Separability Analysis of Atlantic Forest Patches by Comparing Parametric and Non-Parametric Image Classification Algorithms

Martines, Marcos Roberto; Lúcio, Mariana de Paula Garcia; Cavagis, Alexandre Donizeti Martins; Fantin, Marcel; Ferreira, Ricardo Vicente; Alves, Matheus Oliveira; Toppa, Rogério Hartung

doi:10.4236/jgis.2019.115035

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…Martines et al used geographic processing and remote sensing technologies to explore the effect of three image classification algorithms, MLC, SVM and RT. It is found that the three algorithms can distinguish the target successfully [8]. Among them, MLC has higher efficiency, RT has the lowest efficiency, and SVM has the highest accuracy.…”

Section: Related Workmentioning

confidence: 96%

Research on Segmentation Method of Greening Landscape of Urban Community Based on Improved U-Net Network

Sui

2022

Security and Communication Networks

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Aiming at the problem of low segmentation accuracy of greening landscape of urban community, a segmentation method of greening landscape of urban community based on improved U-Net network is proposed by adding an encoder on U-Net network. In addition, simulation is carried out on the remote sensing image data collected by GPRS network, and the effectiveness of the method is verified. The simulation results show that the result of proposed method is close to that of manual annotation. Compared with the traditional segmentation algorithm SVM and SegNet method, as well as the U-Net network before improvement, the proposed method has higher segmentation accuracy. The segmentation accuracy can reach up to 91%, the intersection ratio can reach 76%, and the mean pixel accuracy and mean intersection ratio can reach 89% and 74%, which indicates that the proposed method has certain validity and practicability.

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Section: Related Workmentioning

confidence: 96%

Research on Segmentation Method of Greening Landscape of Urban Community Based on Improved U-Net Network

Sui

2022

Security and Communication Networks

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“…Our study addresses this issue and, in a sense, fills a research gap. It is also worth noting that a similar study (but less detailed) was conducted by Martines et al [25], who also compared three image classification algorithms, namely SVM, ML and RT. However, in the case of this study, the AI classifiers were not used to estimate impervious surfaces, but to verify the discriminability of forest areas, forestry and other uses, for which these authors used geoprocessing and remote sensing techniques, as well as Sentinel satellite data, which have a relatively low resolution (although still higher than Landsat).…”

Section: Introductionmentioning

confidence: 94%

A Comparison of Different Machine Learning Algorithms in the Classification of Impervious Surfaces: Case Study of the Housing Estate Fort Bema in Warsaw (Poland)

2022

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The aim of this study is to extract impervious surfaces and show their spatial distribution, using different machine learning algorithms. For this purpose, geoprocessing and remote sensing techniques were used and three classification methods for digital images were compared, namely Support Vector Machines (SVM), Maximum Likelihood (ML) and Random Trees (RT) classifiers. The study area is one of the most prestigious and the largest housing estates in Warsaw (Poland), the Fort Bema housing complex, which is also an exemplary model for hydrological solutions. The study was prepared on the Geographic Information System platform (GIS) using aerial optical images, orthorectified and thus provided with a suitable coordinate system. The use of these data is therefore supported by the accuracy of the resulting infrared channel product with a pixel size of 0.25 m, making the results much more accurate compared to satellite imagery. The results of the SVM, ML and RT classifiers were compared using the confusion matrix, accuracy (Root Mean Square Error /RMSE/) and kappa index. This showed that the three algorithms were able to successfully discriminate between targets. Overall, the three classifiers had errors, but specifically for impervious surfaces, the highest accuracy was achieved with the SVM classifier (the highest percentage of overall accuracy), followed by ML and RT with 91.51%, 91.35% and 84.52% of the results, respectively. A comparison of the visual results and the confusion matrix shows that although visually the RT method appears to be the most detailed classification into pervious and impervious surfaces, the results were not always correct, e.g., water/shadow was detected as an impervious surface. The NDVI index was also mapped for the same spatial study area and its application in the evaluation of pervious surfaces was explained. The results obtained with the GIS platform, presented in this paper, provide a better understanding of how these advanced classifiers work, which in turn can provide insightful guidance for their selection and combination in real-world applications. The paper also provides an overview of the main works/studies dealing with impervious surface mapping, with different methods for their assessment (including the use of conventional remote sensing, NDVI, multisensory and cross-source data, ‘social sensing’ and classification methods such as SVM, ML and RT), as well as an overview of the research results.

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Separability Analysis of Atlantic Forest Patches by Comparing Parametric and Non-Parametric Image Classification Algorithms

Cited by 2 publications

References 19 publications

Research on Segmentation Method of Greening Landscape of Urban Community Based on Improved U-Net Network

Research on Segmentation Method of Greening Landscape of Urban Community Based on Improved U-Net Network

A Comparison of Different Machine Learning Algorithms in the Classification of Impervious Surfaces: Case Study of the Housing Estate Fort Bema in Warsaw (Poland)

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