【摘要】 高光谱遥感数据具有光谱信息丰富、图谱合一的特点,目前已经广泛地应用在对地观测中。传统的高光谱分类模型大多过分依赖影像光谱信息,没有充分利用空间特征信息,这使得分类精度还有很大的提升空间。条件随机场是一种概率模型,能够较好地融合空间上下文信息,在高光谱影像分类中已经得到越来越多的关注,但大部分条件随机场模型存在超平滑的现象,会导致影像细节丢失。针对该问题,本文提出了一种优化融合影像空-谱信息的高分辨率/高光谱影像分类方法,该方法将影像的纹理信息与原始光谱信息进行融合,利用SVM分类器对其进行预分类,并将各类概率定义为一元势函数,以融合空间特征信息;然后将空间平滑项和局部类别标签成本项加入二元势函数中,以考虑空间背景信息,并保留各类别中的详细信息。最后,通过两组的高分辨率/高光谱影像数据进行试验。结果表明,与SVM算法、传统的条件随机场方法和面向对象的分类方法相比,本文提出的算法在整体分类精度上分别提高了10%、9%和8%以上,同时在保持地物边缘完整性、避免"同谱异物"与"同物异谱"的现象方面有较明显的优势。更多还原

【Abstract】 Hyperspectral remote sensing image has the characteristics of rich spectral information and combining image with spectrum, which has been widely applied in the earth observation. Most of traditional hyperspectral image classification models don’t make fully use of spatial feature information, rely too much on the spectral imformation, making the classification accuracy still have a lot of room to improve. Conditional random field(CRF) is a kind of probability mode that can better integrate spatial context information. It plays a more and more important role in hyperspectral image classification. However, most CRF models have the problem of excess smoothness, which will result in the loss of detail information. Aiming at this problem, this paper proposed a hyperspectral image classification method based on space-spectral fusion conditional random field. The proposed method designs suitable potential functions in a pairwise conditional random field model, fusing the spectral and spatial features to consider the spatial feature information and retain the details in each class. The experiments on two sets of hyperspectral image showed that, compared with the traditional methods, the proposed classification method can effectively improve the classification accuracy, protect the edges and shapes of the features, and relieve excessive smoothing, while retaining detailed information.更多还原