  Visual explanation of ``black-box'' models allows researchers in explainable
artificial intelligence (XAI) to interpret the model's decisions in a
human-understandable manner. In this paper, we propose interpretable class
activation mapping for tree crown detection (Crown-CAM) that overcomes
inaccurate localization & computational complexity of previous methods while
generating reliable visual explanations for the challenging and dynamic problem
of tree crown detection in aerial images. It consists of an unsupervised
selection of activation maps, computation of local score maps, and
non-contextual background suppression to efficiently provide fine-grain
localization of tree crowns in scenarios with dense forest trees or scenes
without tree crowns. Additionally, two Intersection over Union (IoU)-based
metrics are introduced to effectively quantify both the accuracy and inaccuracy
of generated explanations with respect to regions with or even without tree
crowns in the image. Empirical evaluations demonstrate that the proposed
Crown-CAM outperforms the Score-CAM, Augmented Score-CAM, and Eigen-CAM methods
by an average IoU margin of 8.7, 5.3, and 21.7 (and 3.3, 9.8, and 16.5)
respectively in improving the accuracy (and decreasing inaccuracy) of visual
explanations on the challenging NEON tree crown dataset.
