L-UNet: An LSTM Network for Remote Sensing Image Change Detection
Change detection of high-resolution remote sensing images is an important task in earth observation and was extensively investigated. Recently, deep learning has shown to be very successful in plenty of remote sensing tasks. The current deep learning-based change detection method is mainly based on conventional long short-term memory (Conv-LSTM), which does not have spatial characteristics. Since change detection is a process with both spatiality and temporality, it is necessary to propose an end-to-end spatiotemporal network. To achieve this, Conv-LSTM, an extension of the Conv-LSTM structure, is introduced. Since it shares similar spatial characteristics with the convolutional layer, L-...
| Authors | Shuting Sun, Lin Mu, Lizhe Wang et al. |
|---|---|
| Primary category | eess.IV |
| Published | 24 March 2026 |
| Last updated at source | 24 March 2026 |
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