基于卷积神经网络的高分六号卫星多光谱图像压缩

doi:10.19678/j.issn.1000-3428.0064845

计算机工程 ›› 2023, Vol. 49 ›› Issue (9): 287-294. doi: 10.19678/j.issn.1000-3428.0064845

基于卷积神经网络的高分六号卫星多光谱图像压缩

朱孟栩¹^,², 张文豪¹^,²^,*, 李国洪¹^,², 顾行发³, 余涛³^,⁴, 郑逢杰⁵, 张丽丽³^,⁴, 吴俣⁶, 邴芳飞¹^,², 唐健雄¹^,²

1. 北华航天工业学院遥感信息工程学院, 河北廊坊 065000
2. 河北省航天遥感信息处理与应用协同创新中心, 河北廊坊 065000
3. 中国科学院空天信息创新研究院遥感卫星应用国家工程实验室, 北京 100094
4. 中科空间信息(廊坊)研究院, 河北廊坊 065001
5. 航天工程大学航天信息学院, 北京 101416
6. 天津大学地球系统科学学院, 天津 300072

收稿日期:2022-05-30 出版日期:2023-09-15 发布日期:2023-09-14
通讯作者: 张文豪
作者简介:
朱孟栩（1996—），男，硕士研究生，主研方向为图像压缩、定量遥感
李国洪，教授、博士
顾行发，研究员、博士
余涛，研究员、博士
郑逢杰，副教授、博士
张丽丽，助理研究员、博士
吴俣，副教授、博士
邴芳飞，硕士研究生
唐健雄，硕士研究生
基金资助:
国家自然科学基金(41907192); 河北省自然科学基金(D2020409003); 河北省高等学校科学技术研究项目(ZD2021303); 北华航天工业学院博士科研启动基金(BKY-2021-31); 高分辨率对地观测系统重大专项(30-Y30F06-9003-20/22); 国家重点研发计划(2019YFE0127300); 国家重点研发计划(2019YFE0126600); 民用航天预研项目(D040102); 国防基础科研项目(JCKY2020908B001)

GF-6 Multispectral Image Compression Based on Convolutional Neural Network

Mengxu ZHU¹^,², Wenhao ZHANG¹^,²^,*, Guohong LI¹^,², Xingfa GU³, Tao YU³^,⁴, Fengjie ZHENG⁵, Lili ZHANG³^,⁴, Yu WU⁶, Fangfei BING¹^,², Jianxiong TANG¹^,²

1. School of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, Hebei, China
2. Heibei Spacer Remote Sensing Information Processing and Application of Collaborative Innovation Center, Langfang 065000, Hebei, China
3. National Engineering Laboratory of Remote Sensing Satellite Application, Institute of Aerospace Information Innovation, Chinese Academy of Sciences, Beijing 100094, China
4. China Academy of Spatial Information (Langfang), Langfang 065001, Hebei, China
5. School of Aerospace Information, Aerospace Engineering University, Beijing 101416, China
6. School of Earth System Science, Tianjin University, Tianjin 300072, China

Received:2022-05-30 Online:2023-09-15 Published:2023-09-14
Contact: Wenhao ZHANG

摘要/Abstract

摘要：

高分六号多光谱图像的空间冗余和谱间冗余较高，为了降低高分六号多光谱图像所占用的存储空间，提高国产高分多光谱图像的压缩效率，提出一种基于卷积神经网络的端到端多光谱图像压缩模型SMIC。SMIC模型由自编码器、量化结构、熵编码3个部分组成。自编码器通过卷积下采样提取图像的特征，降低数据的空间维度。量化结构采用多进制量化将特征矩阵离散化，减少图像压缩过程中的信息损失。熵编码采用高斯混合模型进行编码，降低码流，减少图像所占用的存储空间。实验结果表明：在相同码率下SMIC模型的高分六号多光谱图像压缩效果明显优于传统图像压缩算法JPEG，重建图像的质量明显提高，图像的峰值信噪比较JPEG高约2 dB，且SMIC重建图像的误差值主要集中在[-100, 100]范围内，区间占比达到80%以上；SMIC模型重建图像的NDVI与原始图像NDVI的决定系数$ {R}^{2} $为0.93；SMIC模型的冬小麦提取准确率为87.16%，误检率为4.47%，冬小麦提取结果验证了SMIC模型能够满足部分定量遥感的应用需求。

关键词: 图像压缩, 卷积神经网络, 高分六号, 多光谱图像, 自编码器

Abstract:

The spatial and inter-spectral redundancy of the multispectral images of Gaofen No.6 (GF-6) are high.To reduce the storage space occupied by these images and improve their compression efficiency, an end-to-end multispectral image compression model SMIC based on a convolutional neural network is proposed.The SMIC model consists of three parts: self encoder, quantization structure, and entropy coding.The self encoder extracts image features through convolutional down sampling to reduce the spatial dimension of the data.The quantization structure uses the multi-band quantization structure to discretize the feature matrix, reducing information loss in the image compression process.The entropy coding adopts the Gaussian mixture model to reduce the code stream and storage space occupied by the image.Experimental results at the same bit rate show that the compression effect of the SMIC model's GF-6 multispectral images is significantly better than that of the traditional JPEG image compression algorithm, and the quality of the reconstructed image is significantly improved.The Peak Signal-to-Noise Ratio (PSNR) of the image is approximately 2 dB higher than that of JPEG, and the error value of the reconstructed image is mostly within [-100, 100], accounting for more than 80%.The determination coefficient R² between the Normalized Difference Vegetation Index (NDVI) extracted from the reconstructed image and NDVI of the original image is 0.93.The quantitative remote sensing results of a winter wheat extraction experiment show an SMIC model accuracy of 87.16%, and the false detection rate is 4.47%.The results of winter wheat extraction show that the SMIC model can meet the application requirements of partial quantitative remote sensing.

Key words: image compression, convolutional neural network, Gaofen No.6(GF-6), multispectral image, autoencoder

朱孟栩, 张文豪, 李国洪, 顾行发, 余涛, 郑逢杰, 张丽丽, 吴俣, 邴芳飞, 唐健雄. 基于卷积神经网络的高分六号卫星多光谱图像压缩[J]. 计算机工程, 2023, 49(9): 287-294.

Mengxu ZHU, Wenhao ZHANG, Guohong LI, Xingfa GU, Tao YU, Fengjie ZHENG, Lili ZHANG, Yu WU, Fangfei BING, Jianxiong TANG. GF-6 Multispectral Image Compression Based on Convolutional Neural Network[J]. Computer Engineering, 2023, 49(9): 287-294.

https://www.ecice06.com/CN/Y2023/V49/I9/287

图/表 10

图1 数据预处理流程

Fig.1 Data preprocessing procedure

图2 SMIC模型整体技术路线

Fig.2 Overall technique flowchart of SMIC model

图3 SMIC模型结构

Fig.3 Structure of SMIC model

图4 模型训练信息损失曲线

Fig.4 Loss curve of model training information

图5 不同码率下的测试数据PSNR值

Fig.5 PSNR values of test data under different code rates

图6 重建图像比较

Fig.6 Reconstruction images comparison

图7 不同压缩算法重建图像的DN值对比

Fig.7 Comparison of DN values of reconstructed images using different compression algorithms

图8 不同压缩算法重建图像的误差统计直方图

Fig.8 Error statistical histograms of reconstructed images using different compression algorithms

图9 归一化植被指数和冬小麦反演结果

Fig.9 Normalized vegetation indexes and inversion results of winter wheat

图10 NDVI散点图对比

Fig.10 NDVI scatter diagram comparison

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