摘要 为了提高多源遥感图像在低分辨率成像环境下的检测识别能力,提出基于随机森林的多源遥感图像特征融合方法。采用增强型D-ASPP结构滤波分析的方法,建立低分辨率多源遥感图像滤波成像分析模型和三维成像模型,通过提取多源遥感图像的多分辨边缘特征信息,建立低分辨率多源遥感图像的特征分割模型,利用空间维度匹配进行遥感图像的特征匹配,利用灰度直方图的细节层次分布特征,构建多源遥感图像的灰度直方图模型及随机森林学习模型,采用低分辨特征分解和信息增强的方法进行图像的特征融合,实现多源遥感图像的多模状态融合和优化成像,提高多源遥感图像的特征融合和成像质量。仿真结果表明,采用该方法进行多源遥感图像融合的对比度较高,图像融合输出的信噪比较高,提高了多源遥感图像的检测识别能力。 In order to improve the detection and recognition ability of multi-source remote sensing images in low-resolution imaging environment,a multi-source remote sensing image feature fusion method based on random forest is proposed.The enhanced A-ASPP structure filter analysis method is used to establish a low-resolution multi-source remote sensing image filter imaging analysis model and a three-dimensional imaging model.By extracting the multi-resolution edge feature information of the multi-source remote sensing image,the low-resolution multi-source remote sensing image is established The feature segmentation model uses spatial dimension matching for remote sensing image feature matching,and uses the level of detail distribution characteristics of the grayscale histogram to construct a grayscale histogram model and a random forest learning model for multi-source remote sensing images,using low-resolution feature decomposition and The method of information enhancement performs image feature fusion,realizes multi-mode state fusion and optimized imaging of multi-source remote sensing images,and improves the feature fusion and imaging quality of multi-source remote sensing images.Simulation results show that this method has higher contrast and higher signal-to-noise ratio,which improves the detection and recognition ability of multi-source remote sensing images.
出处 《自动化与仪器仪表》 2021年第4期52-55,共4页 Automation & Instrumentation
基金 国家自然科学基金项目“遥感数据与植被生态系统碳循环模型的同化研究”(No.40971221)。
