Land-cover classification with high-resolution remote sensing images using transferable deep models
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 d...
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| Vydané v: | Remote sensing of environment Ročník 237; s. 111322 |
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| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
New York
Elsevier Inc
01.02.2020
Elsevier BV |
| Predmet: | |
| ISSN: | 0034-4257, 1879-0704 |
| On-line prístup: | Získať plný text |
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| Abstract | 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.
•A method to learn transferable deep model for 5-class land-cover (LC) classification.•A labeled dataset consisting of 150 Gaofen-2 images for LC classification.•It improves LC classification performance about 20% using multi-source RS images.•The method shows good transferability on different sensors and geolocations. |
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| AbstractList | 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. 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. •A method to learn transferable deep model for 5-class land-cover (LC) classification.•A labeled dataset consisting of 150 Gaofen-2 images for LC classification.•It improves LC classification performance about 20% using multi-source RS images.•The method shows good transferability on different sensors and geolocations. |
| ArticleNumber | 111322 |
| Author | Lu, Qikai Li, Shengyang Tong, Xin-Yi Zhang, Liangpei You, Shucheng Shen, Huanfeng Xia, Gui-Song |
| Author_xml | – sequence: 1 givenname: Xin-Yi surname: Tong fullname: Tong, Xin-Yi organization: State Key Laboratory LIESMARS, Wuhan University, China – sequence: 2 givenname: Gui-Song surname: Xia fullname: Xia, Gui-Song email: guisong.xia@whu.edu.cn organization: State Key Laboratory LIESMARS, Wuhan University, China – sequence: 3 givenname: Qikai surname: Lu fullname: Lu, Qikai organization: Electronic Information School, Wuhan University, China – sequence: 4 givenname: Huanfeng surname: Shen fullname: Shen, Huanfeng organization: School of Resource and Environmental Sciences, Wuhan University, China – sequence: 5 givenname: Shengyang surname: Li fullname: Li, Shengyang organization: Key Laboratory of Space Utilization, Tech. & Eng. Center for Space Utilization, Chinese Academy of Sciences, China – sequence: 6 givenname: Shucheng surname: You fullname: You, Shucheng organization: Remote Sensing Department, China Land Survey and Planning Institute, China – sequence: 7 givenname: Liangpei surname: Zhang fullname: Zhang, Liangpei organization: State Key Laboratory LIESMARS, Wuhan University, China |
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| ContentType | Journal Article |
| Copyright | 2019 Elsevier Inc. Copyright Elsevier BV Feb 2020 |
| Copyright_xml | – notice: 2019 Elsevier Inc. – notice: Copyright Elsevier BV Feb 2020 |
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| DOI | 10.1016/j.rse.2019.111322 |
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| Keywords | Deep learning High-resolution remote sensing land-cover classification Gaofen-2 satellite images |
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| SubjectTerms | Artificial neural networks Classification Datasets Deep learning Detection Gaofen-2 satellite images High resolution High-resolution remote sensing Image acquisition Image classification Image resolution Image segmentation Internet Labels Land cover land-cover classification Mapping Neural networks Remote sensing Satellite imagery Spatial data Spatial resolution Target recognition |
| Title | Land-cover classification with high-resolution remote sensing images using transferable deep models |
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