用于作物田航空图像超级分辨率的有效方差注意力增强扩散模型

IF 10.6 1区 地球科学 Q1 GEOGRAPHY, PHYSICAL ISPRS Journal of Photogrammetry and Remote Sensing Pub Date : 2024-09-11 DOI:10.1016/j.isprsjprs.2024.08.017
Xiangyu Lu , Jianlin Zhang , Rui Yang , Qina Yang , Mengyuan Chen , Hongxing Xu , Pinjun Wan , Jiawen Guo , Fei Liu
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引用次数: 0

摘要

图像超分辨率(SR)可显著提高航空图像的分辨率和质量。新兴的扩散模型(DM)通过多步细化显示了卓越的图像生成能力。为了探索这些模型在高分辨率耕地航空图像 SR 方面的有效性,我们首先建立了 CropSR 数据集,其中包括用于自我监督 SR 训练的 321,992 个样本,以及用于测试的两个真实匹配 SR 数据集,这两个数据集分别来自高低空正射影像图和定点摄影(CropSR-OR/FP)。受观察到的图像方差随飞行高度增加而减小的趋势启发,我们开发了方差-平均空间注意力(VASA)。VASA 在各种类型的 SR 模型中都表现出了有效性,因此我们进一步开发了高效 VASA 增强扩散模型 (EVADM)。为了全面、一致地评估 SR 模型的质量,我们引入了超级分辨率相对保真度指数(SRFI),该指数同时考虑了结构和感知的相似性。在 × 2 和 × 4 真实 SR 数据集上,EVADM 将弗雷谢特-截取距离(FID)分别缩短了 14.6 和 8.0,与基线相比,SRFI 分别提高了 27% 和 6%。EVADM 的卓越泛化能力通过公开的 Agriculture-Vision 数据集得到了进一步验证。广泛的下游案例研究证明了我们的 SR 方法具有很高的实用性,为现实的航空图像增强和有效的下游应用提供了广阔的前景。测试代码和数据集可在 https://github.com/HobbitArmy/EVADM 上获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Effective variance attention-enhanced diffusion model for crop field aerial image super resolution

Image super-resolution (SR) can significantly improve the resolution and quality of aerial imagery. Emerging diffusion models (DM) have shown superior image generation capabilities through multistep refinement. To explore their effectiveness on high-resolution cropland aerial imagery SR, we first built the CropSR dataset, which includes 321,992 samples for self-supervised SR training and two real-matched SR datasets from high-low altitude orthomosaics and fixed-point photography (CropSR-OR/FP) for testing. Inspired by the observed trend of decreasing image variance with higher flight altitude, we developed the Variance-Average-Spatial Attention (VASA). The VASA demonstrated effectiveness across various types of SR models, and we further developed the Efficient VASA-enhanced Diffusion Model (EVADM). To comprehensively and consistently evaluate the quality of SR models, we introduced the Super-resolution Relative Fidelity Index (SRFI), which considers both structural and perceptual similarity. On the × 2 and × 4 real SR datasets, EVADM reduced Fréchet-Inception-Distance (FID) by 14.6 and 8.0, respectively, along with SRFI gains of 27 % and 6 % compared to the baselines. The superior generalization ability of EVADM was further validated using the open Agriculture-Vision dataset. Extensive downstream case studies have demonstrated the high practicality of our SR method, indicating a promising avenue for realistic aerial imagery enhancement and effective downstream applications. The code and dataset for testing are available at https://github.com/HobbitArmy/EVADM.

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来源期刊
ISPRS Journal of Photogrammetry and Remote Sensing
ISPRS Journal of Photogrammetry and Remote Sensing 工程技术-成像科学与照相技术
CiteScore
21.00
自引率
6.30%
发文量
273
审稿时长
40 days
期刊介绍: The ISPRS Journal of Photogrammetry and Remote Sensing (P&RS) serves as the official journal of the International Society for Photogrammetry and Remote Sensing (ISPRS). It acts as a platform for scientists and professionals worldwide who are involved in various disciplines that utilize photogrammetry, remote sensing, spatial information systems, computer vision, and related fields. The journal aims to facilitate communication and dissemination of advancements in these disciplines, while also acting as a comprehensive source of reference and archive. P&RS endeavors to publish high-quality, peer-reviewed research papers that are preferably original and have not been published before. These papers can cover scientific/research, technological development, or application/practical aspects. Additionally, the journal welcomes papers that are based on presentations from ISPRS meetings, as long as they are considered significant contributions to the aforementioned fields. In particular, P&RS encourages the submission of papers that are of broad scientific interest, showcase innovative applications (especially in emerging fields), have an interdisciplinary focus, discuss topics that have received limited attention in P&RS or related journals, or explore new directions in scientific or professional realms. It is preferred that theoretical papers include practical applications, while papers focusing on systems and applications should include a theoretical background.
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