article{Giannopoulos_2022a,
title = {4D U-Nets for Multi-Temporal Remote Sensing Data Classification},
author = {Giannopoulos, Michalis and Tsagkatakis, Grigorios and Tsakalides, Panagiotis},
year = {2022},
date = {2022-01-28},
journal = {Remote Sensing},
volume = {14},
number = {3},
pages = {634},
abstract = {Multispectral sensors constitute a core earth observation imaging technology generating massive high-dimensional observations acquired across multiple time instances. The collected multitemporal remote sensed data contain rich information for Earth monitoring applications, from flood detection to crop classification. To easily classify such naturally multidimensional data, conventional low-order deep learning models unavoidably toss away valuable information residing across the available dimensions. In this work, we extend state-of-the-art convolutional network models based on the U-Net architecture to their high-dimensional analogs, which can naturally capture multidimensional dependencies and correlations. We introduce several model architectures, both of low as well as of high order, and we quantify the achieved classification performance vis-à-vis the latest state-of-the-art methods. The experimental analysis on observations from Landsat-8 reveals that approaches based on low-order U-Net models exhibit poor classification performance and are outperformed by our proposed high-dimensional U-Net scheme.},
keywords = {Higher-Order Convolutional Neural Networks, Multi-Temporal Data Classification, Remote Sensing, U-Nets},
pubstate = {published},
tppubtype = {article}
}
title = {4D U-Nets for Multi-Temporal Remote Sensing Data Classification},
author = {Giannopoulos, Michalis and Tsagkatakis, Grigorios and Tsakalides, Panagiotis},
year = {2022},
date = {2022-01-28},
journal = {Remote Sensing},
volume = {14},
number = {3},
pages = {634},
abstract = {Multispectral sensors constitute a core earth observation imaging technology generating massive high-dimensional observations acquired across multiple time instances. The collected multitemporal remote sensed data contain rich information for Earth monitoring applications, from flood detection to crop classification. To easily classify such naturally multidimensional data, conventional low-order deep learning models unavoidably toss away valuable information residing across the available dimensions. In this work, we extend state-of-the-art convolutional network models based on the U-Net architecture to their high-dimensional analogs, which can naturally capture multidimensional dependencies and correlations. We introduce several model architectures, both of low as well as of high order, and we quantify the achieved classification performance vis-à-vis the latest state-of-the-art methods. The experimental analysis on observations from Landsat-8 reveals that approaches based on low-order U-Net models exhibit poor classification performance and are outperformed by our proposed high-dimensional U-Net scheme.},
keywords = {Higher-Order Convolutional Neural Networks, Multi-Temporal Data Classification, Remote Sensing, U-Nets},
pubstate = {published},
tppubtype = {article}
}