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CognitionMaster: an object-based image analysis framework

DIAGNOSTIC PATHOLOGY 2013年第1期8卷 34-34页

作者： Wienert, Stephan Heim, Daniel Kotani, Manato Lindequist, Bjoern Stenzinger, Albrecht Ishii, Masaru Hufnagl, Peter Beil, Michael Dietel, Manfred Denkert, Carsten Klauschen, Frederick Charite Inst Pathol D-10117 Berlin Germany VMscope GmbH D-10117 Berlin Germany Osaka Univ Lab Cellular Dynam WPI IFReC Suita Osaka 5650871 Japan Japan Sci & Technol Agcy JST CREST Chiyoda Ku Tokyo 1020075 Japan Univ Appl Sci Berlin D-12459 Berlin Germany Univ Heidelberg Hosp Inst Pathol D-69120 Heidelberg Germany Univ Ulm Dept Med 1 D-89081 Ulm Germany

Background: Automated image analysis methods are becoming more and more important to extract and quantify image features in microscopy-based biomedical studies and several commercial or open-source tools are available. However, most of the approaches rely on pixel-wise operations, a concept that has limitations when high-level object features and relationships between objects are studied and if user-interactivity on the object-level is desired. Results: In this paper we present an open-source software that facilitates the analysis of content features and object relationships by using objects as basic processing unit instead of individual pixels. Our approach enables also users without programming knowledge to compose "analysis pipelines" that exploit the object-level approach. We demonstrate the design and use of example pipelines for the immunohistochemistry-based cell proliferation quantification in breast cancer and two-photon fluorescence microscopy data about bone-osteoclast interaction, which underline the advantages of the object-based concept. Conclusions: We introduce an open source software system that offers object-based image analysis. The object-based concept allows for a straight-forward development of object-related interactive or fully automated image analysis solutions. The presented software may therefore serve as a basis for various applications in the field of digital image analysis.

关键词： Software Open source image analysis object-based image analysis

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object-based hyperspectral image classification using a new latent block model based on hidden Markov random fields

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PATTERN analysis AND APPLICATIONS 2022年第2期25卷 467-481页

作者： Fatemighomi, Hamideh Sadat Golalizadeh, Mousa Amani, Meisam Tarbiat Modares Univ Dept Stat Tehran Iran Wood Environm & Infrastruct Solut Ottawa ON Canada

Efficient high-dimensional analyses of hyperspectral datasets and their utilization within classification algorithms is a popular topic in the field of data analytics. A powerful tool for summarizing a large array of datasets is the latent block model (LBM), which finds homogeneous blocks within the data using the finite mixture model (FMM). In this study, for the first time, LBM was modified by replacing the hidden Markov random field (HMRF) instead of using FMM to consider the spatial relationship between pixels and, thus, to develop a new object-based classification algorithm. The proposed clustering algorithm was named LBMHMRF and was used along with the support vector machine (SVM) algorithm to classify land cover/land use (LCLU) categories using two hyperspectral datasets. Unlike LBM, HMRF, and MultiHMRF, the LBMHMRF algorithm allows for the use of more spectral information without estimating a large number of parameters and produces a model with high computation costs saving feature. Additionally, the segmentation results are produced in a shorter period of time compared to the above-mentioned algorithms. It was observed that the proposed object-based classification algorithm (i.e., LBMHMRF + SVM) had the highest potential in terms of visual and statistical accuracies as well as computation time compared to the pixel-based SVM, object-based HMRF + SVM, and MultiHMRF + SVM. The average overall classification accuracies considering the different datasets and cases investigated in this study were 93.1%, 94.6%, 95.7%, and 96.4% for SVM, HMRF + SVM, MultiHMRF + SVM, and LBMHMRF + SVM, respectively.

关键词： Hidden Markov random field Latent block model Clustering Segmentation object-based image analysis Support vector machine

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object-based machine learning approach for soybean mapping using temporal sentinel-1/sentinel-2 data

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GEOCARTO INTERNATIONAL 2022年第23期37卷 6848-6866页

作者： Kumari, Mamta Pandey, Varun Choudhary, Karun Kumar Murthy, C. S. Indian Space Res Org Agr Sci & Applicat Grp Natl Remote Sensing Ctr Hyderabad Telangana India

