Pixel and object-based classification approaches for mapping forest fuel types in Tenerife Island from ASTER data

2013 ◽  
Vol 22 (3) ◽  
pp. 306 ◽  
Author(s):  
Alfonso Alonso-Benito ◽  
Lara A. Arroyo ◽  
Manuel Arbelo ◽  
Pedro Hernández-Leal ◽  
Alejandro González-Calvo
Keyword(s):  
Support Vector Machine ◽  
Image Analysis ◽  
National Forest Inventory ◽  
Support Vector ◽  
Fire Behaviour ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Forest Fuel ◽  
Tenerife Island ◽  
Representative Area

Four classification algorithms have been assessed and compared with mapped forest fuel types from Terra-ASTER sensor images in a representative area of Tenerife Island (Canary Islands, Spain). A BEHAVE fuel-type map from 2002, together with field data also obtained in 2002 during the Third Spanish National Forest Inventory, was used as reference data. The BEHAVE fuel types of the reference dataset were first converted into the Fire Behaviour Fuel Types described by Scott and Burgan, taking into account the vegetation of the study area. Then, three pixel-based algorithms (Maximum Likelihood, Neural Network and Support Vector Machine) and an Object-Based Image Analysis were applied to classify the Scott and Burgan fire behaviour fuel types from an ASTER image from 3 March 2003. The performance of the algorithms tested was assessed and compared in terms of quantity disagreement and allocation disagreement. Within the pixel-based classifications, the best results were obtained from the Support Vector Machine algorithm, which showed an overall accuracy of 83%; 14% of disagreement was due to allocation and 3% to quantity disagreement. The Object-Based Image Analysis approach produced the most accurate maps, with an overall accuracy of 95%; 4% disagreement was due to allocation and 1% to quantity disagreement. The object-based classification achieved thus an overall accuracy of 12% above the best results obtained for the pixel-based algorithms tested. The incorporation of context information to the object-based classification allowed better identification of fuel types with similar spectral behaviour.

scholarly journals Comparison of GEOBIA classification algorithms based on Worldview-3 imagery in the extraction of coastal coniferous forest

2021 ◽  
Vol 145 (11-12) ◽  
pp. 535-544
Author(s):  
Lovre Panđa ◽  
Rina Milošević ◽  
Silvija Šiljeg ◽  
Fran Domazetović ◽  
Ivan Marić ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Image Analysis ◽  
Coniferous Forest ◽  
Kappa Coefficient ◽  
Random Trees ◽  
Support Vector ◽  
Classification Algorithms ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Geographic Object

Šume primorskih četinjača, sa svojom ekološkom, ekonomskom, estetskom i društvenom funkcijom, predstavljaju važan dio europskih šumskih zajednica. Osnovni cilj ovoga rada je usporediti najkorištenije GEOBIA (engl. Geographic Object-Based Image Analysis) klasifikacijske algoritme (engl. Random Trees – RT, Maximum Likelihood – ML, Support Vector Machine – SVM) s ciljem izdvajanja šuma primorskih četinjača na visoko-rezolucijskom WorldView-3 snimku unutar topografskog slijevnog područja naselja Split. Metodološki okvir istraživanja uključuje (1) izvođenje izoštrenog multispektralnog snimka (WV-3<sub>MS</sub>-a); (2) testiranje segmentacijskih korisničko-definiranih parametara; (3) dodavanje testnih uzoraka; (4) klasifikaciju segmentiranog modela; (5) procjenu točnosti klasifikacijskih algoritama, te (6) procjenu točnosti završnog modela. RT se prema korištenim pokazateljima (correctness – COR, completeness – COM i overall quality – OQ) pokazao kao najbolji algoritam. Iterativno postavljanje segmentacijskih parametara omogućilo je detekciju najprikladnijih vrijednosti za generiranje segmentacijskog modela. Utvrđeno je da sjene mogu uzrokovati značajne probleme ako se klasificiranje vrši na visoko-rezolucijskim snimkama. Modificiranim Cohen’s kappa coefficient (K) pokazateljem izračunata je točnost konačnog modela od 87,38%. WV-3<sub>MS</sub> se može smatrati kvalitetnim podatkom za detekciju šuma primorskih četinjača primjenom GEOBIA metode.

