|Title||Joint height estimation and semantic labeling of monocular aerial images with CNNS|
|Author(s)||Srivastava, Shivangi; Volpi, Michele; Tuia, Devis|
|Source||In: 2017 IEEE International Geoscience and Remote Sensing Symposium. - Institute of Electrical and Electronics Engineers Inc. (International Geoscience and Remote Sensing Symposium (IGARSS) ) - ISBN 9781509049523 - p. 5173 - 5176.|
|Event||37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017, Fort Worth, 2017-07-23/2017-07-28|
|Publication type||Contribution in proceedings|
|Keyword(s)||Convolutional neural networks - Digital Surface Model - Multitask learning - Semantic labeling|
We aim to jointly estimate height and semantically label monocular aerial images. These two tasks are traditionally addressed separately in remote sensing, despite their strong correlation. Therefore, a model learning both height and classes jointly seems advantageous and so, we propose a multitask Convolutional Neural Network (CNN) architecture with two losses: one performing semantic labeling, and another predicting normalized Digital Surface Model (nDSM) from the pixel values. Since the nDSM/height information is used only in the second loss, there is no need to have a nDSM map at test time, and the model can estimate height automatically on new images. We test our proposed method on a set of sub-decimeter resolution images and show that our model equals the performances of two separate models, but at the cost of a single one.