高分辨率遥感影像语义分割是全要素地物特征自动提取和智能解译的研究热点。近年来,深度卷积神经网络在语义分割方向取得了大量成果,但遥感影像地物信息本身的复杂性限制了网络发挥。本文引入多尺度并行的空洞卷积帮助网络更大范围捕捉上下文信息,在不增加参数的情况下,网络对小目标和大目标对象均表现出较好的辨识能力。语义级特征的提取必然会导致空间分辨率损失,本文方法丢弃池化操作,并利用空洞卷积保持空间分辨率,对输出的分割结果使用全连接条件随机场融合图像级特征,弥补细节的位置信息,以此提高网络在遥感影像上的语义分割能力。网络的学习策略采用余弦退火方法调整学习率,本文引入周期递增的余弦退火策略可以让网络学习过程稳定,并获得合适数量的局部最优解,集成局部最优解进一步提升网络的像素分类能力。在Gaofen Image Dataset数据集上的实验结果表明本文方法能有效辨识复杂结构的对象,其总体精度与kappa系数均优于目前主流的语义分割模型。

Semantic segmentation of high-resolution remote sensing images is a research hotspot in automatic feature extraction. In recent years, deep convolutional neural networks have achieved a lot of results in the direction of semantic segmentation, but the complexity of the remote sensing image feature information itself limits the network to play. This paper introduces multi-scale parallel atrous convolution to help the network capture contextual information on a larger scale. Without increasing the parameters, the network shows good recognition ability for both small and large targets. The extraction of semantic-level features will inevitably lead to loss of resolution. The method in this paper discards the pooling operation and uses atrous convolution to maintain the resolution. The output segmentation results use fully connected conditional random fields to fuse image-level features to compensate for detailed location information, To improve the semantic segmentation capabilities of the network on remote sensing images. The learning strategy of the network adopts the cosine annealing method to adjust the learning rate. This paper introduces the cosine annealing strategy with increasing period to stabilize the network learning process and obtain a suitable number of local optimal solutions. The integration of the local optimal solutions further improves the network"s pixel classification ability. The experimental results on the Gaofen Image Dataset show that the method in this paper can effectively identify objects with complex structures, and its overall accuracy and kappa coefficient are better than the commonly used semantic segmentation models.