scholarly journals Geographic Object-Based Image Analysis for Automated Landslide Detection Using Open Source GIS Software

2019 ◽  
Vol 8 (12) ◽  
pp. 551 ◽  
Author(s):  
Raphael Knevels ◽  
Helene Petschko ◽  
Philip Leopold ◽  
Alexander Brenning
Keyword(s):  
Image Analysis ◽  
Open Source ◽  
Land Surface ◽  
Region Growing ◽  
Support Vector ◽  
Visual Interpretation ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Geographic Object ◽  
Gis Software

With the increased availability of high-resolution digital terrain models (HRDTM) generated using airborne light detection and ranging (LiDAR), new opportunities for improved mapping of geohazards such as landslides arise. While the visual interpretation of LiDAR, HRDTM hillshades is a widely used approach, the automatic detection of landslides is promising to significantly speed up the compilation of inventories. Previous studies on automatic landslide detection often used a combination of optical imagery and geomorphometric data, and were implemented in commercial software. The objective of this study was to investigate the potential of open source software for automated landslide detection solely based on HRDTM-derived data in a study area in Burgenland, Austria. We implemented a geographic object-based image analysis (GEOBIA) consisting of (1) the calculation of land-surface variables, textural features and shape metrics, (2) the automated optimization of segmentation scale parameters, (3) region-growing segmentation of the landscape, (4) the supervised classification of landslide parts (scarp and body) using support vector machines (SVM), and (5) an assessment of the overall classification performance using a landslide inventory. We used the free and open source data-analysis environment R and its coupled geographic information system (GIS) software for the analysis; our code is included in the Supplementary Materials. The developed approach achieved a good performance (κ = 0.42) in the identification of landslides.

scholarly journals USING PIXEL-BASED AND OBJECT-BASED METHODS TO CLASSIFY URBAN HYPERSPECTRAL FEATURES

2016 ◽  
Vol 42 (3) ◽  
pp. 92-105 ◽  
Author(s):  
Ahmad Hadavand ◽  
Mehdi Mokhtarzadeh ◽  
Mohammad Javad Valadan Zoej ◽  
Saeid Homayouni ◽  
Mohammad Saadatseresht
Keyword(s):  
Image Analysis ◽  
Feature Extraction ◽  
Classification Accuracy ◽  
Support Vector ◽  
Classification Methods ◽  
Spectral Angle Mapper ◽  
Object Based Image Analysis ◽  
Analysis Methods ◽  
Object Based ◽  
Percent Improvement

Object-based image analysis methods have been developed recently. They have since become a very active research topic in the remote sensing community. This is mainly because the researchers have begun to study the spatial structures within the data. In contrast, pixel-based methods only use the spectral content of data. To evaluate the applicability of object-based image analysis methods for land-cover information extraction from hyperspectral data, a comprehensive comparative analysis was performed. In this study, six supervised classification methods were selected from pixel-based category, including the maximum likelihood (ML), fisher linear likelihood (FLL), support vector machine (SVM), binary encoding (BE), spectral angle mapper (SAM) and spectral information divergence (SID). The classifiers were conducted on several features extracted from original spectral bands in order to avoid the problem of the Hughes phenomenon, and obtain a sufficient number of training samples. Three supervised and four unsupervised feature extraction methods were used. Pixel based classification was conducted in the first step of the proposed algorithm. The effective feature number (EFN) was then obtained. Image objects were thereafter created using the fractal net evolution approach (FNEA), the segmentation method implemented in eCognition software. Several experiments have been carried out to find the best segmentation parameters. The classification accuracy of these objects was compared with the accuracy of the pixel-based methods. In these experiments, the Pavia University Campus hyperspectral dataset was used. This dataset was collected by the ROSIS sensor over an urban area in Italy. The results reveal that when using any combination of feature extraction and classification methods, the performance of object-based methods was better than pixel-based ones. Furthermore the statistical analysis of results shows that on average, there is almost an 8 percent improvement in classification accuracy when we use the object-based methods.

