Hannes German Aerosp Ctr DLR German Remote Sensing Data Ctr DFD Munchner Str 20 D-82234 Oberpfaffenhofen Germany Chilean Navy Hydrog & Oceanog Serv SHOA Errazuriz Echaurren 254 Playa Ancha Valparaiso Chile Pontificia Univ Catolica Chile Vicuna Mackenna 4860 Santiago Chile Natl Res Ctr Integrated Nat Disaster Management C Vicuna Mackenna 4860 Santiago Chile Heidelberg Univ GISci Res Grp Berliner Str 48 D-69120 Heidelberg Germany

The impact of natural hazards on mankind has increased dramatically over the past decades. Global urbanization processes and increasing spatial concentrations of exposed elements induce natural hazard risk at a uniquely high level. To mitigate affiliated perils requires detailed knowledge about elements at risk. Considering a high spatiotemporal variability of elements at risk, detailed information is costly in terms of both time and economic resources and therefore often incomplete, aggregated, or outdated. To alleviate these restrictions, the availability of very-high-resolution satellite images promotes accurate and detailed analysis of exposure over various spatial scales with large-area coverage. In the past, valuable approaches were proposed;however, the design of information extraction procedures with a high level of automatization remains challenging. In this paper, we uniquely combine remote sensing data and volunteered geographic information from the OpenStreetMap project (OSM) (i.e., freely accessible geospatial information compiled by volunteers) for a highly automated estimation of crucial exposure components (i.e., number of buildings and population) with a high level of spatial detail. To this purpose, we first obtain labeled training segments from the OSM data in conjunction with the satellite imagery. This allows for learning a supervised algorithmic model (i.e., rotation forest) in order to extract relevant thematic classes of land use/land cover (LULC) from the satellite imagery. Extracted information is jointly deployed with information from the OSM data to estimate the number of buildings with regression techniques (i.e., a multi-linear model from ordinary least-square optimization and a nonlinear support vector regression model are considered). Analogously, urban LULC information is used in conjunction with OSM data to spatially disaggregate population information. Experimental results were obtained for the city of Valparaiso in Chile. Thereby,

关键词： Exposure Risk Vulnerability Remote sensing Volunteered geographic information Land-use-land-cover classification object-based image analysis Rotation forest Population disaggregation Tsunami

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