Xiucheng Yang

An Efficient Approach for Automatic Rectangular Building Extraction From Very High Resolution Optical Satellite Imagery

This letter presents a new approach for rapid automatic building extraction from very high resolution (VHR) optical satellite imagery. The proposed method conducts building extraction based on distinctive image primitives such as lines and line intersections. The optimized framework consists of three stages: First, a developed edge-preserving bilateral filter is adopted to reduce noise and enhance building edge contrast for preprocessing. Second, a state-of-the-art line segment detector called EDLines is introduced for the real-time accurate extraction of building line segments. Finally, we present a graph search-based perceptual grouping approach to hierarchically group previously detected line segments into candidate rectangular buildings. The recursive process was improved through the efficient examination of geometrical information with line linking and closed contour search, in order to obtain more reasonable omission and commission rate in building contour grouping. Extensive experiments performed on VHR optical QuickBird imageries justify the effectiveness and robustness of the proposed linear-time procedure with an overall accuracy of 80.9% and completeness of 87.3%. This method does not require user intervention and thereby has the potential to be adopted in online applications and industrial use in the near future.

Mapping of Urban Surface Water Bodies from Sentinel-2 MSI Imagery at 10 m Resolution via NDWI-Based Image Sharpening

This study conducts an exploratory evaluation of the performance of the newly available Sentinel-2A Multispectral Instrument (MSI) imagery for mapping water bodies using the image sharpening approach. Sentinel-2 MSI provides spectral bands with different resolutions, including RGB and Near-Infra-Red (NIR) bands in 10 m and Short-Wavelength InfraRed (SWIR) bands in 20 m, which are closely related to surface water information. It is necessary to define a pan-like band for the Sentinel-2 image sharpening process because of the replacement of the panchromatic band by four high-resolution multi-spectral bands (10 m). This study, which aimed at urban surface water extraction, utilised the Normalised Difference Water Index (NDWI) at 10 m resolution as a high-resolution image to sharpen the 20 m SWIR bands. Then, object-level Modified NDWI (MNDWI) mapping and minimum valley bottom adjustment threshold were applied to extract water maps. The proposed method was compared with the conventional most related band- (between the visible spectrum/NIR and SWIR bands) based and principal component analysis first component-based sharpening. Results show that the proposed NDWI-based MNDWI image exhibits higher separability and is more effective for both classification-level and boundary-level final water maps than traditional approaches.

Extraction of bridges over water from high-resolution optical remote-sensing images based on mathematical morphology

Bridges over water are typical man-made structures on the land’s surface. An accurate extraction of such bridges from high-resolution optical remote-sensing images plays an important role in civil, commercial, and military applications. Considering the complex features of ground objects within high-resolution optical remote-sensing images and the inefficiency of previous methods of bridge extraction with random bridge orientation, direction-augmented linear structuring elements were constructed and applied in this study by using mathematical morphology to identify and extract bridges over water with different orientations. First, the image pre-processing is performed to facilitate the object extraction. Then by using the histogram-based threshold segmentation method, water bodies such as rivers are extracted and described as a binary image. Based on water bodies, the appropriate direction-augmented linear structuring element is then selected. Together with mathematical morphology operations, such as dilation and erosion, potential bridges are extracted by overlay analysis. Assisted by prior knowledge of bridges, false bridges are screened out and post-processing is finally performed to refine the extracted true bridges. This approach was validated with experiments in Shanghai and Beijing, China. The results show that the direction-augmented linear structuring elements are of high precision and have the capability of extracting bridges over water in different directions within the high-resolution optical remote-sensing image, considering both qualitative and quantitative aspects. Therefore, this approach may be useful in updating geographical databases of bridges and facilitating the assessment of bridge damage caused by natural disasters.

Knowledge-Based Detection and Assessment of Damaged Roads Using Post-Disaster High-Resolution Remote Sensing Image

Road damage detection and assessment from high-resolution remote sensing image is critical for natural disaster investigation and disaster relief. In a disaster context, the pairing of pre-disaster and post-disaster road data for change detection and assessment is difficult to achieve due to the mismatch of different data sources, especially for rural areas where the pre-disaster data (i.e., remote sensing imagery or vector map) are hard to obtain. In this study, a knowledge-based method for road damage detection and assessment solely from post-disaster high-resolution remote sensing image is proposed. The road centerline is firstly extracted based on the preset road seed points. Then, features such as road brightness, standard deviation, rectangularity, and aspect ratio are selected to form a knowledge model. Finally, under the guidance of the road centerline, the post-disaster roads are extracted and the damaged roads are detected by applying the knowledge model. In order to quantitatively assess the damage degree, damage assessment indicators with their corresponding standard of damage grade are also proposed. The newly developed method is evaluated using a WorldView-1 image over Wenchuan, China acquired three days after the earthquake on 15 May 2008. The results show that the producer’s accuracy (PA) and user’s accuracy (UA) reached about 90% and 85%, respectively, indicating that the proposed method is effective for road damage detection and assessment. This approach also significantly reduces the need for pre-disaster remote sensing data.

