Land-cover classification with high-resolution remote sensing images using transferable deep models

计算机科学 土地覆盖 卷积神经网络 遥感 人工智能 可转让性 模式识别(心理学) 上下文图像分类 分割 深度学习 图像分辨率 像素 土地利用 数据挖掘 封面(代数) 图像(数学) 机器学习 地理 罗伊特 土木工程 工程类 机械工程
作者
Xinyi Tong,Gui-Song Xia,Qikai Lu,Huanfeng Shen,Shengyang Li,Shucheng You,Liangpei Zhang
出处
期刊:Remote Sensing of Environment [Elsevier BV]
卷期号:237: 111322-111322 被引量:684
标识
DOI:10.1016/j.rse.2019.111322
摘要

In recent years, large amount of high spatial-resolution remote sensing (HRRS) images are available for land-cover mapping. However, due to the complex information brought by the increased spatial resolution and the data disturbances caused by different conditions of image acquisition, it is often difficult to find an efficient method for achieving accurate land-cover classification with high-resolution and heterogeneous remote sensing images. In this paper, we propose a scheme to apply deep model obtained from labeled land-cover dataset to classify unlabeled HRRS images. The main idea is to rely on deep neural networks for presenting the contextual information contained in different types of land-covers and propose a pseudo-labeling and sample selection scheme for improving the transferability of deep models. More precisely, a deep Convolutional Neural Networks (CNNs) is first pre-trained with a well-annotated land-cover dataset, referred to as the source data. Then, given a target image with no labels, the pre-trained CNN model is utilized to classify the image in a patch-wise manner. The patches with high confidence are assigned with pseudo-labels and employed as the queries to retrieve related samples from the source data. The pseudo-labels confirmed with the retrieved results are regarded as supervised information for fine-tuning the pre-trained deep model. To obtain a pixel-wise land-cover classification with the target image, we rely on the fine-tuned CNN and develop a hybrid classification by combining patch-wise classification and hierarchical segmentation. In addition, we create a large-scale land-cover dataset containing 150 Gaofen-2 satellite images for CNN pre-training. Experiments on multi-source HRRS images, including Gaofen-2, Gaofen-1, Jilin-1, Ziyuan-3, Sentinel-2A, and Google Earth platform data, show encouraging results and demonstrate the applicability of the proposed scheme to land-cover classification with multi-source HRRS images.
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