中国空间科学技术 ›› 2022, Vol. 42 ›› Issue (1): 125-130.doi: 10.16708/j.cnki.1000-758X.2022.0014

• 论文 • 上一篇    下一篇

融合邻域色差的PSPNet对遥感影像的分割

袁伟,许文波,周甜   

  1. 1 成都大学 建筑与土木工程学院,成都610106
    2 电子科技大学 资源与环境学院,成都610097
  • 出版日期:2022-02-25 发布日期:2022-01-27
  • 基金资助:
    四川省科技厅科技支撑计划(2020YFG0327)

Remote sensing image segmentation based on PSPNet with neighborhood color difference

YUAN Wei,XU Wenbo,ZHOU Tian   

  1. 1 School of Architecture and Civil Engineering,Chengdu University,Chengdu 610106,China
    2 School of Resources and Environment,University of Electronic Science and Technology of China,Chengdu 610097,China
  • Online:2022-02-25 Published:2022-01-27

摘要: 传统的遥感影像语义分割利用影像的光谱特性,将具有相似值的像素进行归类,但无法区分具有不同光谱的同一类对象。针对这一问题,提出将邻域的色差信息和原始图像一起输入PSPNet网络中的方法。先将RGB变换到LAB空间,然后采用CIELAB公式计算出每一个像素与周围8个邻域像素的色差值,取平均值作为该像素的邻域色差值。在WHU building dataset和Massachusetts building dataset上使用PSPNet进行对比试验。结果显示,在两个数据集中平均交并比MIoU、准确度ACC和F1score都有不同程度的提高。因此,融合邻域色差的方法能有效提高PSPNet分割精度。

关键词: 遥感影像, 语义分割, 深度学习, 领域色差, 卷积神经网络

Abstract: Traditional semantic segmentation of remote sensing image is to classify the pixels with similar values by using the spectral characteristics of images, but it is unable to distinguish the same kind of objects with different spectra. Aiming at this problem, a method was proposed in which the color difference information of neighborhood is integrated into the original image as input to PSPNet. Firstly, RGB was transformed into LAB. Then CIELAB formula was used to calculate the color difference value between each pixel and eight neighboring pixels, and the average value was taken as the neighborhood color difference value of the pixel. Experiment was done by using PSPNet on WHU building dataset and Massachusetts building dataset. The results show that the MIoU, ACC and F1score with neighborhood color difference are better than without. Therefore, the proposed method of merging neighborhood color difference is an effective way to improve the segmentation accuracy of PSPNet.

Key words: remote sensing image, semantic segmentation, deep learning, neighborhood color difference, convolutional neural network