Automated Ortho-Rectification of UAV-Based Hyperspectral Data over an Agricultural Field Using Frame RGB Imagery

Low-cost Unmanned Airborne Vehicles (UAVs) equipped with consumer-grade imaging systems have emerged as a potential remote sensing platform that could satisfy the needs of a wide range of civilian applications. Among these applications, UAV-based agricultural mapping and monitoring have attracted significant attention from both the research and professional communities. The interest in UAV-based remote sensing for agricultural management is motivated by the need to maximize crop yield. Remote sensing-based crop yield prediction and estimation are primarily based on imaging systems with different spectral coverage and resolution (e.g., RGB and hyperspectral imaging systems). Due to the data volume, RGB imaging is based on frame cameras, while hyperspectral sensors are primarily push-broom scanners. To cope with the limited endurance and payload constraints of low-cost UAVs, the agricultural research and professional communities have to rely on consumer-grade and light-weight sensors. However, the geometric fidelity of derived information from push-broom hyperspectral scanners is quite sensitive to the available position and orientation established through a direct geo-referencing unit onboard the imaging platform (i.e., an integrated Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS). This paper presents an automated framework for the integration of frame RGB images, push-broom hyperspectral scanner data and consumer-grade GNSS/INS navigation data for accurate geometric rectification of the hyperspectral scenes. The approach relies on utilizing the navigation data, together with a modified Speeded-Up Robust Feature (SURF) detector and descriptor, for automating the identification of conjugate features in the RGB and hyperspectral imagery. The SURF modification takes into consideration the available direct geo-referencing information to improve the reliability of the matching procedure in the presence of repetitive texture within a mechanized agricultural field. Identified features are then used to improve the geometric fidelity of the previously ortho-rectified hyperspectral data. Experimental results from two real datasets show that the geometric rectification of the hyperspectral data was improved by almost one order of magnitude

Implications of Sensor Inconsistencies and Remote Sensing Error in the Use of Small Unmanned Aerial Systems for Generation of Information Products for Agricultural Management

Small, unmanned aerial systems (sUAS) for remote sensing represent a relatively new and growing technology to support decisions for agricultural operations. The size and power limitations of these systems present challenges for the weight, size, and capability of the sensors that can be carried, as well as the geographical coverage that is possible. These factors, together with a lack of standards for sensor technology, its deployment, and data analysis, lead to uncertainties in data quality that can be difficult to detect or characterize. These, in turn, limit comparability between data from different sources and, more importantly, imply limits on the analyses that can be accomplished with the data that are acquired with sUAS. This paper offers a simple statistical examination of the implications toward information products of an array of sensor data uncertainty issues. The analysis relies upon high-resolution data collected in 2016 over a commercial vineyard, located near Lodi, California, for the USDA Agricultural Research Service Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) Program. A Monte Carlo analysis is offered of how uncertainty in sensor spectral response and/or orthorectification accuracy can affect the estimation of information products of potential interest to growers, as illustrated in the form of common vegetation indices

Concept for classifying facade elements based on material, geometry and thermal radiation using multimodal UAV remote sensing

This paper presents a concept for classification of facade elements, based on the material and the geometry of the elements in addition to the thermal radiation of the facade with the usage of a multimodal Unmanned Aerial Vehicle (UAV) system. Once the concept is finalized and functional, the workflow can be used for energy demand estimations for buildings by exploiting existing methods for estimation of heat transfer coefficient and the transmitted heat loss. The multimodal system consists of a thermal, a hyperspectral and an optical sensor, which can be operational with a UAV. While dealing with sensors that operate in different spectra and have different technical specifications, such as the radiometric and the geometric resolution, the challenges that are faced are presented. Addressed are the different approaches of data fusion, such as image registration, generation of 3D models by performing image matching and the means for classification based on either the geometry of the object or the pixel values. As a first step towards realizing the concept, the result from a geometric calibration with a designed multimodal calibration pattern is presented

Hyperspectral imaging in environmental monitoring : a review of recent developments and technological advances in compact field deployable systems

