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Mapping Fractional Vegetation Cover Using Unoccupied Aerial Vehicle imagery to Guide Conservation of a Rare Riparian Shrub Ecosystem in Southern California

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REMOTE SENSING 2023年第21期15卷 5113页

作者： Rose, Miranda Brooke Mills, Mystyn Franklin, Janet Larios, Loralee San Diego State Univ Dept Geog San Diego CA 92182 USA Univ Calif Riverside Dept Bot & Plant Sci Riverside CA 92521 USA Calif State Univ Dept Geog Long Beach CA 90840 USA

The use of unoccupied aerial vehicles (UAVs) for vegetation monitoring is widespread in agriculture and forestry but far less so in ecological restoration where it has tremendous unrealized potential. We tested the ability of multispectral data and a derived vegetation index to classify shrub, herbaceous vegetation, and bare soil cover in a rare alluvial floodplain vegetation community in semiarid Southern California, where shrub cover is manipulated in restoration efforts aimed to provide open habitats required by several threatened and endangered species. Three classifiers and three levels of spatial aggregation were compared for their ability to provide accurate shrub cover estimates at a scale commensurate with the needs of conservation managers. We used object-based image analysis (OBIA) and compared maximum likelihood (ML), support vector machine (SVM), and random forest (RF) classifiers applied to high-spatial resolution (0.14 m) data from a four-band Parrot Sequoia+ multispectral sensor. The SVM and RF classifiers yielded similarly high classification accuracy evaluated using the training data (overall accuracy of 96.4% and 97.6%, respectively), much higher than ML (88%). Aggregating shrub cover data to 25 and 50 m resolutions yielded more accurate and well-calibrated cover estimates (mean absolute error 12% and 11%, respectively, for RF) than 10 m aggregation (MAE 19% for RF). Shrub cover estimated using RF and SVM was able to meet the restoration monitoring needs to distinguish the three phases of shrub habitat characterized by their cover (10-30%, 30-75%, >75%) that differ in habitat quality and restoration prescriptions.

关键词： vegetation mapping Mediterranean shrubland object-based image classification UAV restoration conservation remote sensing

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A multi-level context-guided classification method with object-based convolutional neural network for land cover classification using very high resolution remote sensing images

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2020年第0期88卷 102086-000页

作者： Zhang, Chenxiao Yue, Peng Tapete, Deodato Shangguan, Boyi Wang, Mi Wu, Zhaoyan Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Remote Sensing LIESMARS 129 Luoyu Rd Wuhan 430079 Hubei Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn 129 Luoyu Rd Wuhan 430079 Hubei Peoples R China Wuhan Univ HPECIG 129 Luoyu Rd Wuhan 430079 Hubei Peoples R China Collaborat Innovat Ctr Geospatial Technol 129 Luoyu Rd Wuhan 430079 Hubei Peoples R China Italian Space Agcy ASI Via Politecn Snc I-00133 Rome Italy

classification of very high resolution imagery (VHRI) is challenging due to the difficulty in mining complex spatial and spectral patterns from rich image details. Various object-based Convolutional Neural Networks (OCNN) for VHRI classification have been proposed to overcome the drawbacks of the redundant pixel-wise CNNs, owing to their low computational cost and fine contour-preserving. However, classification performance of OCNN is still limited by geometric distortions, insufficient feature representation, and lack of contextual guidance. In this paper, an innovative multi-level context-guided classification method with the OCNN (MLCG-OCNN) is proposed. A feature-fusing OCNN, including the object contour-preserving mask strategy with the supplement of object deformation coefficient, is developed for accurate object discrimination by learning simultaneously high-level features from independent spectral patterns, geometric characteristics, and object-level contextual information. Then pixel-level contextual guidance is used to further improve the per-object classification results. The MLCG-OCNN method is intentionally tested on two validated small image datasets with limited training samples, to assess the performance in applications of land cover classification where a trade-off between time-consumption of sample training and overall accuracy needs to be found, as it is very common in the practice. Compared with traditional benchmark methods including the patch-based per-pixel CNN (PBPP), the patch-based per-object CNN (PBPO), the pixel-wise CNN with object segmentation refinement (PO), semantic segmentation U-Net (U-NET), and DeepLabV3+(DLV3+), MLCG-OCNN method achieves remarkable classification performance (> 80 %). Compared with the state-of-the-art architecture DeepLabV3+, the MLCG-OCNN method demonstrates high computational efficiency for VHRI classification (4-5 times faster).

关键词： VHR image object-based image classification Remote sensing classification Convolutional neural network Deep learning

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object-based classification OF SENTINEL-2 imageRY USING COMPACT TEXTURE UNIT DESCRIPTORS THROUGH GOOGLE EARTH ENGINE

OBJECT-BASED CLASSIFICATION OF SENTINEL-2 IMAGERY USING COMP...

