Automated Georectification and Mosaicking of UAV-Based Hyperspectral Imagery from Push-Broom Sensors

Abstract

Hyperspectral systems integrated on unmanned aerial vehicles (UAV) provide unique opportunities to conduct high-resolution multitemporal spectral analysis for diverse applications. However, additional time-consuming rectification efforts in postprocessing are routinely required, since geometric distortions can be introduced due to UAV movements during flight, even if navigation/motion sensors are used to track the position of each scan. Part of the challenge in obtaining high-quality imagery relates to the lack of a fast processing workflow that can retrieve geometrically accurate mosaics while optimizing the ground data collection efforts. To address this problem, we explored a computationally robust automated georectification and mosaicking methodology. It operates effectively in a parallel computing environment and evaluates results against a number of high-spatial-resolution datasets (mm to cm resolution) collected using a push-broom sensor and an associated RGB frame-based camera. The methodology estimates the luminance of the hyperspectral swaths and coregisters these against a luminance RGB-based orthophoto. The procedure includes an improved coregistration strategy by integrating the Speeded-Up Robust Features (SURF) algorithm, with the Maximum Likelihood Estimator Sample Consensus (MLESAC) approach. SURF identifies common features between each swath and the RGB-orthomosaic, while MLESAC fits the best geometric transformation model to the retrieved matches. Individual scanlines are then geometrically transformed and merged into a single spatially continuous mosaic reaching high positional accuracies only with a few number of ground control points (GCPs). The capacity of the workflow to achieve high spatial accuracy was demonstrated by examining statistical metrics such as RMSE, MAE, and the relative positional accuracy at 95% confidence level. Comparison against a user-generated georectification demonstrates that the automated approach speeds up the coregistration process by 85%.Remote Sens. 2020, 12, 34 2 of 25 understanding [2]. One of the key constraints in our observation capacity relates to the compromise between spatial and temporal resolution, i.e., space-based platforms tend to suffer from either spatial or temporal restrictions that affect the frequency and fidelity of retrievals. Within the last decade, developments in remote sensing using unmanned aerial vehicles (UAV) have provided a sensor-flexible platform that has lowered operational costs, while providing unprecedented spatial (<10 cm) and on-demand temporal resolution [3,4]. To leverage these technological advances, hyperspectral camera systems capturing radiances across the visible and near-infrared portions of the spectrum [5] have been developed, with diverse applications being presented in agriculture [6][7][8], forestry [9], and mining [10] studies. However, while UAV technology has progressed rapidly and there is a level of maturity in many sensing capabilities [11], routine application of hyperspectral imaging systems remains chall...

A Robust Stochastic Approach to Mineral Hyperspectral Analysis for Geometallurgy

“…The first one was the emergence of laboratory and field hyperspectral acquisition equipment, such as LabSpec [5][6][7], TerraSpec [24,25] and Hylogger [26] that are single pixel devices, and CoreScan [4] which is the most well-known multi-pixel laboratory platform. Additionally, there are diverse models of hyperspectral cameras [26]-most of them push broom style scanners-that are multi-pixel devices, for which the companies SPECIM [27][28][29][30][31] and Headwall [32,33] have established the industry standard. The second milestone was the development of software packages for hyperspectral data analysis-such as ENVI [34], and the geological analysis-oriented application The Spectral Geologist (TSG) [35,36] developed by CSIRO-that use classical spectrography approaches and statistical proprietary unmixing technique developments (such as the iterated constrained endmembers method [8]) to perform semi-quantitative mineralogy on a single spectrum basis.…”

An Efficient Method for Generating UAV-Based Hyperspectral Mosaics Using Push-Broom Sensors

“…Finally, popular computer vision algorithms were also tested in coregistration approaches [19]- [22]. SIFT (Scaleinvariant Feature Transform), SURF (Speeded Up Robust Features), FAST (Features from Accelerated Segment Test), and BRIEF (Binary Robust Independent Elementary Features) have been used to perform a robust feature detection of key points between adjacent frames.…”

“…SIFT (Scaleinvariant Feature Transform), SURF (Speeded Up Robust Features), FAST (Features from Accelerated Segment Test), and BRIEF (Binary Robust Independent Elementary Features) have been used to perform a robust feature detection of key points between adjacent frames. To explore the most recent developments in different domains, Angel et al [22] proposed a fully automatic workflow to produce highly accurate georectified UAV-based hyperspectral mosaics collected by push-broom sensors, requiring a small number of GCPs. The Headwall Nano-Hyperspec push-broom sensor was used over two experimental crop sites.…”

Accuracy of 3D Landscape Reconstruction without Ground Control Points Using Different UAS Platforms

“…The high accuracy and precision (i.e., cm error) of results obtained for 2D (e.g., orthomosaics) and 3D (e.g., digital surface model-DSM) mapping, based on structure from motion with multiview stereo photogrammetry (SfM-MVS), is transforming most disciplines where studies need to characterize areas Drones 2020, 4, 13 2 of 26 up to~100 hectares [1,27,28]. In addition, more operationally demanding systems such as hyperspectral pushbroom sensors [22,29], thermal imagers [24] and LiDAR [30] are being implemented on UASs, providing new insights for data fusion and advanced data analysis [31]. Centimeter level accuracies are particularly important in these multi-sensor applications where spatial alignment of the different datasets is needed.…”