Building height information is an important part of urban basic geographic information, which plays an important role in many urban applications, such as urban planning, building floor area ratio calculation, smart city construction [1
]. Automatic building height estimation from high-resolution images has always been one of the fundamental tasks in the field of remote sensing research.
The existing building height estimation methods based on remote sensing images are mainly divided into two categories. The first is based on light detection and ranging (lidar) [6
], interferometric synthetic aperture radar (InSAR) [9
], and stereo pair [12
]. The second is based on the shadows of buildings from remote sensing imagery [15
]. In the first method, multi-source data is used for building height estimation. For example, Soregel et al. proposed an interferometric synthetic aperture radar building height estimation method based on a segmentation algorithm [20
]. Dubois et al. carried out the detection and extraction of building overlap based on the InSAR phase diagram to achieve the purpose of estimating geometric parameters such as building height [9
]. Sportouche et al. used the DTM and system parameters of SAR sensors to provide a building height estimation method based on likelihood criterion optimization [21
]. Wegner et al. used a pair of InSAR images and an aerial orthophoto to estimate the height of buildings [22
]. Brunner et al. proposed a method for the height estimation of generic man-made structures from single detected SAR data, and the efficiency of their method was proven on a set of 40 flat roof and gable roof buildings in the absence of crosstalk effects [23
]. Vu et al. proposed a multi-scale solution based on morphology, which obtains elevation information from airborne lidar data, and describes the elevation data in the morphological scale space to realize the expression of building height [24
]. Ding et al. proposed a method to obtain building height from a single ground image based on the inherent parameters of the camera [25
]. Chen et al. used stereo pairs to extract building height by using the Digital Elevation Model (DEM) to identify building height in the city’s three-dimensional model [26
]. The above methods have improved the efficiency of obtaining height information, and the accuracy of the estimation results is also improved. However, the data used are not easy to obtain, since they are affected by geographical location, weather, and other factors, which shows obvious limitations in application.
Compared with the limitation that the above data is difficult to obtain, the optical remote sensing image has obvious advantages. It can be used to extract building height by constructing a model of the geometric relationship between the building and shadow in the image [27
]. Since 1989, in aerial photogrammetry, researchers have long used shadow information to estimate building height [32
]. Wang et al. used ZY3 images to establish a geometric relationship model between shadow length and building height, and combined the shadow length to calculate the building height. On this basis, a three-dimensional modeling of urban buildings was carried out [15
]. Liasis used the spectrum and spatial analysis information of satellite image to implement a new active contour model, thereby optimizing the shadow segmentation process of buildings, improving the accuracy of shadow extraction, and estimating building height through shadow length [19
]. Izadi et al. proposed a building height calculation method by detecting building boundaries and shadow boundaries, and achieved building height in QuickBird images [33
]. Wang et al. used the geometric relationship between shadows and buildings to calculate the height of buildings in Kunming, China in QuickBird images [34
]. Qi et al. built a method to calculate the height of buildings, which can calculate building height by Google Earth [35
]. Shettigara et al. used the shadow information on the SPOT panchromatic image to construct a model to obtain building height [36
]. Turker et al. used building shadow to calculate the height of collapsed buildings in an earthquake [37
]. Wang et al. proposed a multi-constrained method to extract shadow information from images, and calculate the height information of buildings based on the relationship between shadow and building [38
]. Shao et al. proposed a method combining the spatial index of image objects to improve the accuracy of shadow extraction, and took IKONOS images as an example to estimate the building height using shadow length [39
Although these works are all notable, the application of the scene is limited to building height estimation in remote sensing images. Firstly, the building height estimation model in different scenarios is not perfect, because of the influence of the sun azimuth and altitude angle, the satellite azimuth and altitude, and the terrain. Secondly, the shadow of densely-built areas in some images adheres to each other, which cannot accurately reflect the height of buildings. Finally, the traditional method of using the shadow length to calculate building height cannot effectively deal with the problem of complex shape of the building shadow. To overcome these limitations, this paper proposes a multi-scene building height estimation method based on shadow in high-resolution satellite imagery. The main contributions of our work are summarized below.
(1) The multi-scene building height estimation model is established by analyzing building shadow in remote sensing images, which can explain the geometric relationship between buildings and shadows in different scenarios.
(2) A method-regularized extraction of building shadows is proposed, which can solve the problem of mutual adhesion between shadows in dense areas of buildings.
(3) We propose a method of shadow length calculation based on the combination of fish net and pauta criterion for the problem of complex shadow shapes of buildings, which can provide more reliable basic data for building height estimation.
The remainder of this paper is organized as follows. The methods are presented in Section 2
, including classification and description of building shadows and multi-scene building height estimation. The experiment results and analysis of this article are presented in Section 3
, including building height estimation results of ordinary scene, dense scene, and complex terrain scene. Finally, the conclusion and future work are presented in Section 4
4. Conclusions and Future Works
Although there is much research on building height estimation by shadow, there is little research on the calculation methods of building height in different scenes. In this paper, we implement a variety of scenarios of the building height estimation method, and verify the effectiveness of the method framework in several experimental areas. Firstly, we completed the classification and semantic description of building shadows in different scenes, which provides a basis for using shadows to extract building height. Secondly, we solved the problem of building shadows’ mutual adhesion in dense areas, which is very common in remote sensing images. Then, the shadow length is calculated by combining the fish net line and the pauta criterion in order to obtain more accurate shadow lines, which provides a more reliable data basis for building height estimation. Finally, the comparison with existing methods under the same data also proves that our method has accuracy advantages. In this work, using our method can effectively avoid the limitation of ideal conditions in traditional building height estimation methods, and further expand the application scenarios of building height estimation using shadow.
However, the estimation accuracy still needs to be further improved, and the scene scalability also needs to be further expanded. In future works, we consider using artificial intelligence methods for in-depth research on this basis, so that the building height estimation results can achieve higher accuracy and be applied to more complex scenes.