Abstract
Abstract. Current popular deep neural networks for semantic segmentation are almost supervised and highly rely on a large amount of labeled data. However, obtaining a large amount of pixel-level labeled data is time-consuming and laborious. In remote sensing area, this problem is more urgent. To alleviate this problem, we propose a novel semantic segmentation neural network (S4Net) based on semi-supervised learning by using unlabeled data. Our model can learn from unlabeled data by consistency regularization, which enforces the consistency of output under different random transforms and perturbations, such as random affine transform. Thus, the network is trained by the weighted sum of a supervised loss from labeled data and a consistency regularization loss from unlabeled data. The experiments we conducted on DeepGlobe land cover classification challenge dataset verified that our network can make use of unlabeled data to obtain precise results of semantic segmentation and achieve competitive performance when compared to other methods.
Highlights
In remote sensing science and technology, the classification of remote sensing images is one of the most basic research issues, and it is the basis of other remote sensing research and application
Deep learning has become mainstream in image processing and convolutional neural networks (CNN) have achieved great success (LeCun et al, 2015)
To overcome the problem of a large amount of data required for supervised learning, we proposed a semantic segmentation network based on semi-supervised learning, named S4Net in this paper
Summary
In remote sensing science and technology, the classification of remote sensing images is one of the most basic research issues, and it is the basis of other remote sensing research and application. The currently popular methods can obtain better results, most of the current models are trained by supervised fashion, which needs a large number of labeled data to cooperate with deep networks for learning parameters (Zhang et al, 2016, Zhu et al, 2017, Ball et al, 2017). Of machine learning technology that lies between supervised learning and unsupervised learning It usually uses a small number of labeled data and a large number of unlabeled data to train a neural network (Chapelle et al, 2009). We performed experiments on a public DeepGlobe land cover classification challenge dataset and verified this method can take advantage of unlabeled data and achieve improvements in the context of a small amount of data
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