计算机科学
人工智能
分割
特征(语言学)
核(代数)
卷积神经网络
遥感
频道(广播)
模式识别(心理学)
编码(集合论)
遥感应用
语义学(计算机科学)
数据挖掘
图像分割
钥匙(锁)
航程(航空)
深度学习
抽象
特征提取
特征向量
人工神经网络
机器学习
土地覆盖
注意力网络
语义特征
卫星图像
资源(消歧)
上下文图像分类
计算复杂性理论
作者
Rui Li,Shunyi Zheng,Ce Zhang,Chenxi Duan,Jianlin Su,Libo Wang,Peter M. Atkinson
标识
DOI:10.1109/tgrs.2021.3093977
摘要
Semantic segmentation of remote sensing images plays an important role in a wide range of applications, including land resource management, biosphere monitoring, and urban planning. Although the accuracy of semantic segmentation in remote sensing images has been increased significantly by deep convolutional neural networks, several limitations exist in standard models. First, for encoder–decoder architectures such as U-Net, the utilization of multiscale features causes the underuse of information, where low-level features and high-level features are concatenated directly without any refinement. Second, long-range dependencies of feature maps are insufficiently explored, resulting in suboptimal feature representations associated with each semantic class. Third, even though the dot-product attention mechanism has been introduced and utilized in semantic segmentation to model long-range dependencies, the large time and space demands of attention impede the actual usage of attention in application scenarios with large-scale input. This article proposed a multiattention network (MANet) to address these issues by extracting contextual dependencies through multiple efficient attention modules. A novel attention mechanism of kernel attention with linear complexity is proposed to alleviate the large computational demand in attention. Based on kernel attention and channel attention, we integrate local feature maps extracted by ResNet-50 with their corresponding global dependencies and reweight interdependent channel maps adaptively. Numerical experiments on two large-scale fine-resolution remote sensing datasets demonstrate the superior performance of the proposed MANet. Code is available at https://github.com/lironui/Multi-Attention-Network.
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