The Quality Assessment of Hymap Simulation Spaceborne Hyperspectral Data
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摘要: 为了有效、合理、客观地评价高光谱卫星数据质量, 充分发挥其在矿产及能源普查方面的作用, 进行了一系列研究.围绕最具代表性的3种载荷指标(几何空间分辨率、波谱分辨率及信噪比)的不同尺度, 从均方差异常、直方图异常、数据相关性异常、反射率曲线异常、信噪比参量以及该模拟数据的实际应用(蚀变信息提取和矿物填图)等多角度入手, 系统而全面地分析了模拟星载Hymap高光谱数据针对不同指标与尺度的影像质量效果.研究结果表明, 这3种载荷指标之间相互制约, 并随着空间分辨率和波谱分辨率的提高将降低图像的信噪比.当几何空间分辨率为15 m、波谱分辨率为15~20 nm, 同时信噪比≥350时, 就可以满足常规的矿物填图要求.Abstract: In order to evaluate the hyperspectral satellite data quality effectively, rationally and objectively to facilitate mineral and energy exploration, we carried out an in-depth study, centering on the most representative load indexes (geometric spatial resolution, spectral resolution and signal-to-noise ratio). systematically and comprehensively analysing the image quality effect on different loading indicators and scale of analog spaceborne Hymap hyperspectral data by multi-angle research methods including mean squared error (MSE) abnormalities, abnormal histogram, data related abnormalities, abnormal reflectivity curve, the signal-noise ratio (SNR) parameter and the practical application of the analog data (alteration information extraction and mineral mapping). Results suggest that the three load index restrict each other. With the improvement of spatial resolution and spectral resolution, SNR will reduce. When the geometric space resolution is 15 m, spectral resolution is 15-20 nm, and SNR≥350, it can satisfy the requirement of the conventional mineral mapping.
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Key words:
- analog spaceborne hyperspectral data /
- remote sensing /
- quality assessment /
- mineral
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图 1 航空Hymap数据(a)和模拟星载Hymap数据(b)
Fig. 1. Aviation Hymap data (a) and simulated spaceborne Hymap data (b)
图 2 不同空间分辨率(a)、光谱分辨率(b)和信噪比(c)模拟影像均方差曲线对比
Fig. 2. Mean square error curve comparing of different spatial resolution (a), spectrum resolution (b) and SNR (c) simulation images
图 3 不同空间分辨率(a)、波谱分辨率(b)和信噪比(c)的模拟影像Band1对比
Fig. 3. Band1 histogram contrast of different spatial resolution (a), spectrum resolution (b), SNR (c) simulation image
图 4 不同空间分辨率(a)、波谱分辨率(b)、信噪比(c)的模拟影像波段相关图
Fig. 4. Band correlation diagram of different spatial resolution (a), spectrum resolution (b), SNR (c) simulation image
图 5 不同空间分辨率(a)、波谱分辨率(b)、信噪比(c)模拟影像在A点光谱曲线对比
Fig. 5. Different spatial resolution (a), spectrum resolution (b), SNR (c) simulation image spectral curve contrast of A point
图 6 不同波谱分辨率(a)、空间分辨率的信噪比(b)曲线对比
Fig. 6. SNR curve contrast of different spectrum Resolution (a) and different spatial resolution (b)
图 7 不同空间分辨率(a)、波谱分辨率(b)、信噪比(c)模拟数据的提取结果
图中方框为软件自带定义窗口
Fig. 7. The extraction results of different spatial resolution (a), spectrum resolution (b) and SNR (c) simulation image
图 9 主要蚀变矿物的不同光谱曲线对比
a.高岭石；b.绿泥石；c.绿帘石；d.绢云母；e.金红石；f.蛇纹石
Fig. 9. Different spectral curve contrast of main alteration minerals
表 1 载荷模拟数据的参数设置
Table 1. The parameters set of the load simulation data
参数设置 指标参数 成像时间(GMT) 2010-06-01，06∶30∶00 A.M. 成像区域中心坐标 42°11′20.37″N，265°51′59.87″W 地面平均海拔 0.8 km 地表温度 300 K 大气类型 中纬度夏季 气溶胶类型 乡村，能见度为23 km 传感器高度 650 km 传感器观测角度 180° 空间分辨率 15、45、60和75 m 光谱范围 400~1 000 nm(VNIR)900~2 500 nm(SWIR) 光谱分辨率 30、25、20、15和5 nm 波段数 120(VNIR)160(SWIR) MTF(奈奎斯特频率) 0.2 信噪比(地面反照率0.3，太阳天顶角30°) 200(VNIR)150(SWIR) 量化位数 12 bit 动态范围 20 μw/(cm2×sr×nm)(VNIR) 11 μw/(cm2×sr×nm)(SWIR) 注：据赵慧洁，2009. 矿物光谱分解算法开发和弱信息识别技术研究.矿物光谱分解算法开发和弱信息识别技术研究设计方案报告 
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