不同空间分辨率高光谱遥感数据对蚀变矿物信息提取的影响
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摘要
高光谱遥感由于其精细的光谱分辨率,在定量分析物质成分上独具优势,因此广泛应用于提取蚀变矿物信息。探讨了不同空间分辨率高光谱遥感数据对蚀变矿物信息提取的影响,采用最邻近插值法、双线性插值法和三次卷积插值法3种重采样方法对美国Cuprite矿区空间分辨率为20 m的AVIRIS影像做空间尺度扩展,分别扩展到空间分辨率为25,30,35,40,45,50 m。采用SAM分类方法从不同空间分辨率影像中提取蚀变矿物信息,使用混淆矩阵评价提取结果。一方面比较不同重采样方法对后期蚀变矿物信息提取精度产生的影响;另一方面比较不同空间分辨率对高光谱遥感影像蚀变矿物信息提取精度的影响。结果表明:①采用不同的重采样方法做空间尺度扩展,会影响后期蚀变矿物信息提取的精度,但是数值变化相对较小。相比之下,最邻近插值法重采样下影像蚀变矿物信息提取的精度稍好一些。②在中等空间分辨率(20～50 m)范围内,基于50 m空间分辨率的高光谱影像,蚀变信息提取的总体精度和Kappa系数较20 m的明显下降。其中最邻近插值法重采样下的总体精度和Kappa系数分别下降了7.94%,0.09;双线性插值法重采样下的总体精度和Kappa系数分别下降了6.87%,0.08;三次卷积插值法重采样下的总体精度和Kappa系数分别下降了6.68%,0.08。较高空间分辨率影像的总体精度和Kappa系数整体上均高于较低空间分辨率的情形。
Hyperspectral remote sensing has high spectral resolution and has unique advantages in the quantitative analysis of material composition. Therefore, it is widely used to identify alteration mineral information. This paper discusses the effects of different spatial resolution hyperspectral remote sensing data on the extraction of alteration minerals information. The paper applies three resampling methods of Nearest Neighbor interpolation, Bilinear interpolation and Cubic Convolution interpolation to extend the spatial scale of the AVIRIS hyperspectral image with a spatial resolution of 20 m in the Cuprite Mining District in the United States and extend it to spatial resolution image data of 25,30,35,40,45 m and 50 m. On the one hand, the paper compares the effects of different resampling methods for the accuracy of alteration information extraction. On the other hand, the paper employs the SAM(Spectral Angle Mapper) classification method to extract alteration minerals information from the different spatial resolution images and then uses a confusion matrix to verify the extraction results and compares the overall accuracy. The results show that:(1) Different resampling methods used to extent the spatial scale affect the accuracy of alteration information extraction, but the numerical changes are relatively small. Among them, the Nearest Neighbor interpolation method is slightly better.(2) In the medium spatial resolution(20-50 m) range, the Overall Accuracy and Kappa coefficient of the alteration information extraction decreases significantly. With the three resampling methods, the overall accuracy of the alteration information extraction drops by 7.94%, 6.87%, 6.68%, and the Kappa coefficient decreases by 0.09, 0.08,0.08, respectively. The overall accuracy of alteration information extraction from higher spatial resolution image is higher than that of lower spatial resolution. This shows that although the alteration information extraction depends on the fine spectral resolution of the hyperspectral image to quantitatively identify the components of minerals, it is generally affected by the spatial resolution as well.
Hyperspectral remote sensing has high spectral resolution and has unique advantages in the quantitative analysis of material composition. Therefore, it is widely used to identify alteration mineral information. This paper discusses the effects of different spatial resolution hyperspectral remote sensing data on the extraction of alteration minerals information. The paper applies three resampling methods of Nearest Neighbor interpolation, Bilinear interpolation and Cubic Convolution interpolation to extend the spatial scale of the AVIRIS hyperspectral image with a spatial resolution of 20 m in the Cuprite Mining District in the United States and extend it to spatial resolution image data of 25,30,35,40,45 m and 50 m. On the one hand, the paper compares the effects of different resampling methods for the accuracy of alteration information extraction. On the other hand, the paper employs the SAM(Spectral Angle Mapper) classification method to extract alteration minerals information from the different spatial resolution images and then uses a confusion matrix to verify the extraction results and compares the overall accuracy. The results show that:(1) Different resampling methods used to extent the spatial scale affect the accuracy of alteration information extraction, but the numerical changes are relatively small. Among them, the Nearest Neighbor interpolation method is slightly better.(2) In the medium spatial resolution(20-50 m) range, the Overall Accuracy and Kappa coefficient of the alteration information extraction decreases significantly. With the three resampling methods, the overall accuracy of the alteration information extraction drops by 7.94%, 6.87%, 6.68%, and the Kappa coefficient decreases by 0.09, 0.08,0.08, respectively. The overall accuracy of alteration information extraction from higher spatial resolution image is higher than that of lower spatial resolution. This shows that although the alteration information extraction depends on the fine spectral resolution of the hyperspectral image to quantitatively identify the components of minerals, it is generally affected by the spatial resolution as well.
引文
[1] 王利民,刘佳,高建孟,等.冬小麦面积遥感识别精度与空间分辨率的关系[J].农业工程学报,2016,32(23):152-160.
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[14] 林娜.高光谱遥感岩矿特征提取与分类方法研究[D].成都:成都理工大学,2011.
[15] Clark R N,Swayze G A,Livo K E,et al.Imaging spectroscopy:Earth and planetary remote sensing with the USGS Tetracorder and expert systems[J].Journal of Geophysical Research:Planets,2003,108(E12):1-44.
