Institute of Marxism and Research, Jiangxi Police College, Nanchang, Jiangxi 330000, China.
Comput Intell Neurosci. 2022 Apr 30;2022:4244675. doi: 10.1155/2022/4244675. eCollection 2022.
In this paper, an in-depth study of cross-media semantic matching and user adaptive satisfaction analysis model is carried out based on the convolutional neural network. Based on the existing convolutional neural network, this paper uses rich information. The spatial correlation of cross-media semantic matching further improves the classification accuracy of hyperspectral images and reduces the classification time under user adaptive satisfaction complexity. Aiming at the problem that it is difficult for the current hyperspectral image classification method based on convolutional neural network to capture the spatial pose characteristics of objects, the problem is that principal component analysis ignores some vital information when retaining a few components. This paper proposes a polymorphism based on extension Attribute Profile Feature (EMAP) Stereo Capsule Network Model for Hyperspectral Image Classification. To ensure the model has good generalization performance, a new remote sensing image Pan sharpening algorithm based on convolutional neural network is proposed, which increases the model's width to extract the feature information of the image and uses dilated instead of traditional convolution. The experimental results show that the algorithm has good generalization while ensuring self-adaptive satisfaction.
本文基于卷积神经网络对跨媒体语义匹配和用户自适应满意度分析模型进行了深入研究。在现有的卷积神经网络的基础上，本文利用丰富的信息。跨媒体语义匹配的空间相关性进一步提高了高光谱图像的分类精度，并降低了用户自适应满意度复杂性下的分类时间。针对当前基于卷积神经网络的高光谱图像分类方法难以捕捉物体空间姿态特征的问题，主成分分析在保留少数分量时忽略了一些重要信息。本文提出了一种基于扩展属性剖面特征（EMAP）立体胶囊网络模型的高光谱图像分类方法。为了保证模型具有良好的泛化性能，提出了一种新的基于卷积神经网络的遥感图像 Pan 锐化算法，该算法增加了模型的宽度以提取图像的特征信息，并使用扩张代替传统卷积。实验结果表明，该算法在保证自适应满意度的同时具有良好的泛化能力。
