Next Article in Journal
Correction: Al-Kharusi, E.S., et al. Large-Scale Retrieval of Coloured Dissolved Organic Matter in Northern Lakes Using Sentinel-2 Data. Remote Sensing 2020, 12(1), p.157
Next Article in Special Issue
SPMF-Net: Weakly Supervised Building Segmentation by Combining Superpixel Pooling and Multi-Scale Feature Fusion
Previous Article in Journal
Associations between Benthic Cover and Habitat Complexity Metrics Obtained from 3D Reconstruction of Coral Reefs at Different Resolutions
Previous Article in Special Issue
Multiscale Deep Spatial Feature Extraction Using Virtual RGB Image for Hyperspectral Imagery Classification
Open AccessArticle

Hierarchical Multi-View Semi-Supervised Learning for Very High-Resolution Remote Sensing Image Classification

1
School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
2
School of Information Sciences and Technology, Northwest University, Xi’an 710127, China
3
Punjab University College of Information Technology, University of the Punjab, Lahore 54000, Pakistan
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(6), 1012; https://doi.org/10.3390/rs12061012 (registering DOI)
Received: 24 February 2020 / Revised: 18 March 2020 / Accepted: 19 March 2020 / Published: 21 March 2020
Traditional classification methods used for very high-resolution (VHR) remote sensing images require a large number of labeled samples to obtain higher classification accuracy. Labeled samples are difficult to obtain and costly. Therefore, semi-supervised learning becomes an effective paradigm that combines the labeled and unlabeled samples for classification. In semi-supervised learning, the key issue is to enlarge the training set by selecting highly-reliable unlabeled samples. Observing the samples from multiple views is helpful to improving the accuracy of label prediction for unlabeled samples. Hence, the reasonable view partition is very important for improving the classification performance. In this paper, a hierarchical multi-view semi-supervised learning framework with CNNs (HMVSSL) is proposed for VHR remote sensing image classification. Firstly, a superpixel-based sample enlargement method is proposed to increase the number of training samples in each view. Secondly, a view partition method is designed to partition the training set into two independent views, and the partitioned subsets are characterized by being inter-distinctive and intra-compact. Finally, a collaborative classification strategy is proposed for the final classification. Experiments are conducted on three VHR remote sensing images, and the results show that the proposed method performs better than several state-of-the-art methods. View Full-Text
Keywords: semi-supervised learning; VHR remote sensing image classification; multi-view partition; collaborative classification semi-supervised learning; VHR remote sensing image classification; multi-view partition; collaborative classification
Show Figures

Graphical abstract

MDPI and ACS Style

Shi, C.; Lv, Z.; Yang, X.; Xu, P.; Bibi, I. Hierarchical Multi-View Semi-Supervised Learning for Very High-Resolution Remote Sensing Image Classification. Remote Sens. 2020, 12, 1012.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop