Error-Tolerant Deep Learning for Remote Sensing Image Scene Classification.

Abstract

Due to its various application potentials, the remote sensing image scene classification (RSSC) has attracted a broad range of interests. While the deep convolutional neural network (CNN) has recently achieved tremendous success in RSSC, its superior performances highly depend on a large number of accurately labeled samples which require lots of time and manpower to generate for a large-scale remote sensing image scene dataset. In contrast, it is not only relatively easy to collect coarse and noisy labels but also inevitable to introduce label noise when collecting large-scale annotated data in the remote sensing scenario. Therefore, it is of great practical importance to robustly learn a superior CNN-based classification model from the remote sensing image scene dataset containing non-negligible or even significant error labels. To this end, this article proposes a new RSSC-oriented error-tolerant deep learning (RSSC-ETDL) approach to mitigate the adverse effect of incorrect labels of the remote sensing image scene dataset. In our proposed RSSC-ETDL method, learning multiview CNNs and correcting error labels are alternatively conducted in an iterative manner. It is noted that to make the alternative scheme work effectively, we propose a novel adaptive multifeature collaborative representation classifier (AMF-CRC) that benefits from adaptively combining multiple features of CNNs to correct the labels of uncertain samples. To quantitatively evaluate the performance of error-tolerant methods in the remote sensing domain, we construct remote sensing image scene datasets with: 1) simulated noisy labels by corrupting the open datasets with varying error rates and 2) real noisy labels by deploying the greedy annotation strategies that are practically used to accelerate the process of annotating remote sensing image scene datasets. Extensive experiments on these datasets demonstrate that our proposed RSSC-ETDL approach outperforms the state-of-the-art approaches.