Soybean mapping in Indian context is challenging owing to its short growing period coinciding with the monsoon clouds, inter-cropping and smallholders' land. This study proposes an approach for mapping soybean by integrating object-based image analysis with machine learning (ML) based classification using temporal Sentinel-1 SAR (S-1) and Sentinel-2 optical (S-2) data. Field objects were delineated with scale-optimized multi-resolution segmentation using historical S-2 data. object-based temporal VH-backscatter and NDVI were extracted for training, validation and testing of the three ML models. Validation results showed the outperformance of Extreme gradient boosting (OB-XGBoost) over Random Forest (OB-RF) and Support vector machine (OB-SVM) with an overall accuracy (OA) of 92.50, 91.08 and 90.1, respectively. Testing of OB-XGBoost model resulted in OA, kappa statistics, and F-score (soybean) of 86.12%, 0.82, and 87.23%, respectively. The soybean map produced by the proposed methodology has shown better representation in terms of homogeneity and uniformity than the pixel-based classification.

关键词： Soybean Extreme gradient boosting Random forest SVM object-based image analysis SAR India

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Quantifying Vegetation and Landscape Metrics with Hyperspatial Unmanned Aircraft System imagery in a Coastal Oligohaline Marsh

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ESTUARIES AND COASTS 2022年第4期45卷 1058-1069页

作者： Broussard, Whitney P., III Visser, Jenneke M. Brooks, Robert P. Univ Louisiana Lafayette Inst Coastal & Water Res Lafayette LA 70504 USA JESCO Inc Jennings LA USA Univ Louisiana Lafayette Sch Geosci Lafayette LA 70504 USA Penn State Univ Dept Geog Riparia University Pk PA 16802 USA

Billions of dollars are projected to be spent on restoration projects along the northern Gulf Coast which will require efficient monitoring at both landscape and project-specific scales. Recent developments in unmanned aircraft systems (UAS) have sparked interest in the ability of these "drones" to capture hyperspatial imagery (pixel resolution < 10 cm) that resolves individual species and produces accurate data for monitoring programs in coastal landscapes. We present a case study conducted at Coastwide Reference Monitoring System (CRMS) station 0392, aSpartina patens-dominated, oligohaline coastal marsh in Terrebonne Parish, Louisiana. Results demonstrate the ability of UAS technology to collect hyperspatial, multispectral aerial images in a coastal wetland, and to produce very-high-resolution orthomosaics and digital elevation models. We then used object-based image analysis (OBIA) techniques to (1) delineate the land-water interface, (2) classify composition by dominant species, and (3) quantify average plant height by species. Model results were validated with traditional on-the-ground CRMS vegetation surveys. Results suggest that OBIA methods can overcome the spectral variability of hyperspatial datasets, quantify uncertainties in conventional techniques, and provide improved estimates of wetland vegetation cover and species composition. These methods scale conventional plot-level coverage values to data-rich landscape-level models and provide useful tools to monitor restoration performance, landscape changes, and ecosystem services in coastal wetland systems.

关键词： Unmanned aircraft systems object-based image analysis Coastal wetlands Vegetation mapping Land-water interface Landscape patterns Ecosystem services

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A national fuel type mapping method improvement using sentinel-2 satellite data

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GEOCARTO INTERNATIONAL 2022年第4期37卷 1022-1042页

作者： Stefanidou, Alexandra Gitas, Ioannis Z. Katagis, Thomas Aristotle Univ Thessaloniki Fac Agr Forestry & Nat Environm Dept Forestry & Nat Environm Thessaloniki Greece

Despite the fact that wildland fires have always been an integral part of many ecosystems, their increased frequency and intensity have reinforced the need of fire managers for updated and highly accurate information associated with the spatial distribution of forest fuels. In 2015, a fuel type mapping method was developed in the framework of the "National Observatory of Forest Fires (NOFFi)" project resulting in the generation of a national fuel type map. In this study, we aimed at examining the potential of the newly available Sentinel-2 satellite images for the improvement of the NOFFi's mapping method in terms of accuracy and update effectiveness of the national fuel type map. Results demonstrate Sentinel-2 data will likely improve the resolution and reliability of national fuel type maps, increasing mapping efficiency for operational purposes.

关键词： Fuel types Sentinel-2 object-based image analysis

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Land-Cover Mapping in the Biogradska Gora National Park with Very-High-Resolution Pleiades images 24th

Land-Cover Mapping in the Biogradska Gora National Park with...

