The 3D Elevation Program: summary of program direction

Snyder, Gregory I.

doi:10.3133/fs20123089

Cited by 13 publications

(5 citation statements)

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“…Time series surveys enable damage and deterioration analyses at unprecedented detail across multiple scales. Currently, an initiative, the 3D elevation plan (3DEP) is underway to obtain airborne lidar data across the entire U.S. at meter level resolution (Snyder, 2012).…”

Section: Use Of Lidar In Remote Sensingmentioning

confidence: 99%

Contour Connection Method for automated identification and classification of landslide deposits

Leshchinsky

Olsen

Tanyu

2015

Computers & Geosciences

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Section: Use Of Lidar In Remote Sensingmentioning

confidence: 99%

Contour Connection Method for automated identification and classification of landslide deposits

Leshchinsky

Olsen

Tanyu

2015

Computers & Geosciences

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“…This pairwise correlation is computed without regard to the particular timing of values, so as long as Figure 13. Hillshaded relief of the Alaska study area based on interferometric synthetic aperture radar (IFSAR) data [36], with ETM+ missing data stripes from one acquisition date overlaid in red. Note the tendency for the stripes to align with topographical features (black arrow).…”

Section: Discussionmentioning

confidence: 99%

Using Window Regression to Gap-Fill Landsat ETM+ Post SLC-Off Data

Brooks¹,

Wynne²,

Thomas³

2018

Remote Sensing

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The continued development of algorithms using multitemporal Landsat data creates opportunities to develop and adapt imputation algorithms to improve the quality of that data as part of preprocessing. One example is de-striping Enhanced Thematic Mapper Plus (ETM+, Landsat 7) images acquired after the Scan Line Corrector failure in 2003. In this study, we apply window regression, an algorithm that was originally designed to impute low-quality Moderate Resolution Imaging Spectroradiometer (MODIS) data, to Landsat Analysis Ready Data from 2014-2016. We mask Operational Land Imager (OLI; Landsat 8) image stacks from five study areas with corresponding ETM+ missing data layers, using these modified OLI stacks as inputs. We explored the algorithm's parameter space, particularly window size in the spatial and temporal dimensions. Window regression yielded the best accuracy (and moderately long computation time) with a large spatial radius (a 7 × 7 pixel window) and a moderate temporal radius (here, five layers). In this case, root mean square error for deviations from the observed reflectance ranged from 3.7-7.6% over all study areas, depending on the band. Second-order response surface analysis suggested that a 15 × 15 pixel window, in conjunction with a 9-layer temporal window, may produce the best accuracy. Compared to the neighborhood similar pixel interpolator gap-filling algorithm, window regression yielded slightly better accuracy on average. Because it relies on no ancillary data, window regression may be used to conveniently preprocess stacks for other data-intensive algorithms. [13], which classify each image and impute based on the relationships between pixels sharing a class; and the Profile-Based Interpolator [14], which leverages multitemporal trajectories and geospatial similarities to impute. The third category of gap-filling algorithms, wherein the source images include non-ETM+ data, includes the algorithms from the second category when they employ additional data from other Landsat platforms. Fusion with other sensor platforms such as the Moderate Resolution Imaging Spectroradiometer (MODIS) [15] falls into this category as well. Additionally, Wijedasa [16] used post-classification mosaics to generate a seamless map of peat swamp disturbance based on ETM+ data, though this process required supervised classification. A complete review of these algorithms is beyond the scope of this study, but each of them has advantages and disadvantages, the latter typically centering around the tradeoff of slow computation times against per-pixel interpolation accuracy.However, since the Landsat archive was made freely available to the public [17], it has become common to incorporate the full history, from the Thematic Mapper era to the present day, in time series research [18][19][20]. Many of these studies rely on algorithms that leverage the temporal richness of the Landsat archive to predict image data in other dates (i.e., between-images interpolation), but they do not necessarily perform well at imputing missing...

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“…To establish an accurate ground truth, we acquire publicly available laser point cloud data, processing the data to derive the ground truth of surface elevation. This point cloud data are sourced from the 3D Elevation Program (3DEP) project developed by the United States Geological Survey [35].…”

Section: Study Area and Data Preparationmentioning

confidence: 99%

Disparity Refinement for Stereo Matching of High-Resolution Remote Sensing Images Based on GIS Data

Wang,

Jiang,

Wang

et al. 2024

Remote Sensing

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With the emergence of the Smart City concept, the rapid advancement of urban three-dimensional (3D) reconstruction becomes imperative. While current developments in the field of 3D reconstruction have enabled the generation of 3D products such as Digital Surface Models (DSM), challenges persist in accurately reconstructing shadows, handling occlusions, and addressing low-texture areas in very-high-resolution remote sensing images. These challenges often lead to difficulties in calculating satisfactory disparity maps using existing stereo matching methods, thereby reducing the accuracy of 3D reconstruction. This issue is particularly pronounced in urban scenes, which contain numerous super high-rise and densely distributed buildings, resulting in large disparity values and occluded regions in stereo image pairs, and further leading to a large number of mismatched points in the obtained disparity map. In response to these challenges, this paper proposes a method to refine the disparity in urban scenes based on open-source GIS data. First, we register the GIS data with the epipolar-rectified images since there always exists unignorable geolocation errors between them. Specifically, buildings with different heights present different offsets in GIS data registering; thus, we perform multi-modal matching for each building and merge them into the final building mask. Subsequently, a two-layer optimization process is applied to the initial disparity map based on the building mask, encompassing both global and local optimization. Finally, we perform a post-correction on the building facades to obtain the final refined disparity map that can be employed for high-precision 3D reconstruction. Experimental results on SuperView-1, GaoFen-7, and GeoEye satellite images show that the proposed method has the ability to correct the occluded and mismatched areas in the initial disparity map generated by both hand-crafted and deep-learning stereo matching methods. The DSM generated by the refined disparity reduces the average height error from 2.2 m to 1.6 m, which demonstrates superior performance compared with other disparity refinement methods. Furthermore, the proposed method is able to improve the integrity of the target structure and present steeper building facades and complete roofs, which are conducive to subsequent 3D model generation.

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The 3D Elevation Program: summary of program direction

Cited by 13 publications

References 0 publications

Contour Connection Method for automated identification and classification of landslide deposits

Contour Connection Method for automated identification and classification of landslide deposits

Using Window Regression to Gap-Fill Landsat ETM+ Post SLC-Off Data

Disparity Refinement for Stereo Matching of High-Resolution Remote Sensing Images Based on GIS Data

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