Advances in urban information extraction from high-resolution remote sensing imagery

The study of urban area is one of the hottest research topics in the field of remote sensing. With the accumulation of high-resolution(HR) remote sensing data and emerging of new satellite sensors, HR observation of urban areas has become increasingly possible, which provides us with more elaborate urban information. However, the strong heterogeneity in the spectral and spatial domain of HR imagery brings great challenges to urban remote sensing. In recent years, numerous approaches were proposed to deal with HR image interpretation over complex urban scenes, including a series of features from low level to high level, as well as state-of-the-art methods depicting not only the urban extent, but also the intra-urban variations. In this paper, we aim to summarize the major advances in HR urban remote sensing from the aspects of feature representation and information extraction. Moreover, the future trends are discussed from the perspectives of methodology, urban structure and pattern characterization, big data challenge, and global mapping.

Integration of optical and SAR remote sensing images for crop-type mapping based on a novel object-oriented feature selection method

Remote sensing is an important technical means to investigate land resources.Optical imagery has been widely used in crop classification and can show changes in moisture and chlorophyll content in crop leaves,whereas synthetic aperture radar(SAR)imagery is sensitive to changes in growth states and morphological structures.Crop-type mapping with a single type of imagery sometimes has unsatisfactory precision,so providing precise spatiotemporal information on crop type at a local scale for agricultural applications is difficult.To explore the abilities of combining optical and SAR images and to solve the problem of inaccurate spatial information for land parcels,a new method is proposed in this paper to improve crop-type identification accuracy.Multifeatures were derived from the full polarimetric SAR data(GaoFen-3)and a high-resolution optical image(GaoFen-2),and the farmland parcels used as the basic for object-oriented classification were obtained from the GaoFen-2 image using optimal scale segmentation.A novel feature subset selection method based on within-class aggregation and between-class scatter(WA-BS)is proposed to extract the optimal feature subset.Finally,crop-type mapping was produced by a support vector machine(SVM)classifier.The results showed that the proposed method achieved good classification results with an overall accuracy of 89.50%,which is better than the crop classification results derived from SAR-based segmentation.Compared with the ReliefF,mRMR and LeastC feature selection algorithms,the WA-BS algorithm can effectively remove redundant features that are strongly correlated and obtain a high classification accuracy via the obtained optimal feature subset.This study shows that the accuracy of crop-type mapping in an area with multiple cropping patterns can be improved by the combination of optical and SAR remote sensing images.

城区高分辨率SAR图像的信息获取与重建

简要介绍了一种多方位城区高分辨率SAR图像的信息获取和地物重建方法。该方法的整个流程包括用恒虚警率(CFAR)检测器检测边缘,用平行线Hough变换从边缘提取建筑物的像,根据提取的建筑物像的统计特性,给出有各方位的像估计建筑物参数的方法。用该方法对四方位Pi-SAR图像做试验,得到了较好的结果。最后给出了该方法的实际应用建议。

合成孔径雷达干涉测量技术(InSAR)及其对城市遥感的意义

该文详细论述与分析了合成孔径雷达干涉测量(InSAR)的干涉几何特征以及InSAR影像之间的相关性特征,着重阐 述共分析了影响其地学监测方面的数据质量等相干技术问题。就InSAR开展上海城市地面沉降研究提出了基本思路, 对InSAR城市遥感应用的潜在意义进行了分析和讨论。

Evaluation of Three-dimensional Urban Expansion: A Case Study of Yangzhou City, Jiangsu Province, China

With rapid urban development in China in the last two decades, the three-dimensional(3D) characteristic has been the main feature of urban morphology. However, the vast majority of researches of urban growth have focused on the planar area(two-dimensional(2D)) expansion. Few studies have been conducted from a 3D perspective. In this paper, the 3D urban expansion of the Yangzhou City, Jiangsu Province, China from 2003 to 2012 was evaluated based on Geographical Information System(GIS) tools and high-resolution remote sensing images. Four indices, namely weighted average height of buildings, volume of buildings, 3D expansion intensity and 3D fractal dimension are used to quantify the 3D urban expansion. The weighted average height of buildings and the volume of buildings are used to illustrate the temporal change of the 3D urban morphology, while the other two indices are used to calculate the expansion intensity and the fractal dimension of the 3D urban morphology. The results show that the spatial distribution of the high-rise buildings in Yangzhou has significantly spread and the utilization of the 3D space of Yangzhou has become more efficient and intensive. The methods proposed in this paper laid a foundation for a wide range of study of 3D urban morphology changes.

Building Extraction from High Resolution SAR Imagery in Urban Areas

In this paper, the textural characteristics of the buildings were quantified by using two texture descriptors, namely, Square Root Pair Difference （SRPD） and Gi ＊. Then, a novel method, based on SRPD and Gi ＊, to extract building areas in ur- ban areas from very high resolution SAR images is presented. The results showed that this method has the ability to differentiate buildings from the complicated features in urban areas, which can be employed for land mapping and provides support for relief operations.