Method of Improving Instance Segmentation for Very High Resolution Remote Sensing Imagery Using Deep Learning

Hnatushenko, Volodymyr; Zhernovyi, Vadym

doi:10.1007/978-3-030-61656-4_21

Cited by 4 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In other words, the training data should meet two requirements, i.e., adequate diversity and size, both of which can be achieved through data augmentation. A study (Shijie et al, 2017, Perez and Wang, 2017, Tran et al, 2017, Hnatushenko and Zhernovyi, 2020 has shown that applying data augmentation methods improved the accuracy of results by 4 − 8%.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Quality of CNN-Based Burned Area Detection in Satellite Imagery Through Data Augmentation

Hnatushenko,

Soldatenko

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. This study aims to enhance the quality of detecting burned areas in satellite imagery using deep learning by optimizing the training dataset volume through the application of various augmentation methods. The study analyzes the impact of image flipping, rotation, and noise addition on the overall accuracy for different classes of burned areas in a forest: fire, burned, smoke and background. Results demonstrate that while single augmentation techniques such as flipping and rotation alone did not result in significant improvements, a combined approach and the addition of noise resulted in an enhancement of the classification accuracy. Moreover, the study shows that augmenting the dataset through the use of multiple augmentation methods concurrently, resulting in a fivefold increase in input data, also enhanced the recognition accuracy. The study also highlights the need for further research in developing more efficient CNN models and in experimenting with additional augmentation methods to improve the accuracy of burned area detection, which would benefit environmental protection and emergency response services.

show abstract

Section: Introductionmentioning

confidence: 99%