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Segmenting clouds and their shadows is a critical challenge in remote sensing image processing. The shape, texture, lighting conditions, and background of clouds and their shadows impact the effectiveness of cloud detection. Currently, architectures that maintain high resolution throughout the entire information-extraction process are rapidly emerging. This parallel architecture, combining high and low resolutions, produces detailed high-resolution representations, enhancing segmentation prediction accuracy. This paper continues the parallel architecture of high and low resolution. When handling high- and low-resolution images, this paper employs a hybrid approach combining the Transformer and CNN models. This method facilitates interaction between the two models, enabling the extraction of both semantic and spatial details from the images. To address the challenge of inadequate fusion and significant information loss between high- and low-resolution images, this paper introduces a method based on ASMA (Axial Sharing Mixed Attention). This approach establishes pixel-level dependencies between high-resolution and low-resolution images, aiming to enhance the efficiency of image fusion. In addition, to enhance the effective focus on critical information in remote sensing images, the AGM (Attention Guide Module) is introduced, to integrate attention elements from original features into ASMA, to alleviate the problem of insufficient channel modeling of the self-attention mechanism. Our experimental results on the Cloud and Cloud Shadow dataset, the SPARCS dataset, and the CSWV dataset demonstrate the effectiveness of our method, surpassing the state-of-the-art techniques for cloud and cloud shadow segmentation. [ABSTRACT FROM AUTHOR]

Change detection is widely used in the field of building monitoring. In recent years, the progress of remote sensing image technology has provided high-resolution data. However, unlike other tasks, change detection focuses on the difference between dual-input images, so the interaction between bi-temporal features is crucial. However, the existing methods have not fully tapped the potential of multi-scale bi-temporal features to interact layer by layer. Therefore, this paper proposes a multi-scale feature interaction network (MFINet). The network realizes the information interaction of multi-temporal images by inserting a bi-temporal feature interaction layer (BFIL) between backbone networks at the same level, guides the attention to focus on the difference region, and suppresses the interference. At the same time, a double temporal feature fusion layer (BFFL) is used at the end of the coding layer to extract subtle difference features. By introducing the transformer decoding layer and improving the recovery effect of the feature size, the ability of the network to accurately capture the details and contour information of the building is further improved. The F1 of our model on the public dataset LEVIR-CD reaches 90.12%, which shows better accuracy and generalization performance than many state-of-the-art change detection models. [ABSTRACT FROM AUTHOR]

The hsp70 and hsp90 expression patterns and catalase (CAT) activity in the freshwater planaria Dugesia japonica exposed to cadmium (Cd) under laboratory conditions were investigated. Planaria were exposed to a range of Cd concentrations (0–150 μg Cd/L) for 24 h. The expression levels of hsp70 and hsp90 were determined by relative quantitative real-time polymerase chain reaction. Within the overall dose range in the experiment, the expression level of hsp70 and the activity of CAT in D. japonica were altered significantly. Hsp70 was induced in D. japonica upon Cd exposure concentrations as low as 9.375 μg Cd/L. No significant effect on the expression level of hsp90 was observed. Our findings demonstrated that stress gene hsp70, but not hsp90, was responsive to Cd contamination in D. japonica. CAT activity was significantly induced at concentrations of 18.75, 37.5, and 75 μg Cd/L after 24-h exposure. We recommend that the use of hsp70 as a biomarker should be complemented by evidence of changes in other parameters, such as CAT activity, in D. japonica. [ABSTRACT FROM AUTHOR]