首页|基于改进2DCNN的高光谱遥感图像处理研究

基于改进2DCNN的高光谱遥感图像处理研究

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  • 国家科技期刊平台
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  • NSTL
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针对传统遥感图像处理中的时间成本和人工成本高、效率低等问题,以提高遥感高光谱图像分类中的处理速度、精度,降低参数量为目标,提出改进的2DCNN模型En-De-2CP-2DCNN.首先,使用1DCNN、2DCNN与3DCNN在Pavia University HSI数据集上分别进行分类实验,对比分析各自优缺点.其次,在保持较快的处理速度和不增加模型参数量的前提下,选择2DCNN为基础模型,参考SegNet的Encoder-Decoder结构,融入双卷积池化思想进行基础模型改进,同时优化学习策略.结果表明:En-De-2CP-2DCNN模型F1为99.96%,达到3DCNN的同等水平(99.36%),较改进前(97.28%)提高2.68个百分点;处理速度(5s/epoch)和1DCNN位于同一量级,快于3DCNN(96s/epoch);参数量(2.01MB)较改进前降低了1.54MB,虽高于3DCNN(316KB),但远低于1DCNN(19.21MB).En-De-2CP-2DCNN模型在处理速度和参数量方面的改进,有利于进一步实现移动端的轻量化部署.
Hyperspectral remote sensing image processing based on enhanced 2DCNN
To address the problems of high cost of time and labor and low efficiency frustrated traditional remote sensing image processing,an improved 2DCNN(2D Convolutional Neural Network)model abbreviated as En-De-2CP-2DCNN was proposed,with the purpose to improve the processing speed,accuracy and reduce the number of parame-ters in the classification of remote sensing Hyperspectral Images(HSI).First,1DCNN,2DCNN and 3DCNN were used to carry out classification experiments on Pavia University HSI dataset,and their advantages and disadvantages were compared and analyzed.Second,under the premise of maintaining fast processing speed without increasing model pa-rameters,the 2DCNN was selected as the basic model,which was then improved with referring to the Encoder-Decoder structure of SegNet and integrating the idea of double convolutional pooling,and the learning strategy was optimized.The results show that the F1-score of the proposed En-De-2CP-2DCNN model is 99.96%,reaching the same level of 3DCNN(99.36%),which is 2.68 percentage points higher than that before improvement(97.28%);the processing speed(5 s/epoch)is comparable to that of 1DCNN and faster than 3DCNN(96 s/epoch);the amount of parameters is reduced from 3.55 MB to 2.01 MB,which is higher than 3DCNN(316 KB)but much lower than 1DCNN(19.21 MB).The proposed En-De-2CP-2DCNN model realizes accurate,fast and lightweight processing of remote sensing hy-perspectral images.In particular,the improvement in processing speed and parameter amount is conducive to further realizing the lightweight deployment of mobile terminals.

convolutional neural network(CNN)deep learningremote sensing image processinghyperspectralimage classification

赵章红、张丹、胡昊、陈琳、常升龙

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黄河水利职业技术学院 测绘工程学院,开封,475004

河南省测绘实景三维技术工程研究中心,开封,475004

河南恒茂创远科技股份有限公司,郑州,450000

河南农业大学 农学院,郑州,450046

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卷积神经网络 深度学习 遥感图像处理 高光谱 图像分类

河南省高等学校重点科研项目河南省高等学校重点科研项目河南省高等教育教学改革研究与实践项目测绘地理信息职业教育研究课题高分辨率对地观测系统国家科技重大专项教育部科技发展中心创新应用课题教育部科技发展中心创新应用课题河南省职业教育教学改革研究与实践重大项目

21B4200022221022100922021SJGLX6672021CHZD0280-Y50G19-9001-22/23ZJXF2022083ZJXF20220412023-ZYJYZD-003

2024

10.13878/j.cnki.jnuist.20230809001

南京信息工程大学学报
南京信息工程大学

南京信息工程大学学报

CSTPCD北大核心
影响因子：0.737
ISSN：1674-7070
年,卷(期)：2024.16(1)
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