图像光谱 meaning in Chinese
image spect ra
Examples
- Compared to multi - spectral image , hyperspectral image is high spectral resolution , narrow band , and has many bands . it can distinguish targets with reliability
与多光谱图像相比,高光谱图像光谱波段数目多、光谱分辨率高、波段宽度窄,能够以较高的可信度区分和辨识地物目标。 - Besides , this study has analyzed the correlativity of each band with snow depth , and compared snow true measuring spectrum with image spectrum , picked out the most sensitive band to snow depth , build inversion snow depth model
此外,本文分析了modis数据36个波谱值与雪深的相关关系,并将积雪实测光谱与图像光谱进行对比,挑选出了对雪深反应最敏感并能真实反映积雪光谱的两个波段,建立了雪深反演模型。 - Afterwards , in order to decrease the contradiction between the more complex and mass remote sensing image data and relatively slow speed of information extraction , an improved sfim image fusion method is proposed . this modified algorithm is on the base of sfim fusion technique , combines ihs method and sfim method and then replaces the former mean filter by an adaptive weighted mean filter . compared with the results of several common fusion techniques through a set of simulation tests between multispectral images and panchromatic images , it is proved that the new method can get an excellent result for the aim of improving spatial resolution while preserving the spectral information of multispectral images
论文的主要工作和成果包括：在像素层，论文研究了多传感器数据融合理论及遥感图像预处理的过程和步骤，归纳了多源遥感图像像素层融合的常用算法，并针对目前遥感数据呈海量化、复杂化这一发展趋势同遥感信息提取的能力和效率滞后这一矛盾，在sfim算法的基础上，将ihs变换与sfim相结合，将原算法中的均值滤波器改进为自适应加权均值滤波器，提出了一种改进的sfim算法，通过对一组多光谱图像和全色图像的双传感器融合仿真对比试验，证明了该算法在保持原多光谱图像光谱信息的同时，能够有效提高融合图像的空间分辨能力。 - In this paper we studied the textural features extraction , remote sensing images classification and bp neural network techniques and their applications in the meteorological problems such as recognition of the cloud cluster feature , cloud - drift wind retrieval and heavy rain process analysis etc . to the question of the low precise recognition of satellite images by using spectral features , the proposed approach assumes to perform a multiple analysis based on an advisable decision - making model by first developing a mixed pixel model which was based on the textural features of images , and then improving the recognition intelligence
本文对模式识别领域中的图像纹理特征提取、遥感图像分类、 bp神经网络与纹理特征组合分类等方法，以及它们在云团属性识别、云迹风反演和暴雨过程分析等气象问题中的应用作了研究。针对过去利用图像光谱亮度特征进行识别分析气象卫星图像准确度不高的问题，本文提出了发展混合像元的分解模型，以图像的纹理特征为基础，提高图像识别的智能水平，以实现在分析决策模型的支持下，快速准确的复合分析的解决方案。