Abstract
Remote sensing (RS) visual tasks have gained significant academic and practical importance. However, they encounter numerous challenges that hinder effective feature extraction, including the detection and recognition of multiple objects exhibiting substantial variations in scale within a single image. While prior dual-branch or multi-branch architectural strategies have been effective in managing these object variances, they have concurrently resulted in considerable increases in computational demands and parameter counts. Consequently, these architectures are rendered less viable for deployment on resource-constrained devices. Contemporary lightweight backbone networks, designed primarily for natural images, frequently encounter difficulties in effectively extracting features from multi-scale objects, which compromises their efficacy in RS visual tasks. This article introduces LWGANet, a specialized lightweight backbone network tailored for RS visual tasks, incorporating a novel lightweight group attention (LWGA) module designed to address these specific challenges. LWGA module, tailored for RS imagery, adeptly harnesses redundant features to extract a wide range of spatial information, from local to global scales, without introducing additional complexity or computational overhead. This facilitates precise feature extraction across multiple scales within an efficient this http URL was rigorously evaluated across twelve datasets, which span four crucial RS visual tasks: scene classification, oriented object detection, semantic segmentation, and change detection. The results confirm LWGANet's widespread applicability and its ability to maintain an optimal balance between high performance and low complexity, achieving SOTA results across diverse datasets. LWGANet emerged as a novel solution for resource-limited scenarios requiring robust RS image processing capabilities.
Abstract (translated)
遥感（RS）视觉任务在学术和实际应用中具有重要意义，但它们面临着许多挑战，这些挑战阻碍了有效的特征提取，包括在同一图像内检测并识别多个尺寸变化显著的对象。尽管先前的双分支或多分支架构策略在处理对象变异方面表现出了有效性，但同时也导致了计算需求和参数数量的巨大增加。因此，在资源受限设备上部署这些架构变得不那么可行。目前为自然图像设计的轻量级骨干网络通常难以有效地从多尺度物体中提取特征，从而影响其在遥感视觉任务中的效能。本文介绍了LWGANet，这是一种专门针对遥感视觉任务定制的轻量化骨干网络，并引入了新型的轻量级组注意力（LWGA）模块来解决这些特定挑战。LWGA模块专为遥感图像设计，能够有效地利用冗余特征提取从局部到全局尺度的广泛空间信息，同时不增加复杂性或计算开销。这使得在高效框架内进行多尺度精确特征提取成为可能。该研究在涵盖四个关键RS视觉任务（场景分类、定向物体检测、语义分割和变化检测）的十二个数据集上进行了严格的评估。结果证实了LWGANet广泛的适用性和其保持高性能与低复杂性之间最佳平衡的能力，在各种数据集中取得了最先进的成果。LWGANet成为了一个新的解决方案，适用于需要强大遥感图像处理能力但资源有限的情境。
URL
https://arxiv.org/abs/2501.10040
