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Semantic segmentation is one of the most significant tasks in remote sensing (RS) image interpretation, which focuses on learning global and local information to infer the semantic label of each pixel. Previous studies devise encoder-decoder structured deep learning (DL) models to extract global and local features from RS images with the help of pretraining knowledge to predict semantic labels. However, due to the common heterogeneity between the data for pretraining and the data to be semantically segmented, these models fail to learn general features appropriate to RS datasets. In this article, we propose a novel formulation of the above problem from a causal perspective, where the learned features from pretrained models result from causality and spurious correlations, and only the former carries general information that remains invariant regardless of the exact task and dataset. Based on the above formulation, we propose stepwise intervention and reweighting (SIR). It can reduce the confounding bias introduced by the pretraining knowledge and improve the model’s ability to learn general features, making semantic segmentation of RS images benefit more from pretraining. Besides, we conduct a detailed theoretical analysis of our methods and conduct extensive experiments on two widely used public RS datasets. Experimental results demonstrate that applying SIR to encoder-decoder semantic segmentation models achieves performance improvements, proving the effectiveness and application values of the proposed method.

Downscaling nighttime light (NTL) from satellite imagery presents valuable applications at a more detailed spatial scale, especially in the realms of urban expansion and socio-economic assessment. Nevertheless, due to the complexity of geographical conditions and uncertainties in the relationships among multiple factors, the precision of NTL downscaling often encounters constraints. In this work, an incorporated multifactor geographically neural network weighted regression (MF-GNNWR) NTL downscaling framework is proposed to solve the spatial nonstationarity in high-heterogeneous urban areas, which mainly uses geographically neural network weighted regression (GNNWR) combined with multiple factors including surface physical characteristics, socio-economic attributes, and human activities to improve the accuracy of NTL, particularly in urban regions with complicated land cover. The findings illustrate that the MF-GNNWR framework displays finer downscaling accuracy on different land cover, effectively enhancing data quality. Notably, our findings underscore the pronounced influence of socio-economic and human activity factors on NTL downscaling. Comparative analysis against several alternative downscaling methodologies reveals that the MF-GNNWR framework outperforms them, exhibiting a remarkable 23.10% improvement in the Pearson correlation coefficient (r) and achieving a root-mean-square error (RMSE) of $16.95~\text {{nW/c}{m}}^{2}{/\text {sr}}$ , and after residual compensation, r continue s to increase by 1.5%, while RMSE decreases by $0.157~\text {nW/cm}^{2}{/\text {sr}}$ . These findings highlight the efficacy of the proposed framework in downscaling NTL, underscoring its advantages and practical utility.

Embedded Silicon Germanium (eSiGe) is used in the channel region of PFET devices at the 22-nm FD SOI node. The use of eSiGe results in channel regions becoming strained, which results in better hole mobility and increased performance. However, if the active diffusion regions are too short on each side of a PFET gate, then the effect of channel strain is reduced, and performance is reduced. The Continuous Active Diffusion (CnRx) layout construct suggested by the foundry is a way to help keep channel strain present within a single-cell design. In this article, the CnRx construct is implemented in a 22-nm FD SOI test chip and soft error rates (SER) of stacked-transistor flip-flops (FFs) are shown to increase with heavy ion irradiation. The effect of channel strain on PFETs results in higher collected charges from ion strikes, and charges are more easily passed between adjacent transistors through strained channels. However, with careful schematic and layout design, these effects can be mitigated to produce both high-performance and radiation-tolerant cells using the CnRx construct.