Quality assessment of high density digital surface model over different land cover classes

Abstract

Background and Purpose: Recent research on generation of digital surface models (DSMs) using image matching methods revealed a great potential of DSM application in forestry, especially in forest inventory. However, research dealing with DSM generation from digital aerial images are still lacking in Croatia. Therefore, the main objective of this study was to present the workflow for generating high density DSM from colour infrared (CIR) digital stereo aerial images using area-based image matching algorithm. Materials and Methods:The high density DSM was generated from colour infrared digital aerial stereo images using Dense DTM algorithm of PHOTOMOD software -an area-based image matching algorithm which operates on the principle of cross-correlation approach. To evaluate the quality of the generated DSM, an agreement assessment with manual stereo measurements was conducted over three different land cover classes (forests, shrubs, grasslands) INTRODUCTION Photogrammetry is a typical engineering discipline that is greatly influenced by developments in computer science and electronics (1). This has become especially evident over the past thirty years during which photogrammetry has gone through transition from analogue, over analytical to digital photogrammetry (1, 2). During this development period, analogue aerial photographs as well as analogue and analytical stereo instruments have been replaced with digital aerial images and digital photogrammetric workstations (3,4). Current digital aerial photogrammetric cameras are capable of capturing digital aerial images of very high spatial resolution with ground sampling distance (GSD) of only several centimetres (5). In addition, the radiometric resolution of digital images is superior in comparison to the resolution of analogue photographs (6). Such quality digital images in combination with digital photogrammetric workstations have led to automation, facilitation and improvements in performance of many demanding photogrammetric procedures (aerial triangulation, digital terrain models generation, orthophoto generation, etc.) (7,8).The automation in digital photogrammetry is based on stereo photogrammetric processing called image matching, also referred to as image correlation (9). The aim of image matching is to find corresponding points in two or more stereo images based on their radiometric and geometric similarity (10). The result of image matching is image 3D point cloud which is then used to generate digital terrain model (DTM) which represents the elevation of the Earth's terrain (bare ground) or digital surface model (DSM) which represents the elevation of the Earth's surface including the human-made (e.g. buildings) or natural (e.g. trees) features.In recent years, the automatic DTM and DSM generation through image matching has encountered great interest among the researchers worldwide (11). Various image matching algorithms and photogrammetric software packages have been developed and have become commercially available (11). The main image matchi...

The Evaluation of Photogrammetry-Based DSM from Low-Cost UAV by LiDAR-Based DSM

“…Since such horizontal errors may produce greater vertical errors [36], especially for surfaces with great variations in height on a small area (e.g. forest) [25,37], the lower vertical agreement of DSM P with DSM L is understandable. This is especially evident in Figure 3c, which shows a comparison of DSMs' profiles through the exemplary area.…”

“…moving trees, occlusions, shadows, images radiometric quality, etc. ), which seriously affect the image matching procedure, and thus DSM quality [23,25,40,41].…”

“…[23][24][25] which revealed that many factors may influence on their quality, especially in complex forest structure. The research on DSM quality obtained from UAV images are still lacking (especially in South-east European region), but it can be assumed that apart from technical characteristics related to UAV (e.g.…”

Automated Building Detection in Dense Point Cloud and Update of Open Source Data Bases

“…Smoothing the produced surface is recommended ( Figure 3). For each other point of the initial cloud, its distance from the DTM is calculated as a scale value (Balenović et al, 2016). Objects with low elevation from the DTM are removed from the cloud.…”

A Comparison of Stand-Level Volume Estimates from Image-Based Canopy Height Models of Different Spatial Resolutions

“…The spatial resolution (pixel size) of the resulting DSM thus corresponds to the image pixel size (0.30 m × 0.30 m). A detailed description of the procedure of the DSM generation using Dense DTM, as well as parameters settings, can be found in Balenović et al [53]. Matching errors, i.e., spikes (points above or below the surface) were removed from DSM using the DSM filters.…”