Magnifier：一种基于多粒度神经网络的烧毁区域勾画架构
Magnifier: A Multi-grained Neural Network-based Architecture for Burned Area Delineation
AI总结：
本文提出 Magnifier 方法，通过局部与全局双编码器在不同粒度上分析同一输入，在有限数据下提升烧毁区域分割性能，平均 IoU 提高 2.65%，并以不到一半的 GFLOPs 达到可比或更优效果。
摘要：
在危机管理与遥感领域，图像分割发挥着关键作用，能够通过分析视觉数据支持灾害响应和应急规划等任务。神经网络能够分析卫星获取影像，并确定哪些区域受到了灾难性事件的影响。在这一背景下，神经网络发展所面临的问题是数据稀缺以及缺乏大规模基准数据集，这限制了训练大型神经网络模型的能力。本文提出了一种新方法，即 Magnifier，以在有限数据可用性的情况下提升分割性能。Magnifier 方法适用于任何现有的编码器-解码器架构，它通过双编码器方法在不同上下文层级融合信息来扩展模型：一个局部编码器和一个全局编码器。Magnifier 利用双编码器方法对输入数据进行两次分析。具体而言，局部编码器和全局编码器以不同粒度从同一输入中提取信息。这使得 Magnifier 在给定相同输入图像集合的情况下，能够比其他方法提取更多信息。Magnifier 将结果质量平均 IoU 提升了 +2.65%，同时与原始模型相比，可训练参数数量的增长受到限制。我们使用最先进的烧毁区域分割模型对所提方法进行了评估，结果表明，在不到一半的 GFLOPs 下，平均性能达到可比或更优的水平。
英文摘要：
In crisis management and remote sensing, image segmentation plays a crucial role, enabling tasks like disaster response and emergency planning by analyzing visual data. Neural networks are able to analyze satellite acquisitions and determine which areas were affected by a catastrophic event. The problem in their development in this context is the data scarcity and the lack of extensive benchmark datasets, limiting the capabilities of training large neural network models. In this paper, we propose a novel methodology, namely Magnifier, to improve segmentation performance with limited data availability. The Magnifier methodology is applicable to any existing encoder-decoder architecture, as it extends a model by merging information at different contextual levels through a dual-encoder approach: a local and global encoder. Magnifier analyzes the input data twice using the dual-encoder approach. In particular, the local and global encoders extract information from the same input at different granularities. This allows Magnifier to extract more information than the other approaches given the same set of input images. Magnifier improves the quality of the results of +2.65% on average IoU while leading to a restrained increase in terms of the number of trainable parameters compared to the original model. We evaluated our proposed approach with state-of-the-art burned area segmentation models, demonstrating, on average, comparable or better performances in less than half of the GFLOPs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
