中文摘要：

在核函数集成SVM分类框架下,提出-种融合多尺度光谱- 空间- 语义特征的高分辨率遥感影像分类方 法.首先,以多尺度影像分割集为基础,利用潜狄利克雷分配模型对分割图斑的语义特征进行建模,并结合原始影 像的光谱特征以及分割图斑内的空间均值特征,在不同分割尺度下分别开展光谱- 空间- 语义特征的多核函数融 合分类;然后根据多数投票法原则在决策级集成多尺度分类结果,通过最小尺度下的分割影像实现像素级分类结 果至面向对象分类结果的转化.不同场景和分辨率数据下开展的实验结果表明,该分类方法能够实现分类结果的 自适应平滑分类,并在-定程度上提高建筑物和道路等/ 同谱异物”地物的区分能力,分类总体精度由基于光谱特 征 SVM 的 66 7%和 63 7%提升至86 8%和 87 2%.

英文摘要：

A novel approach for the classification of high resolution remote sensing images is presented by fusing multiscale spectral- spatial- semantic features via the multiple- kernel SVM classifier. Based on a series of image segmentation maps produced by the Entropy Rate Superpixel Segmentation（ ERSS） algorithm, the proposed method utilizes the latent Diridilet allocation to model the semantic feature of each segment first ly. Then, the spectral feature represented by t he original pixef s value and spa-tial feature represented by the average pixel value of each segment are combined with the learned semantic feature to conduct the spectral- spatia- semantic classification in the framework of the composite kernel SVM at each scale in scale space,followed by a majority decision fusion to integrate multiple classification results. Finally, the image segmentation map at the minimum scale is used to convert the ensemble pixel based classification results to the object based classification results. Experimental results u-sing high resolution satellite imageries with different scenes and spatial resolutions indicate that the proposed approach can a2 chieve a self- adaptive smoothing effect on the classification map, and increase the ability to distinguish the geo- objec：ts with spec-tral overlap, such as road and building.T he overall accuracy was improved from 66. 7% and 63. 7% with the conventional SVM based on the spectral feature to 86. 8% and 87.2% with the proposed method.