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Monitoring Post Disturbance Forest Regeneration with Hierarchical object-based image analysis

FORESTS 2013年第4期4卷 808-829页

作者： Moskal, L. Monika Jakubauskas, Mark E. Univ Washington Remote Sensing & Geospatial Anal Lab Sch Environm & Forest Sci Coll Environm Seattle WA 98195 USA Univ Kansas Kansas Appl Remote Sensing Program Lawrence KS 66047 USA

The main goal of this exploratory project was to quantify seedling density in post fire regeneration sites, with the following objectives: to evaluate the application of second order image texture (SOIT) in image segmentation, and to apply the object-based image analysis (OBIA) approach to develop a hierarchical classification. With the utilization of image texture we successfully developed a methodology to classify hyperspatial (high-spatial) imagery to fine detail level of tree crowns, shadows and understory, while still allowing discrimination between density classes and mature forest versus burn classes. At the most detailed hierarchical Level I classification accuracies reached 78.8%, a Level II stand density classification produced accuracies of 89.1% and the same accuracy was achieved by the coarse general classification at Level III. Our interpretation of these results suggests hyperspatial imagery can be applied to post-fire forest density and regeneration mapping.

关键词： seedling regeneration object based image analysis hierarchical classification

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Mapping stony rise landforms using a novel remote sensing, geophysical, and machine learning approach

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GEOMORPHOLOGY 2024年 450卷

作者： Fraser, Shaye Soto-Berelov, Mariela Holden, Lucas Hewson, Robert Webb, John Jones, Simon RMIT Univ Sch Sci Melbourne Australia Univ Twente Fac Geoinformat Sci & Earth Observat ITC Dept Appl Earth Sci Enschede Netherlands La Trobe Univ Discipline Ecol & Environm Melbourne Vic Australia

A basalt lava plain covers much of central and western Victoria in Australia. Many of the younger flows (<1 Ma) have hummocky surfaces with abundant tumuli and lava rises, known locally as stony rises due to the protruding basalt boulders. Stony rises are noteworthy geologically but have ecological importance and are culturally significant to Aboriginal Australians. Ecologically, stony rises host many native plant species. Culturally, stony rises provide evidence of Aboriginal land use and occupation through the presence of artefact deposits. Currently, mapping of stony rises is carried out by conducting field surveys on a study-by-study basis. This could be performed more rapidly using remote sensing datasets such as LiDAR, satellite and aerial imagery, and geophysical data. This research mapped stony rises in an area that is experiencing rapid urban development using a combination of remotely sensed datasets that characterise the morphology of stony rises through a Machine Learning approach that utilises object based image analysis (OBIA) segmentation and Random Forests (RF) classification techniques. The research successfully identified stony rises in the landscape to an accuracy of 89.97 %, with 314 potential new stony rise sites identified. The research identified the most important predictor variables were slope, local elevation, the concentrations of thorium and uranium (obtained from airborne radiometric data), aeromagnetic data (total magnetic intensity), the Normalised Difference Water Index (NDWI), and the Clay Mineral Ratio. The results from this study lay a foundation for standardising the definition of a stony rise within Australia. Furthermore, by utilising various predictor variables, this study highlights the success of a multifaceted approach into detecting a specific landform at a landscape level.

关键词： Stony rises Basalt outcrop Remote sensing Machine learning object based image analysis Random forests

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Parcel level staple crop type identification based on newly defined red-edge vegetation indices and ORNN

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2023年第1期211卷

作者： Xu, Lu Ming, Dongping Zhang, Liang Dong, Dehui Qing, Yuanzhao Yang, Jianyu Zhou, Chenghu China Univ Geosci Beijing Sch Informat Engn Beijing 100083 Peoples R China Minist Nat Resources China Polytech Ctr Nat Resources Big data Beijing 100036 Peoples R China China Agr Univ Coll Land Sci & Technol Beijing 100083 Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res State Key Lab Resources & Environm Informat Syst Beijing 100101 Peoples R China

It is of strategic importance for early estimating planting area and crop yield to identify staple crop types on parcel level in a remotely sensed way. Limited by remote sensor imaging mechanism and current sensor technology, remote sensing images usually cannot be of high resolution in both spatial and spectral aspects. So, it has been found in many studies that supplementary data or features such as synthetic aperture radar data or temporal characteristic of time series data are usually integrated for crop type identification, which demands lots of data and overlooks the most typical spectral characteristics. Thanks to the expanded detecting bands of several moderate resolution optical satellites, the red-edge bands reflecting specific spectral features of crops are available for precise classification. Nevertheless, only a few studies have concentrated on these bands and the potential value of red-edge bands has not been sufficiently stretched in practical use. In this study, two newly defined red-edge vegetation indices (REVIs) and their time series data have been used for distinguishing different crops efficiently. Firstly, according to the reflectance characteristics in optical images and the data distribution of crops in feature space, two kinds of variants of REVIs based on green band and red-edge band, namely, ReG_RVI and ReG_NDVI have been proposed. Then, the time series data of REVIs can be generated from remote sensing images and be fed into the recurrent neural network (RNN) as training data for crop type classification. Finally, object based image analysis and RNN are combined with the idea of majority voting to achieve accurate crop type identification on cultivated land parcel level. The experimental results of two study areas using time series images captured by Gaofen 6 wide field of view sensor show an overall accuracy of above 90 % in identifying crop types, which proves the effectiveness of the newly defined REVIs and the feasibility of the prop