Building Façade Recognition Using Oblique Aerial Images

This study proposes a method to recognize facades from large-scale urban scenes based on multi-level image features utilizing a recently developed oblique aerial photogrammetry technique. The method involves the use of multi-level image features, a bottom-up feature extraction procedure to produce regions of interest through monoscopic analysis, and then a coarse-to-fine feature matching strategy to characterise and match the regions in a stereoscopic model. Feature extraction from typical urban Manhattan scenes is based on line segments. Windows are re-organised based on the spatial constraints of line segments and the homogeneous structure of the spectrum. Facades as regions of interest are successfully constructed with a remarkable single edge and evidence from windows to get rid of occlusion. Feature matching is hierarchically performed beginning from distinctive facades and regularly distributed windows to the sub-pixel point primitives. The proposed strategy can effectively solve ambiguity and multi-solution problems in the complex urban scene matching process, particularly repetitive and poor-texture facades in oblique view.

Deep hierarchical representation and segmentation of high resolution remote sensing images

This paper presents a novel deep hierarchical representation and segmentation approach for high resolution remote sensing image understanding. An information extraction approach using deep hierarchical exploitation for remote sensing image is presented. The key idea is that we adopt a fast scanning image segmentation within a deep hierarchical feature representation framework, using a deep learning technique to split and merge over-segmented regions until they form meaningful objects. The contribution is to develop an effective procedure for multi-scale image representation to address the issue of information uncertainty in practical applications. We test our method on two optical high resolution remote sensing image datasets and produce promising experimental results in the form of multiple layer outputs, which confirm the effectiveness and robustness of the proposed procedure.

A knowledge-based method for road damage detection using high-resolution remote sensing image

Road damage detection from high-resolution remote sensing image is critical for natural disaster investigation and disaster relief. In a disaster context, the pair of pre-disaster and post-disaster road data for change detection are difficult to obtain due to the mismatch of different data sources, especially for rural areas where the pre-disaster data (i.e. remote sensing imagery or vector map) are hard to obtain. In this study, a knowledge-based method for road damage detection solely from post-disaster high-resolution remote sensing image is proposed. The road centerline is firstly extracted based on the preset road seed points. Then, features such as road brightness, standard deviation, rectangularity, aspect ratio are selected form a knowledge model. Finally, under the guidance of the road centerline, the post-disaster roads are extracted and the damaged roads were detected by applying the knowledge model. The newly developed method is evaluated using a WorldView-1 image over Wenchuan, China acquired three days after the earthquake in May 15, 2008. The results show that the producers accuracy (PA) and users accuracy (UA) reached about 90% and 85% respectively, indicating that the proposed method is effective for road damage detection. This approach also significantly reduces the need for pre-disaster remote sensing data.

Shortest path algorithm based on distance comparison

The paper presents a newly shortest path algorithm aiming at the point-to-point problems in traffic network. The algorithm makes use of the axiom that distance is less than path based on the characteristics of transportation network. The test compared with the typical Dijkstra shows the validity and efficiency of the proposed algorithm.

Automated road extraction from multi-resolution images using spectral information and texture

Road is a kind of very typical artificial object. Road extraction from multi-scale remote sensing images is significant both in military field and in peoples daily lives. With the development of remote sensing technology, the scale of remote sensing images that can be obtained becomes various. Therefore, the research of multi-scale remote sensing images is getting more and more attention and it is really a challenging task in the field of image processing. In this paper, a method of road extraction from multi-scale remote sensing images is proposed. Firstly, the textures are extracted and added to the bands of the original image. The filtering, resampling and segmentation operations are then implemented. Next, the spectral characteristics and textures of roads on the remote sensing images are statistically analyzed, and the changes of those on multi-scale remote sensing images are obtained. Then, considering the road characteristics displayed on remote sensing images, some parameters of spectral characteristics and textures are selected to extract roads using the object-oriented method. Finally, the results of road extraction are post-processed based on the opening and closing operation of mathematical morphology. This study has great significance in areas such as features optimization, target recognition, building feature database and improving the utilization of remote sensing data.

Façade reconstruction from oblique areal images

The paper realizes façade 3D reconstruction using recently promising oblique photogrammetry data. the point-to-point problems in traffic network. We make full use of the multi-level image features to extract interest regions of façade, and then present a backwards coarse-to-fine matching, which makes the auxiliary data unnecessary. The experiment shows the efficiency and robustness of the proposed method, and the vectors describing façade 3D information are also verify the high precision.