基于光谱纹理特征融合和神经网络的地表发射率获取方法

doi:10.7523/j.issn.2095-6134.2018.01.014

中国科学院大学学报 ›› 2018, Vol. 35 ›› Issue (1): 102-108.DOI: 10.7523/j.issn.2095-6134.2018.01.014

基于光谱纹理特征融合和神经网络的地表发射率获取方法

徐开发^1,2, 雷斌¹, 张月婷¹

1. 中国科学院电子学研究所空间信息处理与应用系统技术重点实验室, 北京 100190;
2. 中国科学院大学, 北京 100049

收稿日期:2016-12-22 修回日期:2017-03-14 发布日期:2018-01-15
通讯作者: 徐开发
基金资助:
国家部委预研项目资助

Retrieval of land surface emissivity using spectral and texture features based on neural network

XU Kaifa^1,2, LEI Bin¹, ZHANG Yueting¹

1. Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China

Received:2016-12-22 Revised:2017-03-14 Published:2018-01-15

摘要/Abstract

摘要： 地表发射率是热红外遥感中最为关键的参数之一，在热红外遥感的定量化研究、地表能量平衡和地物填图等领域起着重要作用。但是，从热红外遥感数据反演地表发射率需要求解病态方程。提出一种基于神经网络模型逐像元获取地表发射率的方法。该方法基于MODIS 可见光通道的反射率数据提取纹理特征，将纹理信息和可见光光谱信息进行融合作为神经网络的输入特征，发射率作为输出。获取的地表发射率结果和MODIS的标准发射率产品对比，平均误差为0.002。该方法直接建立地表发射率和地表反射率的关系，为单通道热红外卫星精确获取地表温度和发射率提供依据和可能。

关键词: 地表发射率, 神经网络, 光谱和纹理特征, MODIS

Abstract: Land surface emissivity is one of the most important parameters in thermal infrared remote sensing and plays a significant role in the quantitative study of thermal infrared remote sensing, the surface energy balance, and feature mapping. Retrieving the land surface emissivity from thermal infrared remote sensing data is a challenge because it presents an ill-posed problem. In this work, a method, which takes advantage of spectral and texture features of many visible channels available in the moderate resolution imaging spectroradiometer (MODIS) data and is based on back-propagation neural network to obtain land surface emissivity pixel-by-pixel, is presented. The method obtains the land surface emissivity map without the categorization of the land cover and the analysis indicates that the average error, compared to MODIS emissivity product, is within 0.002. It builds a direct relationship between reflectance data and emissivity data, and provides the possibility of obtaining precise emissivity data through the single channel thermal infrared satellite.

Key words: land surface emissivity, neural network, spectral and texture features, MODIS

中图分类号:

O657.33

徐开发, 雷斌, 张月婷. 基于光谱纹理特征融合和神经网络的地表发射率获取方法[J]. 中国科学院大学学报, 2018, 35(1): 102-108.

XU Kaifa, LEI Bin, ZHANG Yueting. Retrieval of land surface emissivity using spectral and texture features based on neural network[J]. , 2018, 35(1): 102-108.

