Building extraction plays a significant role in many high-resolution remote sensing image applications. Many current building extraction methods need training samples while it is common knowledge that different samples often lead to different generalization ability. Morphological building index (MBI), representing morphological features of building regions in an index form, can effectively extract building regions especially in Chinese urban regions without any training samples and has drawn much attention. However, some problems like the heavy computation cost of multi-scale and multi-direction morphological operations still exist. In this paper, a multi-scale filtering building index (MFBI) is proposed in the hope of overcoming these drawbacks and dealing with the increasing noise in very high-resolution remote sensing image. The profile of multi-scale average filtering is averaged and normalized to generate this index. Moreover, to fully utilize the relatively little spectral information in very high-resolution remote sensing image, two scenarios to generate the multi-channel multi-scale filtering index (MMFBI) are proposed. While no high-resolution remote sensing image building extraction dataset is open to the public now and the current very high-resolution remote sensing image building extraction datasets usually contain samples from the Northern American or European regions, we offer a very high-resolution remote sensing image building extraction datasets in which the samples contain multiple building styles from multiple Chinese regions. The proposed MFBI and MMFBI outperform MBI and the currently used object based segmentation method on the dataset, with a high recall and F-score. Meanwhile, the computation time of MFBI and MBI is compared on three large-scale very high-resolution satellite image and the sensitivity analysis demonstrates the robustness of the proposed method.

Landscape patterns and building functions are successfully used to provide the social sensing information of urban areas. However, previous studies treated ground objects equally, ignoring their size differences. Considering the different contributions of various types of ground objects in land-use classification, this paper measured nine area-weighted mean landscape-level metrics to describe landscape patterns based on the land-cover map, derived from remote sensing images. Additionally, the same idea was applied for identifying building functions. Impervious surfaces, which occupy the majority of urban areas, have a decisive impact on land-use classes. In terms of this, this paper proposed the impervious surface area-weighted building-based indexes from the building outline data. To better represent the physical structure of urban areas, the entire study was based on the analysis units delineated by the OpenStreetMap road network. Finally, a random forest model combining the landscape-level metrics and building-based indexes was adopted in Wuchang District of Wuhan city, China. The results showed that the proposed method was effective at describing landscape patterns and identifying building functions for accurate urban land-use classification, increasing the precision by 10.67%. In general, the contribution of landscape-level metrics to the urban land-use classification is slightly greater than that of building-based indexes. Moreover, different land-use types of analysis units express different landscape patterns. It is of great significance for improving urban form and guiding future urban design. The paper demonstrates that area-weighted landscape metrics and building-based indexes offer a better understanding of urban land use, which plays a vital role in urban planning, construction, and management.

The positioning accuracy of Ziyuan-3 01 (ZY-3 01) and Gaofen-7 (GF-7) panchromatic imagery, obtained during the early deployment of the satellites, was verified. Particularly, this paper first validated the geometric positioning accuracy of GF-7 panchromatic imagery. Based on the rigorous geometric model of surveying and mapping satellites, this study applied a geometric calibration (GC) model using the Rodrigues rotation matrix. Also, we employed the self-calibrating bundle adjustment with an additional parameter model to compensate for the internal orientation distortion. The results show that the positioning accuracy can be largely improved with few ground control points. Especially, the horizontal accuracy (specified in x and y) and elevation accuracy of ZY-3 01 in the Anping area are around 4 m and 2 m, respectively, and that of GF-7 in the Beijing area can achieve 3.2 m, 2.8 m, and 1.2 m in the x, y, and z directions, respectively.

In the remote sensing imagery, spectral and texture features are always complex due to different landscapes, which leads to misclassifications in the results of semantic segmentation. The object-based Markov random field provides an effective solution to this problem. However, the state-of-the-art object-based Markov random field still needs to be improved. In this paper, an object-based Markov Random Field model based on hierarchical segmentation tree with auxiliary labels is proposed. A remote sensing imagery is first segmented and the object-based hierarchical segmentation tree is built based on initial segmentation objects and merging criteria. And then, the object-based Markov random field with auxiliary label fields is established on the hierarchical tree structure. A probabilistic inference is applied to solve this model by iteratively updating label field and auxiliary label fields. In the experiment, this paper utilized a Worldview-3 image to evaluate the performance, and the results show the validity and the accuracy of the presented semantic segmentation approach.