快照马赛克高光谱图像CCSDS压缩器的FPGA实现  

FPGA implementation of CCSDS compressor for snapshot mosaic hyperspectral images

作　　者：吕德远 黄浩文 曾珉 曾旭 赵志刚 LV Deyuan;HUANG Haowen;ZENG Min;ZENG Xu;ZHAO Zhigang(College of New Materials and New Energies,Shenzhen Technology University,Shenzhen 518118,China;College of Physics and Optoelectronic Engineering,Shenzhen University,Shenzhen 518052,China)

机构地区：[1]深圳技术大学新材料与新能源学院,广东深圳518118 [2]深圳大学物理与光电工程学院,广东深圳518052

出　　处：《光学精密工程》2022年第8期883-893,共11页Optics and Precision Engineering

基　　金：横向课题项目(No.20200202002);校企合作项目(No.2018010802012);广东省科技创新战略专项资金项目(No.pdjh2020b0526);深圳技术大学校级科研项目(No.2020028555301020,No.JSZZ2020006);研究生校企合作项目(No.XQHZ202017,No.XQHZ202008,No.XQHZ202102,No.XQHZ202120)。

摘　　要：准确的云分类模型对气象监测有重要的意义,传统机器学习云分类模型依赖手工特征提取,容易受噪声数据影响,模型泛化能力较差。深度网络分类模型能自动学习图像深度特征,但是对于图像边缘与细节分类效果不佳。本文针对上述问题进行研究。首先提取Himawari-8卫星云图光谱特征、纹理特征用以训练模糊支持向量机(Fuzzy Support Vector Machine,FSVM)模型;同时利用不同通道云图训练深度网络,学习云图深度特征;最后,根据不同模型特性,训练元分类器对各模型输出进行融合,设计了一种基于深度网络与FSVM集成学习的云分类方法,该方法综合不同模型优势,利用不同模型间的互补性提高云分类结果的鲁棒性和可信度。相比单独使用FSVM或深度网络的分类模型,本文集成学习方法在众多评价指标中有更好的表现,平均命中率、平均误报率和平均临界成功指数分别达到0.9245、0.0796、0.8581;与其它云分类模型相比,本文方法也有更好的分类效果;在具体案例测试中也发现,该方法对于不同云类混合区有更高的识别精度,而且能更加准确的识别云团边缘及细节。本文模型能够满足云分类模型稳定可靠、高精度、泛化性能强的要求。To solve the problem of large data volume and high transmission bandwidth requirement of snapshot mosaic hyperspectral images,the latest CCSDS 123.0-B-2 multispectral/hyperspectral lossless and near-lossless compression international standard is adopted to realize the FPGA-based lossless and near-lossless compression of snapshot mosaic hyperspectral images.By improving the BIP(Band-Interleaved Pixels)sequencing algorithm,a snapshot mosaic hyperspectral sample can be dynamically processed per clock cycle to 11 times higher of data throughput,and the problems of pipelining and parallelization difficulties and slow processing speed in the FPGA hardware implementation of snapshot mosaic hyperspectral image compression of this international standard have been effectively solved.The experimental results show that the overall logic resource usage of the implemented compressor is less than 10%after the layout and wiring of the FPGA on Xilinx XC7Z020CLG-4 platform,and the compression of a hyperspectral image can be completed in about 22 ms under 100 MHz system clock,with lossless compression performance between 2.66~4.30 bits/samples and the performance near-lossless compression is between 1.01~3.70 bits/samples,which can meet the application requirements of snapshot mosaic hyperspectral imaging technology in wireless handheld and unmanned airborne fields.

关 键 词：高光谱图像压缩 快照马赛克高光谱成像 CCSDS 123.0-B-2 FPGA实现 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]