关键词： Crop type Remote sensing Red-edge vegetation index Recurrent neural network object based image analysis

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Exploring the Potential of object based image analysis for Mapping Urban Land Cover

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2012年第3期40卷 507-518页

作者： Jalan, Seema Govt PG Coll Dept Geog Dausa Rajasthan India

The paper investigates the performance and potential of object based image analysis technique (OBIA) for land cover information extraction from high resolution satellite datasets. Efficiency of the technique has been assessed in different urban land cover situations using merged CARTOSAT-1 and IRS-P6 LISS-IV image subsets of Dehradun, India. Multi-scale iterative Nearest Neighbour classification has been applied. Classification results have been quantitatively evaluated. Results reveal that OBIA is a fast, simple, flexible and efficient semi automated tool to deal with high spatial and spectral heterogeneity inherent in high resolution urban datasets. Integration of shape and texture characteristics with traditional spectral signatures, and use of multiple object levels in the classification process yielded classified map units having high correspondence with real world geographical entities. High accuracies were achieved for spectrally and spatially distinct classes like built up structures and tree covered land. Performance of the technique is impaired in case of classes having overlapping spectral characteristics. The parameters for segmentation and class descriptions developed for one area were successfully transferred to the other with minor adaptations. Utility of the technique is further enhanced by flexibility of visualisation of maps at different levels of classification hierarchy and immediate integration of the classified product in GIS environment with direct realization of vector maps for spatial analysis.

关键词： object based image analysis Urban Areas Land Cover Classification High Resolution Data Nearest Neighbour Method

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Multi-scale RoIs selection for classifying multi-spectral images

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MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING 2020年第2期31卷 745-769页

作者： Seal, Ayan Garcia-Pedrero, Angel Bhattacharjee, Debotosh Nasipuri, Mita Lillo-Saavedra, Mario Menasalvas, Ernestina Gonzalo-Martin, Consuleo PDPM Indian Inst Informat Technol Design & Mfg Jabalpur 482005 India Univ Politecn Madrid Ctr Biomed Technol Campus Montegancedo Pozuelo De Alarcon 28233 Spain Jadavpur Univ Kolkata India Univ Concepcion Concepcion Chile

The applications of object-based image analysis (OBIA) in remote sensing studies have received a considerable amount of attention over the recent decade due to dramatically increasing of the spatial resolution of satellite imaging sensors for earth observation. In this study, an unsupervised methodology based on OBIA paradigm for the estimation of multi-scale training sets for land cover classification is proposed. The proposed method consists of selection of valid region of interests in an unsupervised way and its characterization using some attributes in order to form meaningful and reliable training sets for supervised classification of different land covers of a satellite image. Multi-scale image segmentation is a prerequisite step for estimation of multi-scale training sets. However, scale selection remains a challenge in multi-scale segmentation. In this work, we propose a method to determine the appropriate segmentation scale for each land cover with the help of prior knowledge in the form of in-situ data. The proposed method is further discussed and validated through multi-scale segmentation using quick shift and random forest algorithms on two multi-spectral images captured using Worldview-2 sensor. Experimental results indicate that the proposed method qualitatively and quantitatively outperforms three state-of-the-art methods.

关键词： object based image analysis Multi-scale RoIs Quick shift Random forest

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REMOTE SENSING OF ENVIRONMENT 2020年 245卷 111795-111795页

作者： Fitoka, Eleni Tompoulidou, Maria Hatziiordanou, Lena Apostolakis, Antonis Hoefer, Rene Weise, Kathrin Ververis, Charalampos Greek Biotope Wetland Ctr EKBY Goulandris Nat Hist Museum Thermi Greece Jena Optron GmbH Jena Germany Minist Environm & Energy Dept Protected Areas Athens Greece