scholarly journals OBJECT-BASED IMAGE ANALYSIS OF DIFFERENT SPATIAL RESOLUTION SATELLITE IMAGERIES IN URBAN AND SUBURBAN ENVIRONMENT

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 105-112
Author(s):  
I. Kotaridis ◽  
M. Lazaridou
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Land Cover ◽  
Spatial Resolution ◽  
Support Vector ◽  
Landsat 8 ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Very High Spatial Resolution ◽  
Sentinel 2A

Abstract. Monitoring urban and suburban land cover has become a particularly researched investigation field in remote sensing community, since there is a large amount of professionals interested in gathering useful information, regarding urban sprawl and its side effects in natural vegetation, urban parks and water bodies. This paper focuses on studying the implementation of an object-based image analysis methodological framework, in Orfeo ToolBox. Moderate, high and very high spatial resolution satellite images were utilized in order to generate thematic land cover maps of the study area located in Thessaloniki, Greece. Taking into consideration that there is not a relevant research in literature concerning the selection of segmentation parameters values, the optimal values are presented for MeanShift segmentation algorithm. Classifications were conducted with the use of Support Vector Machines algorithm and the final outputs are presented, accompanied by the evaluation of accuracy assessments which is a mandatory step in every remote sensing project. The analysis showed that OBIA, in this case, works well with Landsat-8 and QuickBird data and exceptionally well with Sentinel-2A data with over 90% overall accuracy. Critical considerations on the aforementioned are also included.

scholarly journals A Scalable, Supervised Classification of Seabed Sediment Waves Using an Object-Based Image Analysis Approach

2021 ◽  
Vol 13 (12) ◽  
pp. 2317
Author(s):  
Gerard Summers ◽  
Aaron Lim ◽  
Andrew J. Wheeler
Keyword(s):  
Image Analysis ◽  
Spatial Resolution ◽  
Support Vector ◽  
Multilayer Perceptrons ◽  
Seabed Sediment ◽  
Object Based Image Analysis ◽  
Bathymetric Data ◽  
Sediment Waves ◽  
Object Based

National mapping programs (e.g., INFOMAR and MAREANO) and global efforts (Seabed 2030) acquire large volumes of multibeam echosounder data to map large areas of the seafloor. Developing an objective, automated and repeatable approach to extract meaningful information from such vast quantities of data is now essential. Many automated or semi-automated approaches have been defined to achieve this goal. However, such efforts have resulted in classification schemes that are isolated or bespoke, and therefore it is necessary to form a standardised classification method. Sediment wave fields are the ideal platform for this as they maintain consistent morphologies across various spatial scales and influence the distribution of biological assemblages. Here, we apply an object-based image analysis (OBIA) workflow to multibeam bathymetry to compare the accuracy of four classifiers (two multilayer perceptrons, support vector machine, and voting ensemble) in identifying seabed sediment waves across three separate study sites. The classifiers are trained on high-spatial-resolution (0.5 m) multibeam bathymetric data from Cork Harbour, Ireland and are then applied to lower-spatial-resolution EMODnet data (25 m) from the Hemptons Turbot Bank SAC and offshore of County Wexford, Ireland. A stratified 10-fold cross-validation was enacted to assess overfitting to the sample data. Samples were taken from the lower-resolution sites and examined separately to determine the efficacy of classification. Results showed that the voting ensemble classifier achieved the most consistent accuracy scores across the high-resolution and low-resolution sites. This is the first object-based image analysis classification of bathymetric data able to cope with significant disparity in spatial resolution. Applications for this approach include benthic current speed assessments, a geomorphological classification framework for benthic biota, and a baseline for monitoring of marine protected areas.

