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多层级二值模式下的卷积网络高光谱影像分类

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针对如何改善小样本下卷积神经网络(convolution-al neural network,CNN)的高光谱影像分类效果,结合人工设计特征,提出多层级二值模式下的卷积网络高光谱影像分类方法.该方法先采用多层级二值模式进行高光谱影像纹理特征提取,从不同尺度反映影像的局部细节信息,生成更具鉴别性的特征.以此纹理先验特征为基础,利用卷积神经网络对其进行进一步的自动学习与分类.为验证该分类方法的有效性,选取空间分辨率、地物覆盖类型不同的PaviaU和Salinas高光谱影像实验数据,分别对局部二值模式、多层级二值模式、Gabor、GLCM(gray-level co-occurrence matrix)进行特征判别能力分析,并针对各特征开展卷积网络分类实验.结果表明,多层级二值模式下卷积网络分类总体分类精度分别达到91.98％、98.15％,优于纯光谱、Gabor等分类.
Hyperspectral Image Classification Based on Convolutional Neural Network and Multi-layer Binary Pattern
The hyperspectral image classification method based on convolutional neural network and multi-layer binary pattern is proposed to improve the classification effect of hy-perspectral image of convolutional neural network with small samples via the artificial designed features. Firstly,the textur-al features are expressed using multi-layer binary pattern re-flecting the local details from different scales. On this basis,the deeper automatic learning and classification are carried out using convolution neural network. In order to verify the effec-tiveness of the proposed method,PaviaU and Salinas with dif-ferent spatial resolution and ground cover are used. Five kinds of features such as the spectra,the local binary pattern,Ga-bor,etc.,are employed for feature discriminative ability anal-ysis and hyperspectral image classification. The overall classi-fication accuracies with the proposed method respectively reach 91.98％ and 98.15％,which is superior than the other methods.

hyperspectral image classificationconvolutional neural networktextural featuremulti-layer binary patternlocal binary pattern

职露、胡涛、尹宾宾、余旭初、王彦坤

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郑州师范学院地理与旅游学院,河南郑州,450000

信息工程大学数据与目标工程学院,河南郑州,450000

天津飞眼无人机科技有限公司,天津,300000

深圳职业技术大学物联网研究院,广东深圳,518000

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高光谱影像分类 卷积神经网络 纹理特征 多层级二值模式 局部二值模式

国家自然科学基金国家自然科学基金青年基金福建省自然资源厅科技创新项目

3197172342001389KY-080000-04-2021-030

2024

10.14188/j.2095-6045.2022568

测绘地理信息
武汉大学

测绘地理信息

CSTPCD
影响因子：0.563
ISSN：1007-3817
年,卷(期)：2024.49(5)
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