Abstract
The development of low-cost unmanned aerial vehicles (UAVs) and light weight imaging sensors has resulted in significant interest in their use for remote sensing applications. While significant attention has been paid to the collection, calibration, registration and mosaicking of data collected from small UAVs, the interpretation of these data into semantically meaningful information can still be a laborious task. A standard data collection and classification work-flow requires significant manual effort for segment size tuning, feature selection and rule-based classifier design. In this paper, we propose an alternative learning-based approach using feature learning to minimise the manual effort required. We apply this system to the classification of invasive weed species. Small UAVs are suited to this application, as they can collect data at high spatial resolutions, which is essential for the classification of small or localised weed outbreaks. In this paper, we apply feature learning to generate a bank of image filters that allows for the extraction of features that discriminate between the weeds of interest and background objects. These features are pooled to summarise the image statistics and form the input to a texton-based linear classifier that classifies an image patch as weed or background. We evaluated our approach to weed classification on three weeds of significance in Australia: water hyacinth, tropical soda apple and serrated tussock. Our results showed that collecting images at 5–10 m resulted in the highest classifier accuracy, indicated by F1 scores of up to 94%.
Highlights
The development of low-cost unmanned air vehicles (UAVs) and light-weight imaging sensors in the past decade has resulted in significant interest in their use for remote sensing applications.These applications include vegetation mapping [1,2,3,4,5], archeology [6,7,8], meteorology [9] and high risk activities, such as volcano monitoring [10] and forest fire mapping [11]
We propose an alternative approach to minimise the effort in feature design by coupling a remote sensing UAV with feature learning
We developed a learning-based algorithm for weed classification
Summary
The development of low-cost unmanned air vehicles (UAVs) and light-weight imaging sensors in the past decade has resulted in significant interest in their use for remote sensing applications. These applications include vegetation mapping [1,2,3,4,5], archeology [6,7,8], meteorology [9] and high risk activities, such as volcano monitoring [10] and forest fire mapping [11]. They can be operated frequently (weather permitting) at low altitudes, allowing the collection of data at high spatial and temporal resolutions
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