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Abstract. Unmanned Aerial Vehicle (UAV) missions often collect large volumes of imagery data. However, not all images will have useful information, or be of sufficient quality. Manually sorting these images and selecting useful data are both time consuming and prone to interpreter bias. Deep neural network algorithms are capable of processing large image datasets and can be trained to identify specific targets. Generative Adversarial Networks (GANs) consist of two competing networks, Generator and Discriminator that can analyze, capture, and copy the variations within a given dataset. In this study, we selected a variant of GAN called Conditional-GAN that incorporates an additional label parameter, for identifying epiphytes in photos acquired by a UAV in forests within Costa Rica. We trained the network with 70%, 80%, and 90% of 119 photos containing the target epiphyte, Werauhia kupperiana (Bromeliaceae) and validated the algorithm’s performance using a validation data that were not used for training. The accuracy of the output was measured using structural similarity index measure (SSIM) index and histogram correlation (HC) coefficient. Results obtained in this study indicated that the output images generated by C-GAN were similar (average SSIM = 0.89–0.91 and average HC 0.97–0.99) to the analyst annotated images. However, C-GAN had difficulty to identify when the target plant was away from the camera, was not well lit, or covered by other plants. Results obtained in this study demonstrate the potential of C-GAN to reduce the time spent by botanists to identity epiphytes in images acquired by UAVs.

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Abstract. Deep learning (DL) algorithms are widely used in object detection such as roads, vehicles, buildings, etc., in aerial images. However, the object detection task is still considered challenging for detecting complex structures, oil pads are one such example: due to its shape, orientation, and background reflection. A recent study used Faster Region-based Convolutional Neural Network (FR-CNN) to detect a single oil pad from the center of the image of size 256 × 256. However, for real-time applications, it is necessary to detect multiple oil pads from aerial images irrespective of their orientation. In this study, FR-CNN was trained to detect multiple oil pads. We cropped images from high spatial resolution images to train the model containing multiple oil pads. The network was trained for 100 epochs using 164 training images and tested with 50 images under 3 different categories. with images containing: single oil pad, multiple oil pad and no oil pad. The model performance was evaluated using standard metrics: precision, recall, F1-score. The final model trained for multiple oil pad detection achieved a weighted average for 50 images precision of 0.67, recall of 0.80, and f1 score of 0.73. The 0.80 recall score indicates that 80% of the oil pads were able to identify from the given test set. The presence of instances in test images like cleared areas, rock structures, and sand patterns having high visual similarity with the target resulted in a low precision score.

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