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24th Italian Conference of the Italian-Federation-of-Scientific-Associations-for-Territorial-and-Environmental-Information (ASITA)

作者： Cagliero, Eleonora Morresi, Donato Marchi, Niccolo Paradis, Laure Finsinger, Walter Garbarino, Matteo Lingua, Emanuele Univ Padua Dept Land Environm Agr & Forestry TESAF I-35020 Legnaro PD Italy Univ Montpellier EPHE CNRS ISEMIRD Montpellier France Univ Torino Dept Agr Forest & Food Sci DISAFA I-10095 Grugliasco TO Italy

ISBN: (纸本)9783030944261;9783030944254

Old-growth forests (OGFs) are extremely valuable relict ecosystems for studying natural disturbance dynamics. Small-scale disturbances caused by tree crown mortality of one or few individuals, i.e. gap dynamics, are the most frequent events occurring in OGFs. Understanding these processes requires information on the spatial arrangement of forest patches dominated by different tree species and forest canopy gaps at a fine spatial scale. Here, we aimed at mapping different land-cover classes including conifers, broad-leaved trees, and forest canopy gaps using two very-high-resolution satellite images, i.e. Pleiades images, in the mixed fir-spruce-beech OGF reserve of Biogradska Gora (Montenegro). Specifically, we coupled an object-based image analysis (OBIA) approach and a Random Forest classifier, trained with samples partly derived from field data. The adopted approach showed high accuracy for the main land-cover classes (conifers, broadleaved trees, grasslands, bare ground, and water), e.g. producer's and user's accuracy higher than 92% and 95%, respectively. Conversely, forest canopy gaps were classified with lower accuracy, e g minimum producer's and user's accuracies of 75% and 54%, respectively. Despite the exploitation of textural metrics during both image segmentation and classification, the lack of remote sensing data providing information on the vertical structure of the forest stand prevented us from accurately map forest canopy gaps.

关键词： Very-high-resolution satellite imagery Old-growth forest Land-cover map object-based image analysis Forest canopy gaps

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object-based image analysis and Data mining for Building Ontology of Informal Settlements

Object-based Image Analysis and Data mining for Building Ont...

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Conference on image and Signal Processing for Remote Sensing XVIII

作者： Khelifa, Djerriri Mimoun, Malki Ctr Spatial Tech Div Earth Observat Arzew 31200 Oran Algeria Univ Djillali Liabes Sidi Bel Abbes BEEDIS Lab Abbes 22000 Algeria

ISBN: (纸本)9780819492777

During recent decades, unplanned settlements have been appeared around the big cities in most developing countries and as consequence, numerous problems have emerged. Thus the identification of different kinds of settlements is a major concern and challenge for authorities of many countries. Very High Resolution (VHR) Remotely Sensed imagery has proved to be a very promising way to detect different kinds of settlements, especially through the using of new object-based image analysis (OBIA). The most important key is in understanding what characteristics make unplanned settlements differ from planned ones, where most experts characterize unplanned urban areas by small building sizes at high densities, no orderly road arrangement and Lack of green spaces. Knowledge about different kinds of settlements can be captured as a domain ontology that has the potential to organize knowledge in a formal, understandable and sharable way. In this work we focus on extracting knowledge from VHR images and expert's knowledge. We used an object based strategy by segmenting a VHR image taken over urban area into regions of homogenous pixels at adequate scale level and then computing spectral, spatial and textural attributes for each region to create objects. A genetic-based data mining was applied to generate high predictive and comprehensible classification rules based on selected samples from the OBIA result. Optimized intervals of relevant attributes are found, linked with land use types for forming classification rules. The unplanned areas were separated from the planned ones, through analyzing of the line segments detected from the input image. Finally a simple ontology was built based on the previous processing steps. The approach has been tested to VHR images of one of the biggest Algerian cities, that has grown considerably in recent decades.

关键词： Unplanned settlements VHR imagery object-based image analysis Data mining Ontology

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High-Throughput Plot-Level Quantitative Phenotyping Using Convolutional Neural Networks on Very High-Resolution Satellite images

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REMOTE SENSING 2024年第2期16卷 282-282页

作者： Victor, Brandon Nibali, Aiden Newman, Saul Justin Coram, Tristan Pinto, Francisco Reynolds, Matthew Furbank, Robert T. He, Zhen La Trobe Univ Dept Math & Comp Sci Melbourne Vic 3086 Australia Univ Oxford Leverhulme Ctr Demog Sci Oxford OX1 1JD England Australian Grain Technol AGT Roseworthy SA 5371 Australia Int Maize & Wheat Improvement Ctr CIMMYT Texcoco 56237 MX Mexico Wageningen Univ Ctr Crop Syst Anal Dept Plant Sci NL-6700 AK Wageningen Netherlands Australian Natl Univ Res Sch Biol Div Plant Sci Acton ACT 2601 Australia

