中文摘要：

为实现高分辨率遥感影像特征的有效组织优化,以及提高特征的可判别性,该文提出了基于中层特征学习的多特征软概率级联模型实现场景级土地利用分类。首先,提取影像的密集尺度不变转换特征（dense scale invariant feature transform,DSIFT）、光谱特征（spectral feature,SF）以及局部二值模式特征（local binary pattern,LBP）作为低层特征;然后由局部约束线性编码（locality-constraint linear coding,LLC）分别对DSIFT特征、SF特征以及LBP特征进行稀疏编码得到3种低层特征的稀疏系数,并结合空间金字塔匹配（spatial pyramidal matching,SPM）模型、最大空间平滑方法对稀疏系数进行优化,获得影像的中层特征表达;最后,利用SVM分类器,分别对3种低层特征的中层特征表达进行分类,并分别计算3种低层特征分类的软概率,级联3种特征的软概率将其作为图像最终的特征表达,利用SVM分类器进行第2次分类得到最终分类结果。采用UC-Merced Land Use数据集对该方法进行了验证,试验结果表明：1）该方法总体精度达到88.6%,相较于传统稀疏编码空间金字塔匹配（sparse coding and spatial pyramidal matching,Sc SPM）,局部约束线性编码（locality-constraint linear coding,LLC）等分类方法,总体精度分别提高了12.7%,9.9%;2）相较于提取单一低层特征的场景分类方法,该文算法更有利于实现对影像中复杂且不易区分的地物的表达,可有效提高土地利用分类精度。

英文摘要：

High resolution remote sensing images （HRSI） provide abundant information on the textures and terrain structures of a scene. In recent years, scene classification methods based on mid-level feature learning have been increasingly used for the scene-level land use classification with high resolution remote sensing images. However, it is always a challenging task for effectively organizing and optimizing the spectral, texture and geometrical structure features in the field of land use classification at the scene level. Since the learning algorithm based on mid-level features can represent the low-level features （e.g., spectrum, textures and geometrical structures） of HRSI effectively, the scene level classification of land use can be easily achieved by the use of a classifier like support vector machine （SVM）. Nevertheless, the mid-level feature descriptors are not discriminative enough, because the mid-level feature descriptors are learned by an unsupervised way. Meanwhile, the conventional approaches using this strategy consider merely the geometrical structure features, and neglect other meaningful low-level features of the images. In order to make the learned feature descriptors more discriminative and incorporate different low-level features better, in this work we proposed a method utilizing the vector-cascading model combining multi-features soft-probability to achieve the land use classification at the scene-level. Firstly, the local dense scale invariant feature transform （DSIFT）, spectral features （SF） and local binary pattern （LBP） features were extracted as the low-level features of the images. The spectral features were obtained by calculating the color histogram of the images.Then, with regard to each type of low-level features, from each image a certain number of samples were selected randomly to be clustered by K-means algorithm to generate the dictionary. Secondly,based on the trained dictionary of the different features, the local DSIFT, spectral and LBP features were encod