Pan-sharpening is a fundamental and crucial image processing task for many remote sensing applications, which generates a high-resolution multi-spectral image by fusing a low-resolution multi-spectral image and a high-resolution panchromatic image. Recently, vision transformers have been introduced into the pan-sharpening task for utilizing global contextual information. However, long-range and local dependencies modeling and multi-scale feature learning are all essential to the pansharpening task. Learning and exploiting these various information raises a big challenge and limits the performance and efficiency of existing pansharpening methods. To solve this issue, we propose a Pan-sharpening Network based on Multi-scale embedding and Dual attention transformers (MDPNet). Specifically, a multi-scale embedding block is proposed to embed multi-scale information of the images into vectors. Thus, transformers only need to process a multi-spectral embedding sequence and a panchromatic embedding sequence to efficiently use multi-scale information. Furthermore, an additive hybrid attention transformer is proposed to fuse the embedding sequences in an additive injection manner. Finally, a channel self-attention transformer is proposed to utilize channel correlations for high-quality detail generation. Experiments over QuickBird and WorldView-3 data sets demonstrate the proposed MDPNet outperforms state-of-the-art methods visually and quantitatively with low running time. Ablation studies further verify the effectiveness of the proposed multi-scale embedding and transformers in pan-sharpening.

Background: Mechanical damage to plants triggers local and systemic electrical signals that are eventually decoded into plant defense responses. These responses are constantly affected by other environmental stimuli in nature, for instance, light fluctuation. In recent years, studies on decoding plant electrical signals powered by various machine learning models are increasing in a sense of early prediction or detection of different environmental stresses that threaten plant growth or crop yields. However, the main bottleneck is the low-throughput nature of plant electrical signals, making it challenging to obtain a substantial amount of training data. Consequently, training these models with small datasets often leads to unsatisfactory performance. Results: In the present work, we set out to decode wound-induced electrical signals (also termed slow wave potentials, SWPs) from plants that are deprived of light to different extents. Using non-invasive electrophysiology, we separately collected sets of local and distal SWPs from the treated plants. Then, we proposed a workflow based on few-shot learning to automatically identify SWPs. This workflow incorporates data preprocessing, feature extraction, data augmentation and classifier training. We established the integral and the first-order derivative as features for efficiently classifying SWPs. We then proposed an Adversarial Autoencoder (AAE) structure to augment the SWP samples. Combining them, the Random Forest classifier allowed remarkable classification accuracies of 0.99 for both local and systemic SWPs. In addition, in comparison to two other reported methods, our proposed AAE structure enabled better classification results using our tested features and classifiers. Conclusions: The results of this study establish new features for efficiently classifying wound-induced electrical signals, which allow for distinguishing dark-affected local and systemic plant wound responses. We also propose a new data augmentation structure to generate virtual plant electrical signals. The methods proposed in this study could be further applied to build models for crop plants using electrical signals as inputs, and also to process other small-scale signals.

Pansharpening aims to integrate low-resolution multispectral images (LRMS) and panchromatic images (PAN) to obtain the high-resolution multispectral images (HRMS). As PAN and LRMS images capture the same scene, they have some common information. Due to sensor characteristics differences, they also have unique information. Therefore, recent pansharpening methods separate common and unique features to reduce redundancy. However, they are not explicitly equipped with specific modules to extract common and unique features, which limits redundancy removal. To solve the problems, we propose a pansharpening method using a cross-attention-based module to specifically extract common and unique feature maps from the MS and PAN images. In addition, mutual information maximization and minimization constraints are used to enforce the common and unique features, which can reduce the redundant information. Finally, these features are concatenated to generate the HRMS. Extensive experimental results on QuickBird and Gaofen-2 datasets demonstrate that the proposed method outperforms other methods quantitatively and qualitatively.

