基于面向对象与深度学习的典型地物提取

The typical object extraction method based on object-oriented and deep learning

针对遥感图像地物分割问题面向对象方法可以将不同地物分割到不同的对象之中,在很大程度上解决了农作物、林地、水体、道路、建筑物等典型地物的混分问题,但面向对象方法对于形状、纹理等特征描述仍不够全面,信息量还不足以支撑完整的地物分类、识别。提出一种将面向对象与深度学习相结合的新方法,选用卷积神经网络Caffe框架,对训练样本数据进行深度学习,掌握不同对象的纹理等特性,形成深度学习模型,反过来指导对象分类。实验表明,新方法可以有效解决典型地物分不准的问题。

The object-oriented method solves the problem of segmentation of objects,divides different features into different objects and to a great extent separates the cultivated land,forest land,water,roads,buildings and other typical objects which are inseparable; nevertheless,the object oriented method for features such as shape,texture description is not comprehensive,the amount of information is not enough to support the whole classification and recognition. In this paper,a new method of combining object-oriented and deep learning is proposed,in which the Caffe framework of convolution neural network is used to study the training sample data in depth and,by grasping the texture of different objects and forming deep learning model,guides the classification of objects. The experiment shows that the new method can effectively solve the problem of the low classification accuracy.