To ensure global food security, crop breeders conduct extensive trials across various locations to discover new crop varieties that grow more robustly, have higher yields, and are resilient to local stress factors. These trials consist of thousands of plots, each containing a unique crop variety monitored at intervals during the growing season, requiring considerable manual effort. In this study, we combined satellite imagery and deep learning techniques to automatically collect plot-level phenotypes from plant breeding trials in South Australia and Sonora, Mexico. We implemented two novel methods, utilising state-of-the-art computer vision architectures, to predict plot-level phenotypes: flowering, canopy cover, greenness, height, biomass, and normalised difference vegetation index (NDVI). The first approach uses a classification model to predict for just the centred plot. The second approach predicts per-pixel and then aggregates predictions to determine a value per-plot. Using a modified ResNet18 model to predict the centred plot was found to be the most effective method. These results highlight the exciting potential for improving crop trials with remote sensing and machine learning.

关键词： agriculture deep learning object-based image analysis optical imagery plant breeding

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THREE-DECADE changes of reef cover in Pulau Layang-Layang, Malaysia using multitemporal Landsat images

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MARINE ENVIRONMENTAL RESEARCH 2024年 197卷 106454页

作者： Han, Daniella Hsu Tsyr James, Daniel Waheed, Zarinah Phua, Mui-How Univ Malaysia Sabah UMS Borneo Marine Res Inst Kota Kinabalu 88400 Sabah Malaysia UMS Fac Trop Forestry Kota Kinabalu 88400 Sabah Malaysia UMS Fac Sci & Nat Resources Small Isl Res Ctr Kota Kinabalu 88400 Sabah Malaysia

Over the years, coral reefs in the South China Sea have degraded and faced severe threats from rapid development, coral bleaching, and Crown -of -Thorns Starfish (COTS) outbreak. There is limited knowledge relating to the effects of anthropogenic disturbances and natural events on the coral reefs of Pulau Layang-Layang. This study aims to assess reef cover changes by utilizing Landsat satellite images spanning from 1989 to 2022. Using the object -based image analysis method, this study classified the reef cover into three categories: coral, rock and rubble, and sand. The supervised classification had an overall accuracy of 86.41 -87.38 % and Tau 's coefficients of 0.80 -0.81. The results showed island development and construction of artificial bird sanctuary have led to an increase in coral cover. Furthermore, it was illustrated that the impact of COTS outbreaks in 2010 and 2020 differed significantly, with the latter showing no signs of recovery. Our study underscores the importance of timely intervention to mitigate the spread of COTS. This study provides insights into the resilience and vulnerability of these ecosystems in the face of various stressors.

关键词： Spratly islands Reef cover Crown-of-thorns starfish Anthropogenic disturbance object-based image analysis

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OBSUM: An object-based spatial unmixing model for spatiotemporal fusion of remote sensing images

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REMOTE SENSING OF ENVIRONMENT 2024年 304卷

作者： Guo, Houcai Ye, Dingqi Xu, Hanzeyu Bruzzone, Lorenzo Univ Trento Dept Informat Engn & Comp Sci I-38123 Trento Italy Cent South Univ Sch Geosci & Info Phys Changsha 410083 Peoples R China Nanjing Normal Univ Sch Geog Nanjing 210023 Peoples R China

Spatiotemporal fusion aims to improve both the spatial and temporal resolution of remote sensing images, thus facilitating time-series analysis at a fine spatial scale. However, there are several important issues that limit the application of current spatiotemporal fusion methods. First, most spatiotemporal fusion methods are based on pixel-level computation, which neglects the valuable shape information of ground objects. Moreover, many existing methods cannot accurately retrieve strong temporal changes between the available high-resolution image at base date and the predicted one. This study proposes an object-based Spatial Unmixing Model (OBSUM), which incorporates object-based image analysis and spatial unmixing, to overcome the two abovementioned problems. OBSUM consists of one preprocessing step and three fusion steps, i.e., object-level unmixing, object-level residual compensation, and pixel-level residual compensation. The performance of OBSUM was compared with seven representative spatiotemporal fusion methods at two agricultural sites. The experimental results demonstrated that OBSUM outperformed other methods in terms of both accuracy indices and visual effects over time-series. Furthermore, OBSUM also achieved satisfactory results in crop progress monitoring and crop mapping. Therefore, it has great potential to generate accurate and high-resolution timeseries observations for supporting various remote sensing applications.

关键词： Spatiotemporal fusion object-based image analysis Sentinel-2 Sentinel-3 Time-series Remote sensing

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