北京测绘2025，Vol.39Issue(4)：528-533,6.DOI:10.19580/j.cnki.1007-3000.2025.04.021
基于机器学习的高分辨率遥感影像地理信息提取方法
A machine learning-based method for geographic information extraction from high-resolution remote sensing imagery
摘要
Abstract
To ensure the completeness of geographic information extraction from high-resolution remote sensing imagery,this paper proposed a machine learning-based method for geographic information extraction from high-resolution remote sensing images.The method utilizes the hue,intensity,and saturation(HIS)color space transformation technique to transform the color space of high-resolution remote sensing imagery,enhancing the separability of targets within the image.The transformed geographic image was then input into a convolutional attention mechanism network model,which integrated spatial and channel attention mechanisms to create a feature extraction module.This module extracted and fused image features,resulting in a feature map.The decoding layer uses a cascaded conditional random field(CRF)to process the feature map layer by layer,enhancing the correlation between features at different layers,ensuring feature richness,optimizing edge contours in the feature map,and improving spatial information to obtain the final geographic information extraction results.Test results show that after transformation,the image's contrast and average gradient are both above 0.933 and 0.942,respectively,indicating that the method ensures good transformation quality and can effectively extract target information from the image.关键词
机器学习/地理信息提取/特征图/级联条件随机场(CRF)/色调、亮度和饱和度(HIS)空间变换Key words
machine learning/geographic information extraction/feature map/cascaded conditional random field(CRF)/hue,intensity,and saturation(HIS)space transformation分类
天文与地球科学引用本文复制引用
王文龙,王可可..基于机器学习的高分辨率遥感影像地理信息提取方法[J].北京测绘,2025,39(4):528-533,6.基金项目
国家自然科学基金(42261074) （42261074）
