LGCNet: A Cloud Detection Method in Remote Sensing Images Using Local and Global Semantics

Detecting and eliminating clouds is a crucial step in Remote Sensing Images (RSIs) pre-processing. The removal of clouds can significantly enhance the performance of subsequent remote sensing applications. Existing deep learning (DL) based cloud detection methods extract semantic information to improve feature representation and, subsequently, detection performance. However, these methods do not fully utilize the potential of context semantic information. Besides, to capture semantics from large receptive fields, they employ convolution operators with large kernel sizes, which results in high computational costs. Thus, these computationally heavy models are not suitable for resource-limited devices, particularly satellites. To address this issue, we propose a cloud detection model, LGCNet. This model efficiently extracts both local and global contextual information, fully utilizing semantics while reducing resource usage. LGCNet is built on an encoder-decoder structure. Specifically, the encoder extracts local scale-aware semantics through proposed Local Semantics Blocks (LSBs), which are then skip-connected to the decoder. This approach provides adaptive and diverse local contextual information. On the top of the encoder, the high-level global semantics is captured via the proposed Global Feature TransBlock (GFTB). A variety of extracted semantics ensures improved detection performance. We evaluate the proposed method using two public datasets: LandSat8 and MODIS. We conducted experiments on both a server and an edge computing device. Our extensive experiments revealed that LGCNet outperforms other lightweight cloud detection and semantic segmentation methods in terms of performance and computational load.