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.

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.

PEMBUATAN PETA TUTUPAN LAHAN BERDASARKAN HASIL KLASIFIKASI DENGAN METODE OBJECT BASED IMAGE ANALYSIS (OBIA) PADA FOTO UDARA (Studi Kasus di Desa Wates, Kecamatan Wates, Kabupaten Kulon Progo)

2021 ◽  
pp. 355
Author(s):  
Galuh Qori’ah Fahmah Suratno ◽  
Anindya Sricandra Prasidya
Keyword(s):  
Image Analysis ◽  
Kappa Coefficient ◽  
Spectral Difference ◽  
Object Based Image Analysis ◽  
Object Based

Manusia berperan penting dalam adanya alih fungsi lahan. Dengan adanya alih fungsi lahan dibutuhkan media yang bisa menampilkan tutupan lahan suatu daerah, yaitu peta tutupan lahan. Peta tutupan lahan dibuat dari data ortofoto dengan metode klasifikasi Object Based Image Analysis (OBIA). Metode segmentasi yang digunakan adalah multiresolution dan spectral difference. Kelas tutupan lahan yang mendominasi Desa Wates adalah vegetasi sebesar 31,49% dengan luas 1.296.311,80 m2, pemukiman sebesar 30,03% dengan luas 1.236.325,31 m2, sawah sebesar 25,16% dengan luas 1.035.923,43 m2, jalan sebesar 6,19% dengan luas 254.774,38 m2, lahan terbuka sebesar 3,55% dengan luas 146.090,24 m2, sungai sebesar 2% dengan luas 82.387,70 m2, dan rel kereta api sebesar 1,59% dengan luas 65.331,42 m2. Dari hasil perhitungan, didapatkan nilai overall accuracy sebesar sebesar 74,2857% dan nilai kappa coefficient sebesar 0,7. Nilai tersebut tidak memenuhi syarat batas nilai minimum sebesar 85% karena perbedaan spasial dan temporal media uji yang tidak sebanding.

scholarly journals Avaliação de desempenho de atributos estatísticos e texturais em uma classificação de cobertura da terra baseada em objeto

2012 ◽  
Vol 18 (2) ◽  
pp. 302-326 ◽  
Author(s):  
Cristiane Nunes Francisco ◽  
Cláudia Maria de Almeida
Keyword(s):  
Image Analysis ◽  
Rio De Janeiro ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Geographic Object ◽  
Alos Prism

Este artigo tem como objetivo avaliar o desempenho de duas redes semânticas geradas por mineração de dados para a classificação de cobertura da terra por meio de análise de imagens baseada em objetos geográficos (GEographic Object-Based Image Analysis - GEOBIA). Para isto, uma rede utilizou-se de descritores estatísticos e texturais, e a outra, apenas de descritores estatísticos. A base de dados foi constituída de imagens ALOS/AVNIR fusionadas com imagens ALOS/PRISM e dados de relevo provenientes do banco de dados TOPODATA. A área de estudo corresponde ao município de Nova Friburgo, com 933 km², localizado na região serrana do estado do Rio de Janeiro. O índice Kappa alcançado pela classificação baseada em árvore de decisão composta por descritores estatísticos e texturais foi de 0,81, enquanto que este valor para a classificação derivada apenas de descritores estatísticos foi de 0,84. Considerando os índices alcançados, conclui-se que ambos os resultados apresentam excelente qualidade quanto à acurácia da classificação. O teste de hipótese entre os dois índices mostra, com nível de significância de 5%, que não há diferenças entre as duas classificações quanto à acurácia.

scholarly journals Geographic Object Based Image Analysis of WorldView-3 Imagery for Urban Hydrologic Modelling at the Catchment Scale

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1133 ◽  
Author(s):  
Mark Randall ◽  
Rasmus Fensholt ◽  
Yongyong Zhang ◽  
Marina Bergen Jensen
Keyword(s):  
Image Analysis ◽  
Land Cover ◽  
Land Cover Classification ◽  
Supplementary Data ◽  
Catchment Scale ◽  
Hydrologic Modelling ◽  
Sponge City ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Geographic Object

China’s Sponge City initiative will involve widespread installation of new stormwater infrastructure including green roofs, permeable pavements and rain gardens in at least 30 cities. Hydrologic modelling can support the planning of Sponge Cities at the catchment scale, however, highly detailed spatial data for model input can be challenging to compile from the various authorities, or, if available, may not be sufficiently detailed or updated. Remote sensing methods show great promise for mitigating this challenge due to their ability to efficiently classify satellite images into categories relevant to a specific application. In this study Geographic Object Based Image Analysis (GEOBIA) was applied to WorldView-3 satellite imagery (2017) to create a detailed land cover map of an urban catchment area in Beijing. While land cover classification results based on a Bayesian machine learning classifier alone provided an overall land cover classification accuracy of 63%, the subsequent inclusion of a series of refining rules in combination with supplementary data (including elevation and parcel delineations), yielded the significantly improved overall accuracy of 76%. Results of the land cover classification highlight the limitations of automated classification based on satellite imagery alone and the value of supplementary data and additional rules to refine classification results. Catchment scale hydrologic modelling based on the generated land cover results indicated that 61 to 82% of rainfall volume could be captured for a range of 24 h design storms under varying degrees of Sponge City implementation.

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