The development and uptake of field deployable hyperspectral imaging systems within environmental monitoring represents an exciting and innovative development that could revolutionize a number of sensing applications in the coming decades. In this article we focus on the successful miniaturization and improved portability of hyperspectral sensors, covering their application both from aerial and ground-based platforms in a number of environmental application areas, highlighting in particular the recent implementation of low-cost consumer technology in this context. At present, these devices largely complement existing monitoring approaches, however, as technology continues to improve, these units are moving towards reaching a standard suitable for stand-alone monitoring in the not too distant future. As these low-cost and light-weight devices are already producing scientific grade results, they now have the potential to significantly improve accessibility to hyperspectral monitoring technology, as well as vastly proliferating acquisition of such datasets

Mapping water content in drying Antarctic moss communities using UAS-borne SWIR imaging spectroscopy

Antarctic moss beds are sensitive to climatic conditions, and both their survival and community composition are particularly influenced by the availability of liquid water over summer. As Antarctic regions increasingly face climate pressures (e.g., changing hydrology and heat waves), advancing capabilities to efficiently and non-destructively monitor water content in moss communities becomes a key research priority. Because of the complexity induced by multiple micro-climatic drivers and its fragility, tracking the evolution and responses of moss bed moisture requires monitoring methods that are non-intrusive, efficient, and spatially significant, such as the use of unoccupied aerial systems (UAS). In this study, we combine a multi-species drying laboratory experiment with short-wave infrared (SWIR) spectroscopy analyses to first develop a Random Forest regression Model (RFM) capable of predicting Antarctic moss turf water content (~5% error). The RFM was then applied to UAS-borne SWIR imaging data (900–1700 nm, resolution) of the moss beds at high spatial resolution (2 cm) across three sites in the vicinity of Casey Station, Antarctica. The sites differed in terrain, snow cover, and moisture availability to evaluate method capabilities under different conditions. Optimum RFM parameters and input variables (spectral indices and reflectance spectra) were determined. Maps of moss moisture were validated via acquiring moss spectra and water content (using sponges inserted into the moss turf) collected in situ, for which an exponential correlation (R2 = 0.72) was reported. RFM further allowed investigation of the influential spectral variables to model water content in moss and associated spectral water absorption features. We demonstrated that UAS-borne SWIR imaging is a promising new tool to map and quantify water content in Antarctic moss beds. Hyperspectral mapping facilitates the exploration of the spatial variability of moss health and enables the creation of a baseline against which changes in these moss communities can be measured

Extrinsic Parameter Calibration for Line Scanning Cameras on Ground Vehicles with Navigation Systems Using a Calibration Pattern

Line scanning cameras, which capture only a single line of pixels, have been increasingly used in ground based mobile or robotic platforms. In applications where it is advantageous to directly georeference the camera data to world coordinates, an accurate estimate of the camera's 6D pose is required. This paper focuses on the common case where a mobile platform is equipped with a rigidly mounted line scanning camera, whose pose is unknown, and a navigation system providing vehicle body pose estimates. We propose a novel method that estimates the camera's pose relative to the navigation system. The approach involves imaging and manually labelling a calibration pattern with distinctly identifiable points, triangulating these points from camera and navigation system data and reprojecting them in order to compute a likelihood, which is maximised to estimate the 6D camera pose. Additionally, a Markov Chain Monte Carlo (MCMC) algorithm is used to estimate the uncertainty of the offset. Tested on two different platforms, the method was able to estimate the pose to within 0.06 m / 1.05$^{\circ}$ and 0.18 m / 2.39$^{\circ}$. We also propose several approaches to displaying and interpreting the 6D results in a human readable way.Comment: Published in MDPI Sensors, 30 October 201

Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences

The aim of the Special Issue “Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences” was to present a selection of innovative studies using hyperspectral imaging (HSI) in different thematic fields. This intention reflects the technical developments in the last three decades, which have brought the capacity of HSI to provide spectrally, spatially and temporally detailed data, favoured by e.g., hyperspectral snapshot technologies, miniaturized hyperspectral sensors and hyperspectral microscopy imaging. The present book comprises a suite of papers in various fields of environmental sciences—geology/mineral exploration, digital soil mapping, mapping and characterization of vegetation, and sensing of water bodies (including under-ice and underwater applications). In addition, there are two rather methodically/technically-oriented contributions dealing with the optimized processing of UAV data and on the design and test of a multi-channel optical receiver for ground-based applications. All in all, this compilation documents that HSI is a multi-faceted research topic and will remain so in the future

Applications of Photogrammetry for Environmental Research

ISPRS International Journal of Geo-Information: special issue entitled "Applications of Photogrammetry for Environmental Research