Image Texture as Quality Indicator for Optical DEM Generation: Geomorphic Applications in the Arid Central Andes

Abstract

The generation of Digital Elevation Models (DEMs) through stereogrammetry of optical satellite images has gained great popularity across various disciplines. For the analysis of these DEMs, it is important to understand the influence of the input data and different processing steps and parameters employed during stereo correlation. Here, we explore the effects that image texture, as well as the use of different matching algorithms (Block Matching (BM) and More Global Matching (MGM)), can have on optical DEMs derived from the flexible, open-source Ames Stereo Pipeline. Our analysis relies on a ∼2700 km2 clip of a SPOT6 tristereo scene covering the hyperarid, vegetation-free Pocitos Basin and adjacent mountain ranges in the northwestern Argentine Andes. A large, perfectly flat salt pan (paleolake bed) that covers the center of this basin is characterized by strong contrasts in image texture, providing a unique opportunity to quantitatively study the relationship between image texture and DEM quality unaffected by topography. Our findings suggest that higher image texture, measured by panchromatic variance, leads to lower DEM uncertainty. This improvement continues up to ∼103 panchromatic variance, above which further improvements in DEM quality are independent of local image texture but instead may have sensor or geometric origins. Based on this behavior, we propose that image texture may serve as an important proxy of DEM quality prior to stereo correlation and can help to set adequate processing parameters. With respect to matching algorithms, we observe that MGM improves matching in low-texture areas and overall generates a smoother surface that still preserves complex, narrow (i.e., ridge and valley) features. Based on this sharper representation of the landscape, we conclude that MGM should be preferred for geomorphic applications relying on stereo-derived DEMs. However, we note that the correlation kernel selected for stereo-matching must be carefully chosen depending on local image texture, whereby larger kernels generate more accurate matches (less artifacts) at the cost of smoothing results. Overall, our analysis suggests a path forward for the processing and fusion of overlapping satellite images with suitable view-angle differences to improve final DEMs.

Iterative bundle adjustment without ground control points on SPOT-7 stereogrammetry and its effect on DEM geometric accuracy

“…Therefore, prior to the vertical accuracy assessment, the produced DEM then needs to be properly checked for its horizontal shifts. These horizontal shifts assessment is what we found missing in the previous gcp-less studies 18,19 . In this study, our aim is to perform an iterative-bundle adjustment without using GCP on a tri-stereo SPOT-7 imagery and compared the vertical and horizontal accuracy with four other approaches.…”

“…Its ability to process the Earth Observation (EO) satellite imagery, however, has been applied and investigated by several studies. These studies focuses on monitoring ice sheets and glaciers 16 , arid high relief mountains 18 , and ice mass balance using DEM difference principle 20,31 , other focuses on a more general global elevation change 20 .…”

“…Stereo matching then searches on a specific 2-D window kernel value. In this study, a kernel value of 21 x 21 pixel was chosen as it shown a good results from other studies 16,18 . For our study, two different stereo-correlator algorithm was used.…”

“…The recent development shows recommendation on SPOT-6 or SPOT-7 to specifically performed not just a single bundle adjustment. With the help of seed DEM, another bundle adjustment then can be performed [18][19][20] .…”

A New Texture Spectrum Based on Parallel Encoded Texture Unit and Its Application on Image Classification: A Potential Prospect for Vision Sensing

“…Currently, image recognition based on the textural characteristics of images has a large number of applications in biomedicine [1,2], object detection [3,4], medical diagnostics [5][6][7], and remote sensing [8,9], among others. Because of this, around the world there is a large number of research groups that carry out research on texture analysis, extraction, and/or classification.…”