The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) is equipped with an advanced topographic laser altimeter system (ATLAS) that obtains small spots and high-density photon data. ATLAS has the potential for high-precision detection of global surfaces and helps calculate global carbon sinks. One important carbon sink includes forests and making it critical to precisely measure their carbon storage and obtain both ground and top-of-canopy (TOC) levels of vegetation. Here, we explored a precise and highly reliable ground and TOC detection method based on ICESat-2 data to accurately remove noise and classify photons. This method can be adapted to different scenes and topographies. The first step included removal of pronounced dispersion noise and pseudo-signal photon aggregation noise. We introduced two algorithms for coarse noise removal—an adaptive threshold ellipsoid filter algorithm and an outlier photon cluster removal algorithm based on the photon group distance. Then, the local outlier factor (LOF) algorithm was modified to remove the noise of near-signal photons to obtain the result of fine denoising. Finally, the modified local direction center algorithm was combined with the uniaxial inverse distance weighted statistics to distinguish the ground from the TOC. Across varied scenes and terrains, the proposed denoising method demonstrated an overall accuracy exceeding 0.94. Additionally, the root mean square error of the ground and TOC obtained via the classification method was under 1.4 m and 3.2 m, respectively. These findings highlight that this method has a high level of robustness in effectively detecting both the ground and TOC.
Building height is one of the important data for understanding urban development and changes. Building height estimation using a digital surface model (DSM) based on the difference between the roof elevation and the ground elevation of the building is commonly utilized. However, owing to the limitations of existing DSM techniques, invalid values may exist in the DSM. Existing DSM-based methods for estimating building heights typically use the interpolated DSM; however, when there are many invalid values, there may be errors in the interpolation results, which can mislead the selection of ground elevation values. Therefore, we propose a building-height extraction method that combines an optimal selection region and a multiindex evaluation mechanism to reduce the impact of invalid values and complex terrains. First, the optimal area for the ground elevation search was obtained based on the spatial relationship between the target building and surrounding buildings. Second, a joint multiindicator weighted evaluation mechanism was used to obtain the optimal ground elevation value. Finally, the building height was determined based on the difference between the roof and the ground elevations. Four build-up areas were used to test the effectiveness of the proposed method. The results exhibit high accuracy in complex areas with variable ground elevations, with a mean absolute error (MAE) of 1.16 m in building height. In areas with many invalid values and large shadow coverage of the surface areas, the MAE in building height is 0.92 m. Additionally, we verified the accuracy of the ground elevation estimated after interpolation. It is evident that the performance of the original DSM is satisfactory, with a high tolerance for input data and ability to be used in different building scenarios, providing new ideas for studying building height estimates.
Building boundary optimization is an essential post-process step for building extraction (by image classification). However, current boundary optimization methods through smoothing or line fitting principles are unable to optimize complex buildings. In response to this limitation, this paper proposes an object-oriented building contour optimization method via an improved generalized gradient vector flow (GGVF) snake model and based on the initial building contour results obtained by a classification method. First, to reduce interference from the adjacent non-building object, each building object is clipped via their extended minimum bounding rectangles (MBR). Second, an adaptive threshold Canny edge detection is applied to each building image to detect the edges, and the progressive probabilistic Hough transform (PPHT) is applied to the edge result to extract the line segments. For those cases with missing or wrong line segments in some edges, a hierarchical line segments reconstruction method is designed to obtain complete contour constraint segments. Third, accurate contour constraint segments for the GGVF snake model are designed to quickly find the target contour. With the help of the initial contour and constraint edge map for GGVF, a GGVF force field computation is executed, and the related optimization principle can be applied to complex buildings. Experimental results validate the robustness and effectiveness of the proposed method, whose contour optimization has higher accuracy and comprehensive value compared with that of the reference methods. This method can be used for effective post-processing to strengthen the accuracy of building extraction results.
Building height (BH) estimation is crucial for urban spatial planning and development. BH estimation using digital surface model data involves obtaining ground and roof elevations. However, vegetation and shadows around buildings affect the selection of the required elevation, resulting in large BH estimation errors. In highly urbanized areas, buildings of similar heights often have similar characteristics and spatial proximity, which have reference significance in BH estimation but are rarely utilized. Herein, we propose a BH estimation method based on BIRCH clustering and a random forest (RF) model. We obtain the initial BH results using a method based on the optimal ground search area and a multi-index evaluation. BIRCH clustering and an RF classification model are used to match buildings of similar heights based on their spatial distance and attribute characteristics. Finally, the BH is adjusted based on the ground elevation obtained from the secondary screening and the BH matching. The validation results from two areas with over 12,000 buildings show that the proposed method reduces the root-mean-square error of the final BH results compared with the initial results. Comparing the obtained height maps shows that the final results produce a relatively accurate BH in areas with high shading and vegetation coverage, as well as in areas with dense buildings. Thus, the proposed method has been validated for its effectiveness and reliability.
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