改进区间二型模糊神经网络的遥感图像分割方法  

Improved Interval Type-2 Fuzzy Neural Network for Remote Sensing Image Segmentation

作　　者：王春艳 王子康 WANG Chunyan;WANG Zikang(School of Software,Liaoning Technical University,Huludao 125105,China)

机构地区：[1]辽宁工程技术大学软件学院,葫芦岛125105

出　　处：《地球信息科学学报》2025年第2期522-535,共14页Journal of Geo-information Science

基　　金：国家自然科学基金青年科学基金项目(41801368);辽宁省教育厅基本科研项目(青年项目)(LJKOZ2021154)。

摘　　要：【目的】高分辨率遥感图像具有丰富和精细的空间信息,但其丰富的细节却模糊了不同土地覆盖类型间的界限,增加了分割的模糊性和不确定性。针对遥感图像分割领域的这一问题,本文提出一种改进区间二型模糊神经网络的新型遥感图像分割方法。【方法】该方法通过引入空间邻域信息及模型混合策略,构建混合回归隶属函数,实现对数据复杂特征的精细表达,以提升模型的适应性和特征提取能力;通过构建混合回归隶属函数的不确定区域来映射模糊和不确定的遥感数据特征,提高模型的鲁棒性;使用全连接神经网络结构进行特征整合,进一步增强模型的学习能力,同时引入焦点损失函数缓解类别不平衡问题的影响。【结果】在WHDLD和Potsdam数据集上进行的地物类别分割实验中,提出方法的分割精度优于DeepLab v3+和UNet++,与区间二型模糊神经网络方法相比,总体精度平均提升了8.31%和10.48%,Kappa值提升14.07%和14.59%,F1-score提升16.36%和12.31%。【结论】实验结果表明,提出方法成功应对了遥感图像分割中的模糊性和不确定性,有效缓解了土地覆盖分割中区域噪声造成的影响,具有分割精度高、泛化能力强等特点。[Objectives]High-resolution remote sensing images offer a wealth of detailed spatial information.However,this abundance of detail can blur the boundaries between different land cover types,thereby increasing the ambiguity and uncertainty of segmentation.To address this challenge in remote sensing image segmentation,this paper introduces an innovative segmentation method based on an improved interval type-2 fuzzy neural network.[Methods]By leveraging spatial neighborhood information and a model mixing strategy,a hybrid regression membership function is constructed to enable the precise representation of complex data features,thereby enhancing the model's adaptability and feature extraction capability.The uncertain region of the hybrid regression membership function is designed to map the fuzzy and uncertain features of remote sensing data,improving the model's robustness.The proposed approach utilizes a fully connected neural network architecture to enhance the model's capacity for feature integration and learning while incorporating a focal loss function to address the effects of class imbalance.[Results]In land cover segmentation experiments conducted on the WHDLD and Potsdam datasets,the proposed method significantly outperformed DeepLab v3+and UNet++.The proposed method achieved average overall accuracy improvements of 8.31%and 10.48%,Kappa coefficient enhancements of 14.07%and 14.59%,and F1 score increases of 16.36%and 12.31%,compared to the interval type-2 fuzzy neural network.[Conclusions]The results demonstrate that the proposed method effectively addresses ambiguity and uncertainty in remote sensing image segmentation,significantly mitigating the impact of regional noise on land cover segmentation while achieving high segmentation accuracy and robust generalization capabilities.

关 键 词：遥感图像分割 模糊性 不确定区域 高分辨率 区间二型 神经网络 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置] TP183[自动化与计算机技术—控制科学与工程]