用于遥感图像超分辨率的全局稀疏注意力网络

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Tao Hu , Zijie Chen , Mingyi Wang , Xintong Hou , Xiaoping Lu , Yuanyuan Pan , Jianqing Li
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引用次数: 0

摘要

近来,遥感图像在包括资源勘探在内的各种任务中变得越来越受欢迎。然而,受硬件条件和形成过程的限制,获得的遥感图像往往存在分辨率低的问题。与获取高分辨率图像的高昂硬件成本不同,超分辨率软件方法是恢复低分辨率图像的良好选择。此外,遥感图像有一个共性,即在遥远的地点重复出现类似的视觉模式。为了充分捕捉这些远距离卫星图像背景,我们首先引入了全局注意力网络超分辨率方法来重建图像。这种网络虽然提高了性能,但引入了非必要信息,同时大大增加了计算工作量。为了解决这些问题,我们提出了一种名为全局稀疏注意力网络(GSAN)的创新方法,它将稀疏性约束和全局注意力整合在一起。具体来说,我们的方法应用球形位置敏感哈希算法(SLSH)将特征元素转换为哈希代码,根据哈希代码构建注意力组,并根据注意力组中的相似元素计算注意力矩阵。我们的方法能捕捉有效、有用的全局信息,并将计算量从空间大小的二次方降低到近似线性。广泛的定性和定量实验证明,与其他最先进的方法相比,我们的 GSAN 在性能和计算成本方面具有显著的竞争优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Global sparse attention network for remote sensing image super-resolution

Recently, remote sensing images have become popular in various tasks, including resource exploration. However, limited by hardware conditions and formation processes, the obtained remote sensing images often suffer from low-resolution problems. Unlike the high cost of hardware to acquire high-resolution images, super-resolution software methods are good alternatives for restoring low-resolution images. In addition, remote sensing images have a common nature that similar visual patterns repeatedly appear across distant locations. To fully capture these long-range satellite image contexts, we first introduce the global attention network super-resolution method to reconstruct the images. This network improves the performance but introduces unessential information while significantly increasing the computational effort. To address these problems, we propose an innovative method named the global sparse attention network (GSAN) that integrates both sparsity constraints and global attention. Specifically, our method applies spherical locality sensitive hashing (SLSH) to convert feature elements into hash codes, constructs attention groups based on the hash codes, and computes the attention matrix according to similar elements in the attention group. Our method captures valid and useful global information and reduces the computational effort from quadratic to asymptotically linear regarding the spatial size. Extensive qualitative and quantitative experiments demonstrate that our GSAN has significant competitive advantages in terms of performance and computational cost compared with other state-of-the-art methods.

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来源期刊
Knowledge-Based Systems
Knowledge-Based Systems 工程技术-计算机:人工智能
CiteScore
14.80
自引率
12.50%
发文量
1245
审稿时长
7.8 months
期刊介绍: Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.
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