Commission I,WG ICWG I/Vb KEY WORDS: Precision agriculture, biomass, crop, narrow band indices
ABSTRACT:The indirect estimation of leaf area index (LAI) in large spatial scales is crucial for several environmental and agricultural applications. To this end, in this paper, we compare and evaluate LAI estimation in vineyards from different UAV imaging datasets. In particular, canopy levels were estimated from i.e., (i) hyperspectral data, (ii) 2D RGB orthophotomosaics and (iii) 3D crop surface models. The computed canopy levels have been used to establish relationships with the measured LAI (ground truth) from several vines in Nemea, Greece. The overall evaluation indicated that the estimated canopy levels were correlated (r 2 >73%) with the in-situ, ground truth LAI measurements. As expected the lowest correlations were derived from the calculated greenness levels from the 2D RGB orthomosaics. The highest correlation rates were established with the hyperspectral canopy greenness and the 3D canopy surface models. For the later the accurate detection of canopy, soil and other materials in between the vine rows is required. All approaches tend to overestimate LAI in cases with sparse, weak, unhealthy plants and canopy.
Conventional orthorectification software cannot handle surface occlusions and image visibility. The approach presented here synthesizes related work in photogrammetry and computer graphics/vision to automatically produce orthographic and perspective views based on fully 3D surface data (supplied by laser scanning). Surface occlusions in the direction of projection are detected to create the depth map of the new image. This information allows identifying, by visibility checking through back-projection of surface triangles, all source images which are entitled to contribute color to each pixel of the novel image. Weighted texture blending allows regulating the local radiometric contribution of each source image involved, while outlying color values are automatically discarded with a basic statistical test. Experimental results from a close-range project indicate that this fusion of laser scanning with multiview photogrammetry could indeed combine geometric accuracy with high visual quality and speed. A discussion of intended improvements of the algorithm is also included.
Abstract. The now widely available and highly popular among non-expert users, particularly in the context of UAV photogrammetry, Structure-from-Motion (SfM) pipelines have also further renewed the interest in the issue of automatic camera calibration. The well-documented requirements for robust self-calibration cannot be always met, e.g. due to restrictions in time and cost, absence of ground control and image tilt, terrain morphology, unsuitable flight configuration etc.; hence, camera pre-calibration is frequently recommended. In this context, users often resort to flexible, user-friendly tools for camera calibration based on 2D coded patterns (primarily ordinary chessboards). Yet, the physical size of such patterns poses obvious limitations. This paper discusses the alternative of extending the size of the calibration object by using multiple unordered coplanar chessboards, which might accommodate much larger imaging distances. This is done initially by a detailed simulation to show that – in terms of geometry – this could be a viable alternative to single patterns. A first algorithmic implementation is then laid out, and results from real multi-pattern configurations, both ordered and unordered, are successfully compared. However, aspects of the proposed approach need to be further studied for its reliable practical employment.
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