一种改进Unet网络的遥感影像分割算法

doi:10.19665/j.issn1001-2400.2022.06.009

Abstract

Abstract:

Existing remote sensing image segmentation algorithms simply combine edge information with semantic information,they often fail to ensure an overall improvement in semantic modelling.To solve such problems,an improved remote sensing image segmentation algorithm using Unet networks is proposed.The improved algorithm adds an edge extraction module to the base encoder module.The module fuses the semantic feature information of the backbone network and the boundary feature information obtained from the input image by Canny operator and dilated mathematical morphological operations to learn the edges of remote sensing image.To further acquire global information of remote sensing images for improving segmentation accuracy,an edge-guided context aggregation module is proposed.This module enhances the intra-class consistency by capturing the long-distance dependencies between pixels in the boundary region and pixels inside the object,and then aggregates the contextual information.Under the test of the "Tianzhi Cup" AI Challenge dataset,the overall accuracy of the improved model reached about 84.5% and the average intersection ratio reached about 68.6%,with an accuracy improvement of 5.3% and 9.2% respectively compared with the Unet model.The improved model achieved an overall accuracy of 91.2% and 91.6% on the ISPRS Vaihingen and Potsdam benchmark datasets respectively,making it more suitable for accurate remote sensing image segmentation.

Key words: semantic segmentation, Canny, mathematical morphological operations, deep learning, image processing

CLC Number:

TP391.4

LI Jiaojiao, LIU Zhiqiang, SONG Rui, LI Yunsong. Algorithm for segmentation of remote sensing imagery using the improved Unet[J].Journal of Xidian University, 2022, 49(6): 67-75.

Figures/Tables 9

References 25

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方法名称	a	b	c	d
边缘提取模块			√	√
边缘提取模块(不加Canny分支)		√
边缘引导上下文聚合模块		√	√	√
损失函数L_edge				√
R_mIoU/%	59.42	60.03	60.53	61.76

方法名称	农场	道路	水源	植被	R_OA	R_mIoU
FCN	58.9	24.6	92.4	74.8	83.0	62.7
Unet	60.0	20.5	92.5	64.4	79.2	59.4
SegNet	59.1	28.6	92.6	73.0	82.4	63.3
PSPNet	64.5	34.9	93.9	72.7	83.4	66.5
DeeplabV3+	65.2	34.9	93.8	73.4	83.7	66.8
Ours	65.4	40.8	93.7	74.6	84.5	68.6

方法名称	不可渗透表面	建筑物	低矮植被	树木	汽车	背景	R_OA	F₁均值
SVL_3	86.6	91.0	77.0	85.0	55.6	58.9	84.8	79.0
DST_2	90.5	93.7	83.4	89.2	72.6	61.2	89.1	85.9
UZ_1	89.2	92.5	81.6	86.9	57.3	58.6	87.3	81.5
RIT_L7	90.1	93.2	81.4	87.2	72.0	63.4	87.8	84.8
ONE_7	91.0	94.5	84.4	89.9	77.8	71.9	89.8	87.5
ADL_3	89.5	93.2	82.3	88.2	63.3	69.6	88.0	83.3
DLR_10	92.3	95.2	84.1	90.0	79.3	79.3	90.3	88.2
CASIA2	93.2	96.0	84.7	89.9	86.7	84.7	91.1	90.1
BKHN10	92.9	96.0	84.6	89.8	88.8	81.1	91.0	90.4
TreeUNet	92.5	94.9	83.6	89.6	85.9	82.6	90.4	89.3
文中	93.4	95.9	84.6	90.2	89.6	85.9	91.2	90.6

方法名称	不可渗透表面	建筑物	低矮植被	树木	汽车	杂物	R_OA	F₁均值
SVL_1	83.5	91.7	72.2	63.2	62.2	69.3	77.8	74.6
DST_5	92.5	96.4	86.7	88.0	94.7	78.4	90.3	91.7
UZ_1	89.3	95.4	81.8	80.5	86.5	79.7	85.8	86.7
RIT_L7	91.2	94.6	85.1	85.1	92.8	83.2	88.4	89.8
SWJ_2	94.4	97.4	87.8	87.6	94.7	82.1	91.7	92.4
CASIA2	93.3	97.0	87.7	88.4	96.2	83.0	91.1	92.5
TreeUNet	93.1	97.3	86.8	87.1	95.8	82.9	90.7	92.0
文中	93.6	97.2	89.0	89.6	96.3	86.4	91.6	93.1

Algorithm for segmentation of remote sensing imagery using the improved Unet

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