基于多尺度及DESTIN约束的高分遥感影像田块语义分割方法研究  

Semantic segmentation of agricultural fields from high resolution remote sensing image based on multi-scale and DESTIN constraints

作　　者：肖庆云 程涛[2] 顾兴健 朱艳[2] 黄芬[1] XIAO Qingyun;CHENG Tao;GU Xingjian;ZHU Yan;HUANG Fen(College of Artificial Intelligence,Nanjing Agricultural University,Nanjing 210031,China;National Information Agriculture Engineering Technology Center/Engineering Research Center of Smart Agriculture,Ministry of Education/Key Laboratory of Crop System Analysis and Decision Making,Ministry of Agriculture and Rural Affairs/Jiangsu Provincial Key Laboratory of Information Agriculture/Collaborative Innovation Center for Modern Crop Production,Nanjing Agricultural University,Nanjing 210095,China)

机构地区：[1]南京农业大学人工智能学院,江苏南京210031 [2]南京农业大学国家信息农业工程技术中心/智慧农业教育部工程研究中心/农业农村部农作物系统分析与决策重点实验室/江苏省信息农业重点实验室/现代作物生产省部共建协同创新中心,江苏南京210095

出　　处：《南京农业大学学报》2024年第5期989-999,共11页Journal of Nanjing Agricultural University

基　　金：国家重点研发计划项目(2016YFD0300607)。

摘　　要：[目的]本研究旨在改善基于深度学习的遥感影像田块语义分割中出现的区域不封闭、边缘不贴合、噪点问题,并进一步修正语义分割的识别错误。[方法]以安徽省阜南县、江苏省淮安市为研究地点,自建了农田田块数据集,引入考虑影像多尺度特征的尺度分割思想与基于物候学的DESTIN(delineation by fusing spatial and temporal information)分割算法,提出了基于多尺度及DESTIN约束的高分遥感影像农田田块语义分割方法。[结果]多尺度与DESTIN约束下基于深度模型的田块语义分割有效改善模型出现的区域不封闭、边缘不贴合、噪点和块状模糊等问题,一定程度修正了深度模型语义分割的错误识别,IoU指标在2个测试集上分别达到94.08%和90.79%,相较深度模型的遥感影像田块语义分割分别提高1.65%和2.32%,对研究区域的田块提取区域更完整、精度更高。[结论]多尺度及DESTIN约束进一步改善了田块语义分割问题,有助于提高高分遥感影像的田块识别精度。[Objectives]The objective of this study was to address the problems of region non-closure,edge non-fitting and noise in the semantic segmentation of remote sensing images based on deep learning,and to further correct the recognition errors of semantic segmentation.[Methods]Taking Funan County of Anhui Province and Huai'an City of Jiangsu Province as the research sites,this paper built its own farmland plot dataset,introduced the idea of scale segmentation considering the multi-scale characteristics of images and the DESTIN(delineation by fusing spatial and temporal information)segmentation algorithm based on phenology,and proposed a semantic segmentation method for farmland plots based on multi-scale and DESTIN constraints.[Results]The field semantic segmentation based on the deep model under multi-scale and DESTIN-constrained effectively improved the problems of unenclosed region,non-fitting edges,noise and block-like blurring in the model,and corrected the misidentification area of the deep model semantic segmentation to a certain extent,and the IoU index reached 94.08%and 90.79%on the two test sets,respectively,and increased the semantic segmentation of the field by 1.65%and 2.32%compared with the remote sensing image of the deep model,respectively.The extraction area of the field in the study area was more complete and the accuracy was higher.[Conclusions]Multi-scale and DESTIN constraints further improved the semantic segmentation problem of fields,which was helpful to improve the accuracy of field recognition in high-resolution remote sensing images.

关 键 词：语义分割 多尺度分割 DESTIN分割 农田田块提取 高分遥感影像 

分 类 号：S127[农业科学—农业基础科学] TP391[自动化与计算机技术—计算机应用技术]