Название: Change Detection and Image Time Series Analysis 2: Supervised Methods
Автор: Abdourrahmane M. Atto, Francesca Bovolo
Издательство: Wiley-ISTE
Год: 2021
Страниц: 272
Язык: английский
Формат: epub
Размер: 10.2 MB
Change Detection and Image Time Series Analysis 2 presents supervised machine-learning-based methods for temporal evolution analysis by using image time series associated with Earth observation data. Encouraged by the latest developments in Machine Learning and computer vision, Deep Learning approaches have successfully been applied to EO data. These techniques offer new opportunities for classification tasks, whose main advantage is to automate the extraction of a discriminative and informative feature representation. Thus, there is no need for feature engineering. In the context of EO time series analysis, the exploration of deep methodologies is still a young, but swiftly evolving research area. Given the temporal structure of SITS data, the first remote sensing approaches have explored the use of deep recurrent neural network (RNN) architectures, which effectively handle sequential data.