PFWG改进的CNN多光谱遥感图像分类

PFWG Improved CNN Multispectra Remote Sensing Image Classification

为了实现在遥感图像处理过程中准确地提取到有效地物信息,缩短分类用时,将卷积神经网络(CNN)模型引入遥感图像地物分类,首先提出由图片模糊加权平均(PFWG)改进的CNN分类方法,利用模糊几何聚类算法作为预处理单元对实验样本进行特征规划,并对遥感地物信息进行多源特征决策,简化了分类过程,加快了CNN模型的收敛速度。实验结果表明,利用PFWG改进的CNN分类方法总体分类精度达到了93.73%;Kappa系数为0.94。该方法有效地弥补了CNN自身对遥感图像分类不够细腻、表达效果差的缺点,较好地完成了多光谱遥感图像分类任务,同时具备一定抗干扰能力。

In order to accurately achieve the effective ground information in the process of remote sensing image processing and shorten the classification time,the convolutional neural networks(CNN)model is introduced into the classification of remote sensing image features.First,the picture fuzzy weighted average(PFWG)improved CNN classification method is proposed.The fuzzy geometric clustering algorithm is used as a pre-processing unit to characterize the experimental samples,and for multi-source feature decision-making for remote sensing ground information.The classification process is simplified and the convergence of the CNN model is speeded up.The experimental results show that using PFWG improved CNN classification method,the overall classification accuracy reaches 93.73%,and the Kappa coefficient is 0.94.This method effectively compensates for the shortcoming of CNN itself which is not good enough for classification and has poor expression performance of remote sensing images.It has successfully completed an efficient classification task and has a certain anti-jamming capability.