[16] 张周威,余涛,孟庆岩,等.空间重采样方法对遥感影像信息影响研究[J].华中师范大学学报:自然科学版,2013,47(3):426-430.
[17] 梁繁.云南中甸地区高光谱遥感矿物信息提取[D].成都:成都理工大学,2017.
[18] Zazi L,Boutaleb A,Guettouche M S.Identification and mapping of clay minerals in the region of Djebel Meni (Northwestern Algeria) using hyperspectral imaging,EO-1 Hyperion sensor[J].Arabian Journal of Geosciences,2017,10(11):252.
[19] 何中海,何彬彬.基于权重光谱角制图的高光谱矿物填图方法[J].光谱学与光谱分析,2011,31(8):2200-2204.
[20] Molan Y E,Refahi D,Tarashti A H.Mineral mapping in the Maherabad area,eastern Iran,using the HyMap remote sensing data[J].International Journal of Applied Earth Observation and Geoinformation,2014,27(B):117-127.
[21] 林娜,杨武年,刘汉湖.基于高光谱遥感的岩矿端元识别及信息提取研究[J].遥感信息,2011,26(5):114-117,99.
[22] 湛国毅,湛青青,吴玲,等.基于SAM分类方法遥感矿物信息提取研究[J].湖北民族学院学报:自然科学版,2016,34(1):84-86.
[23] Chen D,Stow D A,Gong P.Examining the effect of spatial resolution and texture window size on classification accuracy:An urban environment case[J].International Journal of Remote Sensing,2004,25(11):2177-2192.
[2] 张焕雪,李强子.空间分辨率对作物识别及种植面积估算的影响研究[J].遥感信息,2014,29(2):36-40,46.
[3] 徐凯健,田庆久,杨闫君,等.遥感土地覆被分类的空间尺度响应研究[J].地球信息科学学报,2018,20(2):246-253.
[4] 汪权方,许纪承,陈媛媛,等.遥感影像空间分辨率对居民地信息提取的影响[J].资源科学,2012,34(1):159-165.
[5] 张翠芬,余健,郝利娜,等.多尺度纹理及多光谱影像协同的遥感岩性识别方法[J].地质科技情报,2017,36(4):236-243.
[6] Underwood E C,Ustin S L,Ramirez C M.A comparison of spatial and spectral image resolution for mapping invasive plants in coastal california[J].Environmental Management,2007,39(1):63-83.
[7] Roth K L,Roberts D A,Dennison P E,et al.The impact of spatial resolution on the classification of plant species and functional types within imaging spectrometer data[J].Remote Sensing of Environment,2015,171:45-57.
[8] 阎继宁,周可法,王金林,等.基于SAM与SVM的高光谱遥感蚀变信息提取[J].计算机工程与应用,2013,49(19):141-146.
[9] 唐超,周可法,张楠楠,等.基于Landsat-8 OLI和ASTER数据集成和融合的矿化蚀变信息提取:以包古图斑岩型铜矿为例[J].地质科技情报,2018,37(6):211-217.
[10] 杨闫君,田庆久,占玉林,等.空间分辨率与纹理特征对多光谱遥感分类的影响[J].地球信息科学学报,2018,20(1):99-107.
[11] Chen X,Warner T A,Campagna D J.Integrating visible,near-infrared and short-wave infrared hyperspectral and multispectral thermal imagery for geological mapping at Cuprite,Nevada[J].Remote Sensing of Environment,2007,110(3):344-356.
[12] Meero F V D,Bakker W.Cross correlogram spectral matching:Application to surface mineralogical mapping by using AVIRIS data from Cuprite,Nevada[J].Remote Sensing of Environment,1997,61(3):371-382.
[13] Ashley R P,Abrams M J.Alteration mapping using multispectral images:Cuprite mining district,Esmeralda County,Nevada[J].Applied & Environmental Microbiology,1980,39(1):261.
[14] 林娜.高光谱遥感岩矿特征提取与分类方法研究[D].成都:成都理工大学,2011.
[15] Clark R N,Swayze G A,Livo K E,et al.Imaging spectroscopy:Earth and planetary remote sensing with the USGS Tetracorder and expert systems[J].Journal of Geophysical Research:Planets,2003,108(E12):1-44.
[16] 张周威,余涛,孟庆岩,等.空间重采样方法对遥感影像信息影响研究[J].华中师范大学学报:自然科学版,2013,47(3):426-430.
[17] 梁繁.云南中甸地区高光谱遥感矿物信息提取[D].成都:成都理工大学,2017.
[18] Zazi L,Boutaleb A,Guettouche M S.Identification and mapping of clay minerals in the region of Djebel Meni (Northwestern Algeria) using hyperspectral imaging,EO-1 Hyperion sensor[J].Arabian Journal of Geosciences,2017,10(11):252.
[19] 何中海,何彬彬.基于权重光谱角制图的高光谱矿物填图方法[J].光谱学与光谱分析,2011,31(8):2200-2204.
[20] Molan Y E,Refahi D,Tarashti A H.Mineral mapping in the Maherabad area,eastern Iran,using the HyMap remote sensing data[J].International Journal of Applied Earth Observation and Geoinformation,2014,27(B):117-127.
[21] 林娜,杨武年,刘汉湖.基于高光谱遥感的岩矿端元识别及信息提取研究[J].遥感信息,2011,26(5):114-117,99.
[22] 湛国毅,湛青青,吴玲,等.基于SAM分类方法遥感矿物信息提取研究[J].湖北民族学院学报:自然科学版,2016,34(1):84-86.
[23] Chen D,Stow D A,Gong P.Examining the effect of spatial resolution and texture window size on classification accuracy:An urban environment case[J].International Journal of Remote Sensing,2004,25(11):2177-2192.
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