Mapping and assessment of water-related ecosystems is a challenging task that requires advanced processing techniques with clear rules and standards in terminology and definition of class features. These ecosystems are hydrologically and ecologically connected at catchment level and co-exist within a human context. The SWOS (Satellite based Wetland Observation Service - Horizon 2020) national service case for mapping and assessing the 10 Greek Ramsar wetland sites and their catchment areas is built on the requirements of the Ramsar Convention on Wetlands and of the Aichi Biodiversity Targets of the Strategic Plan for Biodiversity 2011-2020. It contributes directly to Sustainable Development Goal (SDG) Global Indicator 6.6.1 "Sub-Indicator 1 - spatial extent of water-related ecosystems". An object-based image analysis (OBIA) approach was adopted using Sentinel-2 satellite images for the year 2017, to discriminate 31 classes (wetland and non-wetland) over an area of 2,015,591 ha. The classification model was further adjusted to Landsat 5 TM imagery of previous years (1986-1987) in order to extract possible changes in the spatial extent of water-related ecosystems. The Mapping and Assessment of Ecosystems and their Services (MAES) ecosystem typology, as this was enhanced within SWOS, was applied. Results demonstrate the effectiveness of the employed classification model, techniques and rules, in obtaining highly accurate (over 90%) mapping results on the spatial extent of water-related ecosystems. Also, they highlight the contribution of Earth Observation (EO) and geospatial analysis in assessments of area-based changes and their causes, as well as in identification of conservation and management priorities (i.e. areas for restoration). In addition, to address the need to strengthen national capacities, the established SWOS service lines have been used as a contribution to the user community.

关键词： Wetland ecosystem extent object based image analysis Sentinel 2MSI Landsat 5TM SDG 6.6.1 indicator Ramsar convention MAES nomenclature

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Heuristic sample learning for complex urban scenes: Application to urban functional-zone mapping with VHR images and POI data

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2020年第0期161卷 1-12页

作者： Zhang, Xiuyuan Du, Shihong Zheng, Zhijia Peking Univ Inst Remote Sensing & GIS 5 Yiheyuan Rd Beijing 100871 Peoples R China McGill Univ Dept Nat Resource Sci 111 Lakeshore Rd Sainte Anne De Bellevue PQ H9X 3V9 Canada

Urban functional zones are basic units of urban planning and resource allocation, and contribute to a wide range of urban studies and investigations. Existing studies on functional-zone mapping with very-high-resolution (VHR) satellite images focused much on feature representations and classification techniques, but ignored zone sampling which however was fundamental to automatic zone classifications. Functional-zone sampling is much complicated and can hardly be resolved by classical sampling methods, as functional zones are complex urban scenes which consist of heterogeneous land covers and have highly abstract categories. To resolve the issue, this study presents a novel sampling paradigm, i.e., heuristic sample learning (HSL). It first proposes a sparse topic model to select representative functional zones, then uses deep forest to select confusing zones, and finally embraces Chinese restaurant process to label these selected zones. The presented method collects both representative and confusing zone samples and identifies their categories accurately, which makes the functionalzone classification process robust and the classification results accurate. Experiments conducted in Beijing indicate that HSL is effective and efficient for functional-zone sampling and classifications. Compared to traditional manual sampling, HSL reduces the time cost by 55% and improves the classification accuracy by 11.3% on average;furthermore, HSL can reduce the variation in sampling and classification results caused by different proficiency of operators. Accordingly, HSL significantly contributes to functional-zone mapping and plays an important role in urban studies.

关键词： Urban functional zones image classification Sample selection Heuristic sample learning object based image analysis

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Using orthoimages generated from oblique terrestrial photography to estimate and monitor vegetation cover

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ECOLOGICAL INDICATORS 2019年 101卷 91-101页

作者： Wallace, Luke Saldias, Daisy S. Reinke, Karin Hillman, Samuel Hally, Bryan Jones, Simon RMIT Univ Sch Sci Melbourne Vic Australia Bushfire & Nat Hazards Cooperat Res Ctr Melbourne Vic Australia

Percent vegetation cover is important variable used in understanding ecosystem processes, vegetation health and productivity. Downward looking images captured using a handheld camera have been demonstrated as a viable option for rapidly capturing in situ information to assess vegetation cover. This technique, however, is prone to perspective distortions biasing cover estimates towards taller vegetation elements. In this paper we present a new approach to generate imagery for use in vegetation cover estimation utilising multiple overlapping photographs and structure from motion algorithms to produce a 3D point cloud representation of the target plot. This point cloud is then converted into an orthoimage consisting of four bands-red, green, blue and vegetation height-which is free from perspective distortions. The approach is trialled in two Eucalypt forests in South Eastern Australia to produce an estimate of change in cover of all vegetation elements following a prescribed burn. Orthoimages are generated with 2.5 mm resolution and classified using object-based image analysis and random forests into broad vegetation and fuel classes. Utilising this approach an overall classification accuracy of 81% is achieved with the resulting estimates of cover agreeing with visual point based interpretation to within 6% across all classes.

