Object contra Context: Dual Local - Global Semantic Segmentation in Aerial Images

@paper{AAAIW1715177,

  author = {Alina Marcu and Marius Leordeanu},

  title = {Object Contra Context: Dual Local-Global Semantic Segmentation in Aerial Images},

  conference = {AAAI Workshops},

  year = {2017},

  keywords = {Aerial Images; Context; Convolutional Neural Networks; Semantic Segmentation},

  abstract = {The importance of visual context in object recognition has been intensively studied over the years. Along with the advent of deep convolutional neural networks (CNN), using contextual information with such systems starts to receive attention in the literature. Regardless of deep learning advances, aerial image analysis still poses many great challenges. Satellite images are often taken under poor lighting conditions and contain low resolution objects, many times occluded. For this particular task, visual context could be of great help, but there are still very few papers that consider context in aerial image understanding. Our work addresses the task of object segmentation in aerial images with a novel dual-stream deep convolutional neural network that integrates the local object appearance and global contextual information into a unified network. Our model learns to combine local object appearance and global semantic knowledge simultaneously and in a complementary way, so that together they form a powerful classifier. Experiments on the Massachusetts Buildings Dataset demonstrate the superiority of our model over state-of-the-art methods. We also introduce two new challenging datasets for the task of buildings and road segmentation. While our local-global model could also be useful in general recognition tasks, we clearly demonstrate the effectiveness of visual context in conjunction with deep nets in aerial image understanding.},

  url = {https://aaai.org/ocs/index.php/WS/AAAIW17/paper/view/15177}

}