The use of UAVs finds application in a variety of fields, among which are the small scale surveys for environmental protection application. In this frame some experimental tests were carried out at Politecnico di Milano to assess metric accuracies of images acquired by UAVs and derived photogrammetric products. A block of 5 strips and 49 photos was taken by fixed wing system SenseFly, carrying a camera Canon Ixus 220HS on a rural area included in an Italian Park. Images are processed through bundle adjustment, automatic DEM extraction and orthoimages production steps with several software packages, with the aim to evaluate their characteristics, capabilities and weaknesses. The software packages tested were Erdas-LPS, EyeDEA (University of Parma), Agisoft Photoscan, Pix4UAV, PhotoModeler Scanner. For the georeferencing of the block 16 pre-signalized ground control points were surveyed in the area through GPS (NRTK survey). Comparison of results is given in terms of differences among orientation parameters and their accuracies. Moreover, comparisons among different digital surface models are evaluated. Furthermore, exterior orientation parameters, image points and ground points coordinates, obtained by the various software packages, were used as initial values in a comparative adjustment made by scientific in-house software. Paper confirms that computer vision software are faster in computation and, even if their main goal is not to pursue high accuracy in points coordinates determination, they seems to produce results comparable to those obtainable with standard photogrammetric approach. Agisoft Photoscan seems in this case to yield the best results in terms of quality of photogrammetric products
In the frame of project FoGLIE (Fruition of Goods and Landscape in Interactive Environment), UAS were used to survey a park area in its less accessible zones, for scenic and stereoscopic videos, 3D modeling and vegetation monitoring. For this last application, specifically, through the acquisition of very high resolution images taken with two UAS-borne compact cameras (RGB and NIR), a DSM of a small vegetated area and the corresponding orthoimages were produced and co-registered. Planimetric and height accuracies in block adjustments and orthophotos are in the range of 0.10 m horizontally and 0.15 m in height. Then, after the derivation of synthetic channels, both unsupervised classification and supervised one were performed in order to test the algorithms' ability to distinguish between different bushes and trees species: some of them were correctly classified by the latter method but misclassifications still remain. The overall accuracy for the unsupervised classification is about 50% while the supervised one yields an overall accuracy around 80%.
This paper focuses on the use of ultra-high resolution Unmanned Aircraft Systems (UAS) imagery to classify tree species. Multispectral surveys were performed on a plant nursery to produce Digital Surface Models and orthophotos with ground sample distance equal to 0.01 m. Different combinations of multispectral images, multi-temporal data, and texture measures were employed to improve classification. The Grey Level Co-occurrence Matrix was used to generate texture images with different window sizes and procedures for optimal texture features and window size selection were investigated. The study evaluates how methods used in Remote Sensing could be applied on ultra-high resolution UAS images. Combinations of original and derived bands were classified with the Maximum Likelihood algorithm, and Principal Component Analysis was conducted in order to understand the correlation between bands. The study proves that the use of texture features produces a significant increase of the Overall Accuracy, whose values change from 58% to 78% or 87%, depending on components reduction. The improvement given by the introduction of texture measures is highlighted even in terms of User's and Producer's Accuracy. For classification purposes, the inclusion of texture can compensate for difficulties of performing multi-temporal surveys.
ABSTRACT:UAVs systems represent a flexible technology able to collect a big amount of high resolution information, both for metric and interpretation uses. In the frame of experimental tests carried out at Dept. ICA of Politecnico di Milano to validate vector-sensor systems and to assess metric accuracies of images acquired by UAVs, a block of photos taken by a fixed wing system is triangulated with several software. The test field is a rural area included in an Italian Park ("Parco Adda Nord"), useful to study flight and imagery performances on buildings, roads, cultivated and uncultivated vegetation. The UAV SenseFly, equipped with a camera Canon Ixus 220HS, flew autonomously over the area at a height of 130 m yielding a block of 49 images divided in 5 strips. Sixteen pre-signalized Ground Control Points, surveyed in the area through GPS (NRTK survey), allowed the referencing of the block and accuracy analyses. Approximate values for exterior orientation parameters (positions and attitudes) were recorded by the flight control system. The block was processed with several software: Erdas-LPS, EyeDEA (Univ. of Parma), Agisoft Photoscan, Pix4UAV, in assisted or automatic way. Results comparisons are given in terms of differences among digital surface models, differences in orientation parameters and accuracies, when available. Moreover, image and ground point coordinates obtained by the various software were independently used as initial values in a comparative adjustment made by scientific in-house software, which can apply constraints to evaluate the effectiveness of different methods of point extraction and accuracies on ground check points.
ABSTRACT:The use of aerial imagery acquired by Unmanned Aerial Vehicles (UAVs) is scheduled within the FoGLIE project (Fruition of Goods Landscape in Interactive Environment): it starts from the need to enhance the natural, artistic and cultural heritage, to produce a better usability of it by employing audiovisual movable systems of 3D reconstruction and to improve monitoring procedures, by using new media for integrating the fruition phase with the preservation ones. The pilot project focus on a test area, Parco Adda Nord, which encloses various goods' types (small buildings, agricultural fields and different tree species and bushes). Multispectral high resolution images were taken by two digital compact cameras: a Pentax Optio A40 for RGB photos and a Sigma DP1 modified to acquire the NIR band. Then, some tests were performed in order to analyze the UAV images' quality with both photogrammetric and photo-interpretation purposes, to validate the vector-sensor system, the image block geometry and to study the feasibility of tree species classification. Many pre-signalized Control Points were surveyed through GPS to allow accuracy analysis. Aerial Triangulations (ATs) were carried out with photogrammetric commercial software, Leica Photogrammetry Suite (LPS) and PhotoModeler, with manual or automatic selection of Tie Points, to pick out pros and cons of each package in managing non conventional aerial imagery as well as the differences in the modeling approach. Further analysis were done on the differences between the EO parameters and the corresponding data coming from the on board UAV navigation system.
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