Abstract
ABSTRACTDuring the last few years, artificial intelligence based on deep learning, and particularly based on convolutional neural networks, has acted as a game changer in just about all tasks related to photogrammetry and remote sensing. Results have shown partly significant improvements in many projects all across the photogrammetric processing chain from image orientation to surface reconstruction, scene classification as well as change detection, object extraction and object tracking and recognition in image sequences. This paper summarizes the foundations of deep learning for photogrammetry and remote sensing before illustrating, by way of example, different projects being carried out at the Institute of Photogrammetry and GeoInformation, Leibniz University Hannover, in this exciting and fast moving field of research and development.
Highlights
The use of neurons and neural networks for artificial intelligence in general, and for tasks related to image understanding in particular, is not new
After Minsky and Papert (1969) proved mathematically that the original concept could not model the important XOR statement, which dealt the research on neural networks a significant blow, the field was revived about two decades later with the introduction of backpropagation (Rummelhart, Hinton, and Williams 1986; LeCun 1987), which allowed the efficient training of multi-layer artificial neural networks, to which the theoretical restrictions noted by Minsky and Papert (1969) do not apply
Other important steps were the introduction of Convolutional Neural Networks (CNN, LeCun et al 1989; LeCun and Bengio 1998) and deep belief networks (Hinton, Osindero, and Teh 2006)
Summary
The use of neurons and neural networks for artificial intelligence in general, and for tasks related to image understanding in particular, is not new. The breakthrough of deep learning came, when Krizhevsky, Sutskever, and Hinton (2012) won the ImageNet Large-Scale Recognition Challenge, a classification task involving 1000 different classes (Russakovsky et al 2015) using a CNN-based approach. Their network, called AlexNet, lowered the remaining error by nearly 50% compared to the previous best result. The main reasons are twofold: (a) since a few years, computers are powerful enough to process and store data using large networks with many layers (called “deep” networks), in particular when using GPUs (graphical processing units) during training, and (b) more and more training data became available for the different tasks (it should be noted that AlexNet used some 1,2 million labeled training images to learn a total of some 60 million parameters).
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