参考文献

[1] Song J H, Chen S B, Sun K, et al. Visualization to surface temperature retrieval using matlab[J]. Microcomputer Information, 2008, 24:132.
[2] BOREL C. Error analysis for a temperature and emissivity retrieval algorithm for hyperspectral imaging data[J]. International Journal of Remote Sensing Remote, 2008,29(17/18):5029-5045.
[3] Caselles V. Thermal remote sensing of land surface temperature from satellites:current status and future prospects[J]. Remote Sensing Reviews, 1995,12(3):175-224.
[4] Jiménez Muñoz J C, Sobrino J A. A generalized single channel method for retrieving land surface temperature from remote sensing data[J]. Journal of Geophysical Research, 2003,108(D22):2015-2023.
[5] Islam T,Hulley G C, Malakar N K, et al. A physics-based algorithm for the simultaneous retrieval of land surface temperature and emissivity from VⅡRS thermal infrared data[J]. IEEE Trans Geosci Remote Sens, 2017,108(1):563-576.
[6] Qian Y G, Wang N, Ma L L, et al. Evaluation of temperature and emissivity retrieval using spectral smoothness method for low-emissivity materials[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016,9:4307-4315.
[7] Emami H, Safari A, Mojaradi B. Fusion methods for land surface emissivity and temperature retrieval of the landsat data continuity mission data[J]. IEEE Trans Geosci Remote Sens, 2016,54:3842-3855.
[8] Zhang X, Ding F, Peng X, et al. Fast retrieval of land surface emissivity from landsat data through IDL programming[C]//2014 Third International Workshop on Earth Observation and Remote Sensing Applications (EORSA). IEEE,2014:76-80.
[9] Wang N, Wu H, Nerry F, et al. Temperature and emissivity retrievals from hyperspectral thermal infrared data using linear spectral emissivity constraint[J]. IEEE Trans Geosci Remote Sens, 2011,49:1291-1303.
[10] Huang Q N, Guo H D, Xi X H et al. Improved method of land surface emissivity retrieval from Landsat TM/ETM+data[C]//2012 IEEE International Geoscience and Remote Sensing Symposium. IEEE,2012:4206-4208.
[11] Gillespie A, Rokugawa S, Matsunaga T, et al. A temperature and emissivity separation algorithm for advanced spaceborne thermal emission and reflection radiometer (ASTER) images[J]. IEEE Trans Geosci Remote Sens, 2002,36(4):1113-1126.
[12] Sobrino J A, Oltra-Carrió R, Jiménez-Muñoz J C, et al. Emissivity mapping over urban areas using a classification-based approach:application to the dual-use european security IR experiment (DESIREX)[J]. International Journal of Applied Earth Observation and Geoinformation, 2012,18(1):141-147.
[13] Caicedo J P R, Verrelst J, Munoz-Mari J, et al. Toward a semiautomatic machine learning retrieval of biophysical parameter[J]. IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing,2014,7(4):1249-1259.
[14] Mao K B, Tang H J, Li L Y, et al. An NN algorithm for retrieving land surface temperature and emissivity from MODIS data[J]. Remote Sensing Information, 2007(4):9-15.
[15] Mao K B, Shi J C, Tang H J, et al. A neural network technique for separating land surface emissivity and temperature from ASTER imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008,46(1):200-208.
[16] Wang N. Simultaneous retrieval of land surface temperature, emissivity and atmospheric profiles from hyperspectral thermal infraed data[D]. Beijing:University of Chinese Academy of Sciences,2011.
[17] Xiong X, Wenny B N, Barnes W D. Overview of NASA earth observing systems terra and aqua moderate resolution imaging spectroradiometer instrument calibration algorithms and on-orbit performance[J]. Remote Sens, 2009, 3.
[18] Oltra-Carrió R, Sobrino J A, Franch B, et al. Land surface emissivity retrieval from airborne sensor over urban areas[J]. Remote Sensing of Environment, 2012,123:298-305.
[19] Musci M, Feitosa R Q, Costa G A O P, et al. Assessment of binary coding techniques for texture characterization in remote sensing imagery[J]. IEEE Geosci Remote Sens Letters, 2013,10(6):1607-1611.
[20] Devi K, Gupta P, Grover D, et al. An effective texture feature extraction approach for iris recognition system[C]//20162nd International Conference on Advances in Computing, Communication, & Automation. 2016:1-5.
[21] Yang P P, Hou Z T, Liu X L, et al. Texture feature extraction of mountain economic forest using high spatial resolution remote sensing images[C]//2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE,2016:3156-3159.
[22] Edgar A. Corzo M, Arthur A N. Side scan sonar images attributes characterization for seabed mapping[C]//2015 IEEE/OES Acoustics in Underwater Geosciences Symposium (RIO Acoustics). de Janeiro:IEEE,2015:1-4.

基于光谱纹理特征融合和神经网络的地表发射率获取方法

Retrieval of land surface emissivity using spectral and texture features based on neural network

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