| Autor: |
Kalita, Indrajit, Chakraborty, Shounak, Reddy, Talla Giridhara Ganesh, Roy, Moumita |
| Předmět: |
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| Zdroj: |
Earth Science Informatics; Feb2024, Vol. 17 Issue 1, p655-678, 24p |
| Abstrakt: |
In this manuscript, a novel framework has been presented for firm classification of a geographical area based on spatial as well as time-series analysis of multi-temporal very high resolution (VHR) satellite images. For this dual objective, an attention-based deep learning mechanism combined with the capabilities of convolutional-recurrent neural networks has been investigated for this purpose. The proposed classification strategy is introduced as 'firm' since it allows the classifier to assign only one class label to a multi-temporal image stack of co-registered images, as opposed to multiple. This technique ascertains the land-cover class by taking into consideration the geophysical changes on a landmass and thus outsmarting the conventional techniques relying on the visual interpretation of a single image. The attention mechanism focuses on the important portions of the image scene while the convolutional long short-term memory neural networks exploit the temporal dependencies on the time-series image scenes. Moreover, an adaptive land cover classification scheme, considering the features extracted from the proposed classification approach has been explored for more robust time-series based firm classification. To assess the performance of the proposed schemes, the experiments have been conducted on the two novels VHR multi-temporal land cover classification datasets. The investigated models have been shown to have the capacity to outperform the other state-of-the-art techniques under non-adaptive as well as adaptive scenarios using the multi-temporal images captured over disjoint geographical locations. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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