Abstract
Abstract. The use of multispectral imagery for monitoring biodiversity in ecosystems is becoming widespread. A key parameter of forest ecosystems is the distribution of dead wood. This work addresses the segmentation of individual dead tree crowns in nadir-view aerial infrared imagery. While dead vegetation produces a distinct spectral response in the near infrared band, separating adjacent trees within large swaths of dead stands remains a challenge. We tackle this problem by casting the segmentation task within the active contour framework, a mathematical formulation combining learned models of the object’s shape and appearance as prior information. We explore the use of a deep convolutional generative adversarial network (DCGAN) in the role of the shape model, replacing the original linear mixture-of-eigenshapes formulation. Also, we rely on probabilities obtained from a deep fully convolutional network (FCN) as the appearance prior. Experiments conducted on manually labeled reference polygons show that the DCGAN is able to learn a low-dimensional manifold of tree crown shapes, outperforming the eigenshape model with respect to the similarity of the reproduced and referenced shapes on about 45 % of the test samples. The DCGAN is successful mostly for less convex shapes, whereas the baseline remains superior for more regular tree crown polygons.
Highlights
From an ecological perspective, monitoring the state and quantity of coarse woody debris (CWD) is a crucial task due to its role in forest biodiversity, nutrient cycles, and as carbon sequestration (Harmon et al, 1986)
It is well known that dead vegetation produces a distinct reflectance signature in the infrared spectral band (Jensen, 2006), remote sensing based on passive optical sensors has been widely used for detecting diseased and dead trees (Wang et al, 2007; Vogelmann, 1990; Heurich et al, 2010; Polewski et al, 2016)
We propose to combine the strengths of Generative adversarial networks (GANs) and fully convolutional networks within the energy minimization framework of active contour segmentation (Cremers et al, 2007)
Summary
From an ecological perspective, monitoring the state and quantity of coarse woody debris (CWD) is a crucial task due to its role in forest biodiversity, nutrient cycles, and as carbon sequestration (Harmon et al, 1986). It is well known that dead vegetation produces a distinct reflectance signature in the infrared spectral band (Jensen, 2006), remote sensing based on passive optical sensors has been widely used for detecting diseased and dead trees (Wang et al, 2007; Vogelmann, 1990; Heurich et al, 2010; Polewski et al, 2016). Advances in optical sensor technology have capacitated the widespread use of high-resolution aerial images in large-scale forest inventories. This opens up new possibilities for mapping dead vegetation with unprecedented precision and spatial coverage. The centimeterresolution aerial imagery reveals much more complexity in the shape of dead tree crowns than was possible with the previous generations of sensors. The interactions of adjacent dead tree crowns can lead to the formation of complex aggregates that are difficult to separate into individual trees
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