scholarly journals Object-Based Image Analysis for Sago Palm Classification: The Most Important Features from High-Resolution Satellite Imagery

2018 ◽  
Vol 10 (8) ◽  
pp. 1319 ◽  
Author(s):  
Sarip Hidayat ◽  
Masayuki MATSUOKA ◽  
Sumbangan Baja ◽  
Dorothea Agnes Rampisela
Keyword(s):  
Image Analysis ◽  
Tree Species ◽  
Classification Accuracy ◽  
Local Governments ◽  
Support Vector ◽  
Textural Features ◽  
Palm Tree ◽  
Sago Palm ◽  
Object Based Image Analysis ◽  
Object Based

Sago palm (Metroxylon sagu) is a palm tree species originating in Indonesia. In the future, this starch-producing tree will play an important role in food security and biodiversity. Local governments have begun to emphasize the sustainable development of sago palm plantations; therefore, they require near-real-time geospatial information on palm stands. We developed a semi-automated classification scheme for mapping sago palm using machine learning within an object-based image analysis framework with Pleiades-1A imagery. In addition to spectral information, arithmetic, geometric, and textural features were employed to enhance the classification accuracy. Recursive feature elimination was applied to samples to rank the importance of 26 input features. A support vector machine (SVM) was used to perform classifications and resulted in the highest overall accuracy of 85.00% after inclusion of the eight most important features, including three spectral features, three arithmetic features, and two textural features. The SVM classifier showed normal fitting up to the eighth most important feature. According to the McNemar test results, using the top seven to 14 features provided a better classification accuracy. The significance of this research is the revelation of the most important features in recognizing sago palm among other similar tree species.

scholarly journals ASSESSMENT OF COMBINING CONVOLUTIONAL NEURAL NETWORKS AND OBJECT BASED IMAGE ANALYSIS TO LAND COVER CLASSIFICATION USING SENTINEL 2 SATELLITE IMAGERY (TENES REGION, ALGERIA)

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 383-389
Author(s):  
N. Zaabar ◽  
S. Niculescu ◽  
M. K. Mihoubi
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Land Cover ◽  
Image Classification ◽  
Support Vector ◽  
Object Based Image Analysis ◽  
Sensing Applications ◽  
Object Based ◽  
Land Cover Maps ◽  
Sentinel 2

Abstract. Land cover maps can provide valuable information for various applications, such as territorial monitoring, environmental protection, urban planning and climate change prevention. In this purpose, remote sensing based on image classification approaches undergoing a high revolution can be dedicated to land cover mapping tasks. Similarly, deep learning models are considerably applied in remote sensing applications; which can automatically learn features from large amounts of data. Prevalently, the Convolutional Neural Network (CNN), have been increasingly performed in image classification. The aim of this study is to apply a new approach to analyse land cover, and extract its features. Experiments carried out on a coastal town located in north-western Algeria (Ténès region). The study area is chosen because of its importance as a part of the national strategy to combat natural hazards, specifically floods. As well as, a simple CNN model with two hidden layers was constructed, combined with an Object-Based Image Analysis (OBIA). In this regard, a Sentinel-2 image was used, to perform the classification, using spectral index combinations. Furthermore, to compare the performance of the proposed approach, an OBIA based on machines learning algorithms mainly Random Forest (RF) and Support Vector Machine (SVM), was provided. Results of accuracy assessment of classification showed good values in terms of Overall accuracy and Kappa Index, which reach to 93.1% and 0.91, respectively. As a comparison, CNN-OBIA approach outperformed OBIA based on RF algorithm. Therefore, Final land cover maps can be used as a support tool in regional and national decisions.

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