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Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS)

作者： Djerriri, Khelifa Safia, Abdelmounaime Adjoudj, Reda Agence Spatiale Algerienne Ctr Tech Spatiales Arzew Algeria Univ Djillali Liabes Sidi Bel Abbes Dept Comp Sci Sidi Bel Abbes Algeria Sherbrooke Univ Ctr Res & Applicat Remote Sensing CARTEL Dept Geomat Sherbrooke PQ Canada

ISBN: (纸本)9781728121901

This study investigates the possibilities of improving the classification of high spatial resolution images by using object-based approach, superpixel segmentation and compact texture unit descriptors. The proposed approach was implemented on Google Earth Engine (GEE) which provides a fast and easy-to-use platform with its freely available datasets and geospatial analysis tools for applications such multi-class classification. In this work, Multispectral Instrument (MSI) images of Sentinel-2 were utilized to classify main land-cover and land-use types. The obtained results were validated using the Corine land cover inventory.

关键词： object-based image classification Superpixel Segmentation Compact Texture Unit Descriptors Google Earth Engine

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A recurrent curve matching classification method integrating within-object spectral variability and between-object spatial association

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2021年 101卷 102367-102367页

作者： Tang, Yunwei Qiu, Fang Jing, Linhai Shi, Fan Li, Xiao Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci 9 South Rd Beijing 100094 Peoples R China Univ Texas Dallas Geospatial Informat Sci 800 West Campbell Rd Richardson TX 75080 USA Henan Univ Technol Coll Informat Sci & Engn 100 Lianhua St Zhengzhou 450001 Henan Peoples R China

object-based image analysis (OBIA), which has been commonly used for land cover and land use classification, may encounter challenges when satellite images' spatial resolution achieves at the sub-meter level. An image object may exhibit spectral heterogeneity, causing traditional object-level statistical measures such as mean values of the pixels in an object not suited to represent the feature of the object. Additionally, an image object may have strong spatial association with its surroundings. Traditional OBIA only considers spatial features of individual object, but ignoring spatial arrangement or spatial association between objects. This paper proposes a new OBIA method by integrating within-object spectral variability and between-object spatial association. The within-object spectral variability is captured by the histograms of the pixels in an object across multispectral bands to reflect the heterogeneity of their pixel values. based on this, the initial classification result is obtained using non-parametric curve matching methods. Then, the between-object spatial association is represented by curves derived from the frequency of pairwise classes in four main directions, also in the form of curves. The curves now composed of both the histograms of spectral feature and the class pair frequency of spatial feature are then fused for another curve matching based classification. This recurrent process is repeated and the spatial association is recaptured from the previous classification result at each iteration until a stopping criterion is satisfied. The curve matching classification method based on histograms of spectral feature is superior to traditional OBIA based on only object-level statistical measures since it fully characterizes spectral variability in the objects. The between-object spatial association works as a spatial filter that considers spatial arrangement of classes in a neighborhood. The developed method is especially suitable for classifying hig

关键词： object-based image classification Recurrent curve matching method Spatial association High spatial resolution

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Scale-sets image classification with hierarchical sample enriching and automatic scale selection

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2021年 105卷 102605-102605页

作者： Hu, Zhongwen Shi, Tiezhu Wang, Chisheng Li, Qingquan Wu, Guofeng Shenzhen Univ MNR Key Lab Geoenvironm Monitoring Great Bay Area Shenzhen 518060 Peoples R China Shenzhen Univ Guangdong Key Lab Urban Informat & Guangdong Hong Shenzhen 518060 Peoples R China Shenzhen Univ Shenzhen Key Lab Spatial Smart Sensing & Serv Shenzhen 518060 Peoples R China Shenzhen Univ Sch Architecture & Urban Planning Shenzhen 518060 Peoples R China

object-based image analysis (OBIA) has been widely used for classifying high-spatial-resolution images, and the selection of scale parameter(s) is inevitable in previous OBIA tasks. However, selecting appropriate scale(s) for an application inherently depends on the objective of the application, which cannot be robustly solved at the segmentation stage. In this study, a novel framework without any scale parameter was proposed for object-based image classification. It consists of four major steps: (1) Scale-sets image representation: Multiscale segments from a region-merging algorithm are organized using a scale-sets structure, where each segment is represented as a node of the hierarchy. Note that the implementation of this step does not need any scale parameter. (2) Multiscale sample selection and enriching: Multiscale segmentation results are retrieved from the scale-sets structure and visualized, and then training samples are selected from multiple scales. The training samples are further enriched according to hierarchical relations. (3) Feature extraction and multiscale classification: Segments are described using spectral, textural and geometric features, and then classified using a Random Forest classifier. Through this step, all segments are labeled with a class label, and a classified scale-sets structure is obtained. (4) Automatic optimal classification map selection: A multiscale accuracy assessment is conducted to evaluate the performances at different scales, and the optimal classification map is selected. A QuickBird image and a Gaofen2 image were used to demonstrate the effectiveness and advantages of the proposed approach. The experimental results demonstrated that the optimal scale of an object-based image classification work is a range, not a single value. Moreover, the multiscale accuracies obtained using training samples and ground truth maps showed the same tendency. Therefore, it is possible to automatically estimate the optimal classification s

