Weakly supervised deep convolutional networks for fine-grained object recognition in multispectral images
Cinbiş, R. G.
International Geoscience and Remote Sensing Symposium, IGARSS 2019
Institute of Electrical and Electronics Engineers Inc.
1478 - 1481
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The challenging task of training object detectors for fine-grained classification faces additional difficulties when there are registration errors between the image data and the ground truth. We propose a weakly supervised learning methodology for the classification of 40 types of trees by using fixed-sized multispectral images with a class label but with no exact knowledge of the object location. Our approach consists of an end-to-end trainable convolutional neural network with separate branches for learning class-specific and location-specific scoring of image regions. Comparative experiments show that the proposed method simultaneously learns to detect and classify the objects of interest with high accuracy.