关键词： Percent vegetation cover Orthoimage Structure from motion object based image analysis Prescribed burning

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imagens de drone e Índice de Vegetação por Diferença Normalizada (NDVI) para classificação segmentada em Áreas de Preservação Permanente (APP)

Imagens de drone e Índice de Vegetação por Diferença Nor...

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作者： Tagliarini, Felipe de Souza Nogueira Universidade Estadual Paulista

O advento dos drones nas geotecnologias possibilitou uma plataforma atuante em diferentes áreas para o mapeamento com elevada precisão e resolução, como no caso da análise ambiental, onde a correta identificação do uso do solo é de fundamental importância. O objetivo deste estudo consistiu na identificação do uso e ocupação do solo em Áreas de Preservação Permanente (APP) hídricas do rio Lavapés, em trecho da Fazenda Experimental Lageado, no município de Botucatu- SP. Foi utilizada a Análise de imagem Baseada em Objeto (OBIA), bem como o Índice de Vegetação por Diferença Normalizada (NDVI), e técnicas de aerofotogrametria e fotointerpretação com imagens de alta resolução coletadas por drone. As imagens foram obtidas por meio dos sensores MAPIR Survey3W RGB e Survey3W NIR/InfraRED, embarcados em drone multirrotor 3DR SOLO. Para construção dos ortomosaicos RGB e NDVI, as imagens foram processadas no software Pix4Dmapper 3.0. A classificação OBIA RGB obteve acurácia temática por índice Kappa de 70,69 %, com qualidade de classificação "Muito boa" e concordância substantiva com o referencial da VT (Verdade Terrestre). Para a classificação OBIA NDVI, a acurácia temática foi de 73,60 % por índice Kappa, com qualidade de classificação "Muito boa" e concordância substantiva com a VT. A comparação das duas classificações, com a OBIA NDVI como referencial devido a maior acurácia e das propriedades espectrais do NDVI, obteve índice Kappa de 83,16 %, com qualidade de classificação "Excelente" e concordância quase perfeita com o referencial, demonstrando a correlação existente entre uma classificação visual (OBIA RGB) e uma baseada em índice de vegetação (OBIA NDVI). A classe “vegetação arbórea”, obteve os menores erros de omissão e comissão nas duas classificações e na comparação entre ambas, garantindo uma grande confiança de classificação. Já a classe “sombras”, obteve os maiores erros de omissão e comissão, devido a maior confusão gerada por pixels de outras classes, pela amb

关键词： Sensoriamento remoto Veículo Aéreo Não Tripulado Índice radiométrico Análise de imagem Baseada em Objeto Remote sensing Unmanned Aerial Vehicle Radiometric index object based image analysis Tese de doutorado

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object-based Multi-Temporal and Multi-Source Land Cover Mapping Leveraging Hierarchical Class Relationships

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REMOTE SENSING 2020年第17期12卷 2814-2814页

作者： Gbodjo, Yawogan Jean Eudes Ienco, Dino Leroux, Louise Interdonato, Roberto Gaetano, Raffaele Ndao, Babacar Univ Montpellier UMR TETIS INRAE F-34000 Montpellier France Univ Montpellier LIRMM F-34095 Montpellier France UPR AIDA Cirad Dakar Senegal Univ Montpellier Cirad AIDA F-34398 Montpellier France UMR TETIS Cirad F-34000 Montpellier France Ctr Suivi Ecol Dakar 15532 Senegal Univ Cheikh Anta Diop Dakar 15532 Senegal

European satellite missions Sentinel-1 (S1) and Sentinel-2 (S2) provide at high spatial resolution and high revisit time, respectively, radar and optical images that support a wide range of Earth surface monitoring tasks, such as Land Use/Land Cover mapping. A long-standing challenge in the remote sensing community is about how to efficiently exploit multiple sources of information and leverage their complementarity, in order to obtain the most out of radar and optical data. In this work, we propose to deal with land cover mapping in an object-based image analysis (OBIA) setting via a deep learning framework designed to leverage the multi-source complementarity provided by radar and optical satellite image time series (SITS). The proposed architecture is based on an extension of Recurrent Neural Network (RNN) enriched via a modified attention mechanism capable to fit the specificity of SITS data. Our framework also integrates a pretraining strategy that allows to exploit specific domain knowledge, shaped as hierarchy over the set of land cover classes, to guide the model training. Thorough experimental evaluations, involving several competitive approaches were conducted on two study sites, namely the Reunion island and a part of the Senegalese groundnut basin. Classification results, 79% of global accuracy on the Reunion island and 90% on the Senegalese site, respectively, have demonstrated the suitability of the proposal.

关键词： land cover classification multi-source remote sensing satellite image time series object based image analysis deep learning neural networks pretraining

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