【摘 要】
：
针对高光谱图像语义分割中空间信息利用不充分的问题,提出了一种基于多任务学习的语义分割算法,分别为语义分割及基于遥感指数的图像重建任务.网络主要由3D卷积和2D卷积组成,通过主成分分析(principal component analysis,PCA)来减少冗余信息,通过不同任务的重要程度优化两者的损失函数权重.2个特征在分割任务中融合送入由3D通道注意力及空间池化金字塔(spatial pyramid pooling,SPP)组成的光谱-空间特征,提取模块获取谱间相关性及空间上下文信息,从而实现更好的分割
【机 构】
：
西安交通大学信息与通信工程学院,西安710049
论文部分内容阅读
针对高光谱图像语义分割中空间信息利用不充分的问题,提出了一种基于多任务学习的语义分割算法,分别为语义分割及基于遥感指数的图像重建任务.网络主要由3D卷积和2D卷积组成,通过主成分分析(principal component analysis,PCA)来减少冗余信息,通过不同任务的重要程度优化两者的损失函数权重.2个特征在分割任务中融合送入由3D通道注意力及空间池化金字塔(spatial pyramid pooling,SPP)组成的光谱-空间特征,提取模块获取谱间相关性及空间上下文信息,从而实现更好的分割效果.通过在Indian Pines数据集上验证,总体分割精度(overall accuracy,OA)达到了99.55％,Kappa达到了99.49％,有效地提高了高光谱遥感图像分割精度,同时在Salinas数据上通过消融实验也验证了所提算法各模块及其参数设置的有效性.
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