摘要
高光谱遥感技术是上世纪80年代发展起来的一种新兴遥感技术,高光谱遥感技术借助成像光谱仪革命性地将成像技术和细分光谱技术结合在一起,能在电磁波谱的可见光、近红外、中红外和热红外波段范围内提供对地物光谱的精细探测,所获取的图像数据包含丰富的空间和光谱信息,为地物目标的精确探测和识别提供了可能。然而,相对于高光谱数据提供的巨大信息,当前的高光谱图像处理技术远远滞后于高光谱成像仪的发展,仍有许多问题急待进一步研究和解决。
本文从高光谱图像图谱合一的特点出发,以典型地物和环境为研究对象,充分挖掘高光谱图像的内在特性,发展有效的图像分析工具,研究精度高鲁棒性强的目标探测与识别方法。
论文首先对高光谱遥感技术的应用和研究发展状况进行了综述,在此基础上,分析了高光谱遥感图像应用中当前存在的主要问题,并针对高光谱图像分类和目标识别中存在的问题进行了研究。
其次,论文研究了基于光谱分段的匹配和识别方法。在高光谱图像处理中,光谱匹配技术是高光谱地物识别的关键技术之一,光谱匹配通过比较反映地物光谱辐射特性的光谱曲线来识别地物的类别。目标的光谱辐射特性分散于整个成像光谱区域中,并且以不同尺度的吸收峰或吸收谷的形式分布。因此在提取目标的光谱辐射特性时,应考虑不同尺度上的目标吸收特性,采用多尺度分析方法全面地提取目标的光谱辐射特性。论文研究了多尺度小波变换在光谱特征提取和识别中的应用,提出了一种基于多尺度小波变换拐点提取的光谱分段匹配方法,该方法以高斯二阶导函数为小波基,通过多尺度变换分析提取谱线的最佳拐点,并基于最佳拐点实现谱线的分段匹配和识别。
再次,针对高光谱图像中存在的阴影现象,论文从阴影的光谱特性分析入手,提出了一种基于密度聚类的多波段多特征阴影检测和提取方法。该方法从多个特征空间对阴影进行分析,采用动态阈值密度聚类方法对不同的特征数据进行分割,获得相应的阴影检测结果,在此基础上,结合不同特征空间检测结果的特点,提出了一种有效的多证据判决方法,实现了阴影判决结果的融合,取得了稳定的阴影提冉峁?
为了有效消除阴影对高光谱图像分类和识别精度的影响,论文分析了现有的高光谱图像阴影去除方法,针对阴影信息弱的特点,提出了一种基于张量修复技术和辐射模型校正增强技术相结合的自适应阴影去除方法,该方法通过张量分析和投票技术推测阴影区域的光照和亮度统计特性,利用辐射传输模型对阴影区进行自适应增强处理,实现了对阴影的有效去除。
论文最后利用高光谱图像图谱合一的特点,对典型地物道路的识别提取进行了研究,提出了一种基于光谱特征和几何特征相结合的道路识别和提取方法。该方法首先通过分析道路的光谱特征,利用光谱匹配方法提取道路潜在区域,然后结合道路的几何形态特征,利用数学形态学算子对潜在区域的几何特征进行进一步识别,实现了道路的有效提取。针对提取的道路可能受各种干扰因素影响而产生的断裂和不连续现象,论文采用基于张量的道路主方向提取和修复技术,实现了断裂的路连接修复,提高了道路提取的完整性。
This thesis was based on the property of syncretism of graph and spectrum for hyperspectral image. By excavating the inherence characteristic of hyperspectral remote sensing image and developing several effective analysis tools of image processing, high precision and robust method of object detecting and recognization was studied for typical objects and background in this paper.
Firstly, the application and development of hyperspectral remote sensing image were reviewed. And then this paper pointed out and studied the existed problems of hyperspectral remote sensing image.
Secondly, a matching and recognition method based on spectral subsection was researched. As well known, spectral matching technology is one of the key issues in the field of hyperspectral remote sensing image since it can be used for object recognition by comparing the spectral curves which provided the eradiating feature of ground objects. The spectral radiation feature is distributed to the entire spectrum and presented as several absorbing peaks and valleys with different scales. Based on this understanding, the study of extracting object radiation feature should be performed by taking multi-scale analysis technology which is helpful to extracting the spectral properties completely. In this thesis, the discrete wavelet transform is utilized for the application of spectral feature extraction. To this purpose, we took two order derivative of Gaussian function as wavelet basis and found the optimal inflexions based on multi-scale analysis, and presented a spectral subsection matching method based on the optimal inflexions.
Thirdly, considering the shadow phenomena presented in hyperspectral images, a shadow detection method was described based on the density clustering of multiple bands and features by start on the spectrum analysis. The rough idea of this method covers (a) obtain the shadow region by segmenting the data with different features by utilizing the dynamic thresholding density clustering method and (b) a robust and effective shadow detection method was presented by a multiple evidences decision strategy for different shadow results obtained by different features.
To remove the effect of shadow for the precision of image classification and reorganization, the existed shadow removing methods were discussed. Since the shadow information is sparseness, an adaptive shadow removal method was proposed based on tensor inpainting technology and radiation transmission correction technology. The procedure of this method includes that (a) conferring the statistics property of illumination and brightness by tensor analysis and voting technology and (b) adaptive enhancing the shadow regions by using radiation transmission model.
Finally, we studied road detection method based on the property of syncretism of graph and spectrum for high spectral image. The first step of this method is to detect the underlying region of shadow by using spectral matching method, and then by integrating the geometric characteristic of road which was extracted by directional morphological operations, the roads were detected completely. To overcome the ruptures and discontinuities caused by the different disturbing factors and enhance the completeness of extracting road, the rupture roads were inpainted by extracting and inpainting the main direction of road based on tensor analysis.
引文
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