Processing UAV Based RGB Data to Identify Land Cover with Focus on Small Water Body Comparison of Methods

Komárková, Jitka; Jech, J.

doi:10.23919/cisti49556.2020.9141170

Cited by 1 publication

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Overall, the supervised methods provide better results than the unsupervised methods. The classification accuracy of the supervised methods is around 90%, in line with the article [34]. Remarkably, the deep learning method provided the highest level of accuracy in our study.…”

Section: Baroch-all Classificationssupporting

confidence: 89%

See 1 more Smart Citation

Comparison of Automatic Classification Methods for Identification of Ice Surfaces from Unmanned-Aerial-Vehicle-Borne RGB Imagery

Jech,

Komárková,

Bhattacharya

2023

Applied Sciences

Self Cite

View full text Add to dashboard Cite

This article describes a comparison of the pixel-based classification methods used to distinguish ice from other land cover types. The article focuses on processing RGB imagery, as these are very easy to obtained. The imagery was taken using UAVs and has a very high spatial resolution. Classical classification methods (ISODATA and Maximum Likelihood) and more modern approaches (support vector machines, random forests, deep learning) have been compared for image data classifications. Input datasets were created from two distinct areas: The Pond Skříň and the Baroch Nature Reserve. The images were classified into two classes: ice and all other land cover types. The accuracy of each classification was verified using a Cohen’s Kappa coefficient, with reference values obtained via manual surface identification. Deep learning and Maximum Likelihood were the best classifiers, with a classification accuracy of over 92% in the first area of interest. On average, the support vector machine was the best classifier for both areas of interest. A comparison of the selected methods, which were applied to highly detailed RGB images obtained with UAVs, demonstrates the potential of their utilization compared to imagery obtained using satellites or aerial technologies for remote sensing.

show abstract

Section: Baroch-all Classificationssupporting

confidence: 89%

“…The authors of one article [34] used a different approach to image classification. This work's advantage is that image classification methods were used for RGB data from a UAV.…”

Section: State Of Artmentioning

confidence: 99%