Cloud detection is one of the key technologies in the field of remote sensing. Although extensive deep learning-based cloud detection methods achieve good performance, their detection results in confusing areas such as cloud boundaries and thin clouds are often not satisfactory due to the potential inter-class similarity and intra-class inconsistency of objects. To this end, we propose a Boundary-Aware Bilateral Fusion network (BABFNet), which effectively enhances cloud detection in confusing areas by introducing a boundary prediction branch as an auxiliary. To avoid the loss of details, the boundary prediction branch is designed to run at full resolution with a shallow architecture, while some Semantic Enhancement Modules (SEMs) are used to supplement high-level semantic information by introducing multi-level encoder features of the cloud detection branch. This feature sharing in turn drives the cloud detection branch to focus more on cloud boundaries during training. At the end of the network, a Bilateral Fusion Module (BFM) is added for information complementarity between features from these two branches. The features from the cloud detection branch provide multi-scale features to the boundary prediction branch for more accurate boundary prediction, while the features from the boundary prediction branch further serve as prior knowledge to help the cloud detection branch aggregate contextual information. To verify the effectiveness of the proposed method, we select four different networks as cloud detection branches and conduct comparative experiments on two public datasets, GF-1 WFV and MODIS. The experimental results show that the proposed method significantly enhances cloud detection in confusing areas.

In recent years, a large number of convolutional neural networks-based cloud detection algorithms have been proposed for remote sensing image preprocessing and most of them have an encoder-decoder structure. However, downsampling and upsampling operations, as the basic components of these methods, inevitably lead to the loss of detailed information in high-level features, which affects cloud detection performance. At the same time, the physical characteristics of the cloud, such as the variable size and irregular structure, also put forward requirements for the multi-scale feature representation ability of the network. To this end, we propose a novel cloud detection network named DMNet, which contains a Dense Feature Enhancement Module (DFEM) and a Multi-scale Context Fusion Spatial Attention Module (MCFSAM). DFEM aims to achieve information complementarity by exploiting the different properties of the features at different levels of the encoder, so as to strengthen the detailed information of high-level features and make the low-level features have more semantics. MCFSAM introduces a Multi-scale Context Fusion Block (MCFB) in spatial attention, which enables the network to densely capture contextual information at different scales and further emphasize useful features in the spatial dimension. Extensive experiments on GF-1 wide field-of-view satellite imagery (GF-1 WFV) dataset and Moderate-Resolution Imaging Spectroradiometer (MODIS) dataset demonstrate that our method outperforms other state-of-the-art cloud detection algorithms.

The influence of the ozone oxidation conditions on the interfacial and electrical properties of high-k/SiGe gate stacks was investigated by using a step-by-step fabrication procedure. The relationship between the electrical characteristics and the corresponding interfacial chemical structures revealed that a long oxidation time was necessary to obtain a high-quality interlayer (IL), whereas adverse in terms of interface state density (D_it) and equivalent oxide thickness (EOT) scaling. This conclusion suggested us to employ a post-high-k-deposition oxidation (post-O) method to fabricate high-k/IL/SiGe gate stacks, in which the ozone oxidation was carried out after the deposition of the high-k layer. Compared with the step-by-step method, the post-O method could realize an ultrathin IL over a long oxidation time, owing to the reduced oxidation rate. Consequently, a well-balanced high-k/IL/SiGe gate stack with an ultrathin Ge-free IL of 0.26 nm, low gate leakage at V_FB-1 of 6 × 10⁻⁴ A/cm², and D_it as low as 2.2 × 10¹² eV⁻¹cm⁻² was obtained.