中文摘要：

目的为进一步提高遥感影像的分类精度，将卷积神经网络（CNN）与条件随机场（CRF）两个模型结合，提出一种新的分类方法。方法首先采用CNN对遥感图像进行预分类，并将其类成员概率定义为CRF模型的一阶势函数；然后利用高斯核函数的线性组合定义CRF模型的二阶势函数，用全连接的邻域结构代替常见的4邻域或8邻域；接着加入区域约束，使用Mean—shift分割方法得到超像素，通过计算超像素的后验概率均值修正各像素的分类结果，鼓励连通区域结果的一致性；最后采用平均场近似算法实现整个模型的推断。结果选用3组高分辨率遥感图像进行地物分类实验。本文方法不仅能抑制更多的分类噪声，同时还可以改善过平滑现象，保护各类地物的边缘信息。实验采用类精度、总体分类精度OA、平均分类精度AA，以及Kappa系数4个指标进行定量分析，与支持向量机（SVM）、CNN和全连接CRF相比，最终获得的各项精度均得到显著提升，其中，AA提高3．28个百分点，OA提高3．22个百分点，Kappa提高5．07个百分点。结论将CNN与CRF两种模型融合，不仅可以获得像元本质化的特征，而且同时还考虑了图像的空间上下文信息，使分类更加准确，后加入的约束条件还能进一步保留地物目标的局部信息。本文方法适用于遥感图像分类领域，是一种精确有效的分类方法。

英文摘要：

Abstract： Objective Remote sensing image classification refers to the use of computers to analyze the spectral and spatial information of various land cover objects in remote sensing images, divide feature space into non-overlapping subspaces, and place a pixel into a specific subspace. In computer vision, this procedure aims to assign a predefined semantic label to each pixel in an image. This process is also called ＂semantic segmentation. ＂ The rapid development of computer applica- tion technology, aerospace, and sensor technology in recent years has resulted in numerous methods for acquiring different types of remote sensing image data. As an important aspect of remote sensing technology, the classification of high-resolu- tion remote sensing imagery has gained considerable attention. A novel image classification method is proposed in this study. This method is based on a fully connected conditional random field （CRF） model, which is combined with a convolutional neural network （CNN）. These two models are merged to utilize their respective advantages to further improve clas- sification accuracy for remote sensing images. Method On the one hand, most traditional classification methods typically rely on artificial experiences to extract the characteristics of training samples. After learning, a single-layer feature without a hierarchical structure is obtained. These methods generally have shallow structures, and the features they produced are relatively simple. By contrast, as a new research direction in the field of machine learning, deep learning can transform the feature representation of training samples from the original space into a new feature space layer by layer, as well as learn to automatically yield a hierarchical feature representation, which is conducive to classification and feature visualiza- tion. For the past years, this new subject has achieved a significant breakthrough in the field of computer vision applica- tions, such as visual recognition challenges, image classification,