WEAKLY SUPERVISED DEEP CONVOLUTIONAL NETWORKS FOR FINE-GRAINED OBJECT RECOGNITION IN MULTISPECTRAL IMAGES

Download
2019-01-01
Aygunes, Bulut
AKSOY, SELİM
Cinbiş, Ramazan Gökberk
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.
Citation Formats
B. Aygunes, S. AKSOY, and R. G. Cinbiş, “WEAKLY SUPERVISED DEEP CONVOLUTIONAL NETWORKS FOR FINE-GRAINED OBJECT RECOGNITION IN MULTISPECTRAL IMAGES,” presented at the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Yokohama, JAPAN, 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/39730.