Pan-sharpening is a fundamental and crucial task in the remote sensing image processing field, which generates a high-resolution multi-spectral image by fusing a low-resolution multi-spectral image and a high-resolution panchromatic image. Recently, deep learning techniques have shown competitive results in pan-sharpening. However, diverse features in the multi-spectral and panchromatic images are not fully extracted and exploited in existing deep learning methods, which leads to information loss in the pan-sharpening process. To solve this problem, a novel pan-sharpening method based on multi-resolution transformer and two-stage feature fusion is proposed in this article. Specifically, a transformer-based multi-resolution feature extractor is designed to extract diverse image features. Then, to fully exploit features with different content and characteristics, a two-stage feature fusion strategy is adopted. In the first stage, a multi-resolution fusion module is proposed to fuse multi-spectral and panchromatic features at each scale. In the second stage, a shallow-deep fusion module is proposed to fuse shallow and deep features for detail generation. Experiments over QuickBird and WorldView-3 datasets demonstrate that the proposed method outperforms current state-of-the-art approaches visually and quantitatively with fewer parameters. Moreover, the ablation study and feature map analysis also prove the effectiveness of the transformer-based multi-resolution feature extractor and the two-stage fusion scheme.

目的遥感图像指代分割旨在根据自然语言指令生成目标区域的像素级掩码。然而，通用分割基础模型迁移至遥感场景时，仍面临深层跨模态交互过程中语义约束逐渐衰减、解码阶段层级查询信息利用不充分以及复杂目标边界监督不充分等问题。方法针对上述问题，提出一种用于遥感图像指代分割的语义动态边界增强方法—SDM-SAM2。该框架以SAM2（Segment Anything Model 2）为基础，在图像编码阶段构建阶段感知文本记忆，通过保存并检索不同阶段的文本信息，以维持指代表达约束，从而缓解深层语义传播中的语义衰减问题；在Mask Decoder中引入任务自适应多层查询融合模块，通过可学习层级权重对初始查询和不同解码层查询进行融合，使浅层定位信息与深层语义表征共同参与掩码预测，提升模型对复杂目标的定位与分割能力；同时设计文本加权Sobel边界损失，在连续前景概率图上提取可反向传播的边界响应，并结合文本指定目标对目标轮廓施加显式约束。结果在RefSegRS和RRSIS-D两个数据集上开展实验。完整模型在RefSegRS测试集上取得78.05%的平均交并比（Mean Intersection over Union，mIoU）和84.16%的总体交并比（Overall Intersection over Union，oIoU），在RRSIS-D测试集上取得68.49%的mIoU和78.59%的oIoU。两个数据集上的消融结果表明，阶段感知文本记忆、任务自适应多层查询融合和文本加权Sobel边界损失均能从不同环节改善基础模型；其中，阶段感知文本记忆在两个数据集上均取得最高的单模块mIoU。不同边界算子对比结果表明，Sobel算子取得了三种候选算子中最高的mIoU。结论SDM-SAM2从语义保持、动态解码和边界建模三个方面增强了SAM2对遥感图像指代分割任务的适配能力，能够改善复杂遥感场景下文本指定目标的定位和掩码预测效果。代码链接：https：//github.com/3038794185-cloud/SDM-SAM2。

Pansharpening is a critical technique in remote sensing aimed at generating high-resolution multispectral (HRMS) images by fusing high-resolution panchromatic (PAN) images with low-resolution multispectral (MS) images. However, existing methods face challenges in frequency-domain feature extraction, as global analyses often neglect regional characteristics, while local patch-based approaches may compromise the structural integrity of the image. To address these issues, we propose a novel pansharpening network utilizing a dual-branch architecture to extract frequency-domain features from PAN and MS images. This approach effectively harnesses their complementary information to enhance pansharpening performance. The extracted features are integrated with spatial-domain details via a hierarchical fusion module, enabling comprehensive reconstruction of HRMS images. Additionally, we introduce a novel frequency-domain feature extraction method, termed Region-based Self-similarity Adaptive Frequency Convolution (RSAFC). This method dynamically adjusts the frequency characteristics of distinct image regions by leveraging cluster-based self-similarity relationships and adaptive convolution operations that combine amplitude and phase, thereby achieving precise modeling of frequency-domain information. Experimental evaluations on the WorldView-3 and QuickBird datasets demonstrate that the proposed method outperforms state-of-the-art approaches in both subjective and objective metrics.