Bit-alterable flash-based SSDs, offering page-level erase operation, allows individual flash pages in a block to be erased independently. The page-level erase operation alleviates the overhead of page migration during garbage collection and improves the SSD lifetime. However, when the number of invalid pages within a block exceeds a certain threshold, the latency of page-level garbage collections using page-level erase may exceed that of block-level garbage collections. In bit-alterable flash memory, existing garbage collection strategies dynamically choose between page-level and block-level garbage collections based on their latency. This often fails to fully exploit the advantage of page-level garbage collection in reducing write amplification under low-load conditions.To address this limitation, we propose an adaptive garbage collection strategy called AGCB to dynamically adjust garbage collection operations by the runtime workload of flash channels, thereby enhancing SSD performance and lifetime. Specifically, AGCB classifies flash channels as busy or idle by monitoring the depth of the transaction queue in cache. According to this classification, AGCB selectively applies page-level or block-level garbage collection operations, aiming to minimize the impact of garbage collections with host I/O requests. Meanwhile, we introduce a staged victim block selection scheme to further improve garbage collection efficiency and wear leveling. The experimental results unveil that compared with the existing schemes, AGCB reduces the number of garbage collection operations, average response time, and blocked user requests by an average of 14.6%, 14.7%, and 17.3%, respectively.

In recent years, the rapid advancement of multi-sensory platforms has significantly increased the availability of multisource remote sensing data, facilitating its systematic application to various tasks. The joint classification of hyperspectral images (HSIs) and light detection and ranging (LiDAR) data remains a critical research topic, with a key challenge being the effective extraction and integration of complementary information from multi-source remote sensing data. However, existing graph convolutional networks (GCNs)-based methods often fail to account for the heterogeneous topological relationships between HSI and LiDAR. Moreover, the discriminative power of HSI and LiDAR features extracted by existing methods is insufficient. In addition, existing methods are unable to fully exploit the rich self-supervised information present in local neighborhood. To address these limitations, we propose a heterogeneous contrastive graph fusion network (HCGFN) for the joint classification of HSI and LiDAR data. First, we propose a branch enhancement module to enhance the discriminative power of HSI and LiDAR. Second, a contrastive learning module is introduced to effectively align HSI and LiDAR representations. Finally, we propose a dynamic heterogeneous graph structure learning module to model heterogeneous relationship and achieve efficient interaction and effective fusion between HSI and LiDAR. The extensive experimental results on three benchmark datasets indicate the effectiveness of the proposed HCGFN compared with other state-of-the-art methods. Specifically, under limited training samples, the proposed HCGFN outperformed state-of-the-art methods in overall accuracy by 5.10%, 2.46%, and 8.79% on datasets Trento, MUUFL, and Houston2013, respectively.

Graph convolutional networks (GCNs) have recently gained prominence in hyperspectral images (HSIs) classification tasks given their superior performance on non-Euclidean data. However, GCN-based methods are heavily reliant on complete graph structural information, which can cause the aggregation and transmission of information across nodes from differing classes, thereby compromising the classification performance. Furthermore, the scarcity of labeled pixels in HSIs often limits the representational capability of such methods. To address these issues, we propose an aggregative and contrastive dual-view graph attention network (ACoD-GAT) for HSI classification. Specifically, we present a progressive aggregation module, including a pixel clustering submodule and a node aggregation submodule to exploit semantic information at various levels. Besides, we integrate multiscale manipulation with a diffusion matrix to construct the dual view to further extract semantic information from both local and global perspectives. Moreover, we design an unsupervised contrastive loss function and a supervised contrastive loss function to facilitate contrastive learning on the dual view, improving the representational capabilities of ACoD-GAT with very few labeled samples. The extensive experimental results on four benchmark datasets demonstrate the superiority of the proposed ACoD-GAT compared with other state-of-the-art methods.

Conventional deep learning-based methods for single remote sensing image super-resolution (SRSISR) have made remarkable progress. However, the super-resolution (SR) outputs of these methods are yet to become sufficiently satisfactory in visual quality. Recent diffusion model-based generative deep learning models are capable to enhance the visual quality of output images, but this capability is limited due to their sampling efficiency. In this article, we propose FastDiffSR, an SRSISR method based on a conditional diffusion model. Specifically, we devise a novel sampling strategy to reduce the number of sampling steps required by the diffusion model while ensuring the sampling quality. Meanwhile, the residual image is adopted to reduce computational costs, demonstrating that integrating channel attention and spatial attention begets a further improvement in the visual quality of output images. Compared to the state-of-the-art (SOTA) convolutional neural network (CNN)-based, GAN-based, and Transformer-based SR methods, our FastDiffSR improves the learned perceptual image patch similarity (LPIPS) by 0.1–0.2 and achieves better visual results in some real-world scenes. Compared with existing diffusion-based SR methods, our FastDiffSR achieves significant improvements in pixel-level evaluation metric peak signal-noise ratio (PSNR) while having smaller model parameters and obtaining better SR results on Vaihingen data with faster inference time by 2.8–28 times, showing excellent generalization ability and time efficiency. Our code will be open source athttps://github.com/Meng-333/FastDiffSR.

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.