Journal
REMOTE SENSING
Volume 14, Issue 3, Pages -Publisher
MDPI
DOI: 10.3390/rs14030634
Keywords
remote sensing; u-nets; higher-order convolutional neural networks; multi-temporal data classification
Categories
Funding
- Hellenic Foundation for Research and Innovation (HFRI)
- General Secretariat for Research and Innovation (GSRI) through HFRI Faculty [1725 V4-ICARUS]
- CALCHAS project within H2020 Framework Program of European Commission [842560]
- Marie Curie Actions (MSCA) [842560] Funding Source: Marie Curie Actions (MSCA)
Ask authors/readers for more resources
This article introduces a high-dimensional convolutional network model based on the U-Net architecture, which can be used for the classification of multispectral remote sensing data. Experimental results show that the model outperforms traditional low-order models in classification performance.
Multispectral sensors constitute a core earth observation imaging technology generating massive high-dimensional observations acquired across multiple time instances. The collected multi-temporal 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 multi-dimensional 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-a-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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available