关键词： object-based image classification Scale-sets structure Multiscale analysis Scale parameter estimation Sample enriching Land use land cover

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The application of drones for mosquito larval habitat identification in rural environments: a practical approach for malaria control?

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MALARIA JOURNAL 2021年第1期20卷 244-244页

作者： Stanton, Michelle C. Kalonde, Patrick Zembere, Kennedy Spaans, Remy Hoek Jones, Christopher M. Univ Liverpool Liverpool Sch Trop Med Vector Biol Dept Liverpool Merseyside England Univ Lancaster Lancaster Med Sch Lancaster England Malawi Liyerpool Wellcome Trust Clin Res Programm Blantyre Malawi

BackgroundSpatio-temporal trends in mosquito-borne diseases are driven by the locations and seasonality of larval habitat. One method of disease control is to decrease the mosquito population by modifying larval habitat, known as larval source management (LSM). In malaria control, LSM is currently considered impractical in rural areas due to perceived difficulties in identifying target areas. High resolution drone mapping is being considered as a practical solution to address this barrier. In this paper, the authors' experiences of drone-led larval habitat identification in Malawi were used to assess the feasibility of this *** mapping and larval surveys were conducted in Kasungu district, Malawi between 2018 and 2020. Water bodies and aquatic vegetation were identified in the imagery using manual methods and geographical object-based image analysis (GeoOBIA) and the performances of the classifications were compared. Further, observations were documented on the practical aspects of capturing drone imagery for informing malaria control including cost, time, computing, and skills requirements. Larval sampling sites were characterized by biotic factors visible in drone imagery and generalized linear mixed models were used to determine their association with larval *** covering an area of 8.9 km(2) across eight sites was captured. Larval habitat characteristics were successfully identified using GeoOBIA on images captured by a standard camera (median accuracy=98%) with no notable improvement observed after incorporating data from a near-infrared sensor. This approach however required greater processing time and technical skills compared to manual identification. Larval samples captured from 326 sites confirmed that drone-captured characteristics, including aquatic vegetation presence and type, were significantly associated with larval *** study demonstrates the potential for drone-acquired imagery to support mos

关键词： Drones Machine-learning object-based image classification Mosquito Anopheles Malaria Larval habitat Mapping

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US EPA EnviroAtlas Meter-Scale Urban Land Cover (MULC): 1-m Pixel Land Cover Class Definitions and Guidance

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REMOTE SENSING 2020年第12期12卷 1909-1909页

作者： Pilant, Andrew Endres, Keith Rosenbaum, Daniel Gundersen, Gillian US EPA Ffice Res & Dev MD243-05 Res Triangle Pk NC 27711 USA Oak Ridge Inst Sci & Educ POB 117 Oak Ridge TN 37831 USA Oak Ridge Associated Univ Inc POB 117 Oak Ridge TN 37831 USA

This article defines the land cover classes used in Meter-Scale Urban Land Cover (MULC), a unique, high resolution (one meter(2)per pixel) land cover dataset developed for 30 US communities for the United States Environmental Protection Agency (US EPA) EnviroAtlas. MULC data categorize the landscape into these land cover classes: impervious surface, tree, grass-herbaceous, shrub, soil-barren, water, wetland and agriculture. MULC data are used to calculate approximately 100 EnviroAtlas metrics that serve as indicators of nature's benefits (ecosystem goods and services). MULC, a dataset for which development is ongoing, is produced by multiple classification methods using aerial photo and LiDAR datasets. The mean overall fuzzy accuracy across the EnviroAtlas communities is 88% and mean Kappa coefficient is 0.84. MULC is available in EnviroAtlas via web browser, web map service (WMS) in the user's geographic information system (GIS), and as downloadable data at EPA Environmental Data Gateway. Fact sheets and metadata for each MULC community are available through EnviroAtlas. Some MULC applications include mapping green and grey infrastructure, connecting land cover with socioeconomic/demographic variables, street tree planting, urban heat island analysis, mosquito habitat risk mapping and bikeway planning. This article provides practical guidance for using MULC effectively and developing similar high resolution (HR) land cover data.

