基于样本自动扩充的街区式农村居民地遥感提取方法

Supervised Dense Rural Residential Extraction from High-resolution Remote Sensing Images Based on Automatically Augmentation of Training Samples

与基于非监督分类机制的居民地提取方法相比较,基于监督分类机制的居民地提取方法具有较高的提取精度。但是,基于监督分类机制的方法依赖于人工标注的训练样本,繁琐的标注工作限制了这类方法在遥感大数据时代的应用。利用监督居民地提取方法具有较高提取精度的优点,同时克服这类方法需要人工标注样本的缺点,能够建立更为实用的居民地提取方法。为此,针对中国华北平原广泛分布的街区式农村居民地,提出一种基于监督分类机制且仅需单个人工标注样本的居民地遥感提取方法。该方法首先根据居民地在遥感影像上的特征设计居民地排除规则,对划分的影像块进行初步分类;然后,从划分为非居民地的影像块中随机挑选一定量的影像块作为负样本,以人工标注的单个正样本为基础进行正样本扩充;最后,采用k-近邻分类法训练居民地分类器,对初步判定为居民地的影像块做进一步分类。试验结果表明,方法能够准确地提取影像中的居民地,对地物背景存在差异的遥感影像具有良好的提取效果。

Compared with the unsupervised methods of residential areas extraction, the supervised methods are of relatively higher accuracy. However, the supervised methods rely on large amounts of training samples, and manually labeling residential areas is tedious and time-consuming, limiting their applications in the era of remote sensing big data. In order to improve application performances of the extraction methods based on supervised classification, it is necessary to overcome the disadvantage that training samples need to be manually labeled with high accuracy. Dense rural residential areas composed of single-family building blocks are the predominant type of rural residential areas in North China Plain. In this paper, we set extraction targets to be the dense rural residential areas, and propose a novel extraction method for high-resolution remote sensing images. The proposed method utilizes a supervised classifier but only one positive sample labeled manually is required. Firstly, four exclusion rules are designed based on the features of rural residential areas in high-resolution remote sensing images. According to the exclusion rules, all of the image blocks are classified into two categories of residential areas or non-residential areas. After the coarse classification, a certain number of the negative samples are randomly selected from the image blocks belonging to the category of non-residential areas. Then one positive sample is labeled manually, and more positive samples are collected from the image blocks in the neighborhood of the only one positive sample by performing the nearest neighbor classifier. At last, the K- Nearest Neighbor classifier is adopted to pick up image blocks which are closer to more positive samples in the feature space. The classifier filters image blocks belonging to the category of non-residential areas from the coarse extraction result, then the final extraction result is obtained. Experimental results conducted on the test images confirm that the proposed approach is both efficient and robust to images with different backgrounds.