  Accurate detection of individual tree crowns from remote sensing data poses a
significant challenge due to the dense nature of forest canopy and the presence
of diverse environmental variations, e.g., overlapping canopies, occlusions,
and varying lighting conditions. Additionally, the lack of data for training
robust models adds another limitation in effectively studying complex forest
conditions. This paper presents a novel method for detecting shadowed tree
crowns and provides a challenging dataset comprising roughly 50k paired
RGB-thermal images to facilitate future research for illumination-invariant
detection. The proposed method (ShadowSense) is entirely self-supervised,
leveraging domain adversarial training without source domain annotations for
feature extraction and foreground feature alignment for feature pyramid
networks to adapt domain-invariant representations by focusing on visible
foreground regions, respectively. It then fuses complementary information of
both modalities to effectively improve upon the predictions of an RGB-trained
detector and boost the overall accuracy. Extensive experiments demonstrate the
superiority of the proposed method over both the baseline RGB-trained detector
and state-of-the-art techniques that rely on unsupervised domain adaptation or
early image fusion. Our code and data are available:
https://github.com/rudrakshkapil/ShadowSense