关键词： high spatial resolution land cover data remote sensing EnviroAtlas ecosystem services decision support image classification machine learning object-based image classification rule-based image classification pixel-based image classification GIS 1 m pixel

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Weakly Supervised Learning for Land Cover Mapping of Satellite image Time Series via Attention-based CNN

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IEEE ACCESS 2020年 8卷 179547-179560页

作者： Ienco, Dino Gbodjo, Yawogan Jean Eudes Gaetano, Raffaele Interdonato, Roberto Univ Montpellier UMR TETIS INRAE F-34000 Montpellier France CIRAD UMR TETIS F-34000 Montpellier France

The unprecedented possibility to acquire high resolution Satellite image Time Series (SITS) data is opening new opportunities to monitor the different aspects of the Earth Surface but, at the same time, it is raising up new challenges in term of suitable methods to analyze and exploit such huge amount of rich image data. One of the main tasks associated to SITS data analysis is related to land cover mapping. Due to operational constraints, the collected label information is often limited in volume and obtained at coarse granularity level carrying out inexact and weak knowledge that can affect the whole process. To cope with such issues, in the context of object-based SITS land cover mapping, we propose a new deep learning framework, named TASSEL (aTtentive weAkly Supervised Satellite image time sEries cLassifier), to deal with the weak supervision provided by the coarse granularity labels. Our framework exploits the multifaceted information conveyed by the object-based representation considering object components instead of aggregated object statistics. Furthermore, our framework also produces an additional outcome that supports the model interpretability. Quantitative and qualitative experimental evaluations are carried out on two real-world scenarios. Results indicate that not only TASSEL outperforms the competing approaches in terms of predictive performances, but it also produces valuable extra information that can be practically exploited to interpret model decisions.

关键词： Satellites Remote sensing Task analysis Supervised learning Machine learning Time series analysis Predictive models Weakly supervised learning object-based image classification satellite image time series land cover classification deep learning

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object-based classification Of Sentinel-2 imagery Using Compact Texture Unit Descriptors Through Google Earth Engine

Object-Based Classification Of Sentinel-2 Imagery Using Comp...

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Geoscience and Remote Sensing Symposium (M2GARSS), Mediterranean and Middle-East

作者： Khelifa Djerriri Abdelmounaime Safia Reda Adjoudj Agence Spatiale Algérienne Centre des Techniques Spatiales Arzew Algeria Centre for Research and Applications in Remote Sensing (CARTEL) Sherbrooke University Canada Department of Computer Sciences Djillali Liabes University Sidi Bel Abbes Algeria

ISBN: (数字)9781728121901

ISBN: (纸本)9781728121918

关键词： object-based image classification Superpixel Segmentation Compact Texture Unit Descriptors Google Earth Engine object orientation Google High resolution imaging images Descriptors Subject Headings topsoil Earth Engines Taxonomy Phyla Probability Compact Compact Pattern texture

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Using convolutional neural network to identify irregular segmentation objects from very high-resolution remote sensing imagery

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JOURNAL OF APPLIED REMOTE SENSING 2018年第2期12卷 025010-1-025010-21页

作者： Fu, Tengyu Ma, Lei Li, Manchun Johnson, Brian A. Nanjing Univ Jiangsu Prov Key Lab Geog Informat Sci & Technol Nanjing Jiangsu Peoples R China Nanjing Univ Sch Geog & Oceanog Sci Nanjing Jiangsu Peoples R China Inst Global Environm Strategies Nat Resources & Ecosyst Serv Area Hayama Kanagawa Japan

Convolutional neural network (CNN) has shown great success in computer vision tasks, but their application in land-use type classifications within the context of object-based image analysis has been rarely explored, especially in terms of the identification of irregular segmentation objects. Thus, a blocks-based object-based image classification (BOBIC) method was proposed to carry out end-to-end classification for segmentation objects using CNN. Specifically, BOBIC takes advantage of CNN to automatically extract complex features from the original image data, thereby avoiding the uncertainty caused by the manual extraction of features in OBIC. Additionally, OBIC compensates for the shortcomings of CNN whereby it is difficult to delineate a clear right boundary for ground objects at the pixel level. Using three high-resolution test images, the proposed BOBIC was compared with support vector machine (SVM) and random forest (RF) classifiers, and then, the effect of image blocks and mixed objects on classification accuracy was evaluated for the proposed BOBIC. Compared with conventional SVM and RF classifiers, the inclusion of CNN improved the OBIC classification performance substantially (5% to 10% increases in overall accuracy), and it also alleviated the effect derived from mixed objects. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.

关键词： object-based image classification convolutional neural network multiresolution segmentation irregular segmented object image block

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