Next Article in Journal
Seabed Mapping in Coastal Shallow Waters Using High Resolution Multispectral and Hyperspectral Imagery
Previous Article in Journal
Three-Dimensional Reconstruction of Soybean Canopies Using Multisource Imaging for Phenotyping Analysis
Open AccessArticle

An Enhanced Single-Pair Learning-Based Reflectance Fusion Algorithm with Spatiotemporally Extended Training Samples

1
College of Ming Engineering, Taiyuan University of Technology, Taiyuan 030024, China
2
School of Land Science and Technology, China University of Geosciences, Beijing 100083, China
3
Shanxi Coal Geology Geophysical Surveying Exploration Institute, Jinzhong 030600, China
4
China Centre for Resources Satellite Data and Application, Beijing 100094, China
5
Academy of Opto-Electronics, Chinese Academy of Science, Beijing 100094, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(8), 1207; https://doi.org/10.3390/rs10081207
Received: 28 May 2018 / Revised: 11 July 2018 / Accepted: 19 July 2018 / Published: 1 August 2018
(This article belongs to the Section Remote Sensing Image Processing)
Spatiotemporal fusion methods are considered a useful tool for generating multi-temporal reflectance data with limited high-resolution images and necessary low-resolution images. In particular, the superiority of sparse representation-based spatiotemporal reflectance fusion model (SPSTFM) in capturing phenology and type changes of land covers has been preliminarily demonstrated. Meanwhile, the dictionary training process, which is a key step in the sparse learning-based fusion algorithm, and its effect on fusion quality are still unclear. In this paper, an enhanced spatiotemporal fusion scheme based on the single-pair SPSTFM algorithm has been proposed through improving the process of dictionary learning, and then evaluated using two actual datasets, with one representing a rural area with phenology changes and the other representing an urban area with land cover type changes. The validated strategy for enhancing the dictionary learning process is divided into two modes to enlarge the training datasets with spatially and temporally extended samples. Compared to the original learning-based algorithm and other employed typical single-pair-based fusion models, experimental results from the proposed fusion method with two extension modes show improved performance in modeling reflectance using the two preceding datasets. Furthermore, the strategy with temporally extended training samples is more effective than the strategy with spatially extended training samples for the land cover area with phenology changes, whereas it is opposite for the land cover area with type changes. View Full-Text
Keywords: sparse learning; single image-pair; reflectance fusion; dictionary training; spatiotemporal extension sparse learning; single image-pair; reflectance fusion; dictionary training; spatiotemporal extension
Show Figures

Figure 1

MDPI and ACS Style

Li, D.; Li, Y.; Yang, W.; Ge, Y.; Han, Q.; Ma, L.; Chen, Y.; Li, X. An Enhanced Single-Pair Learning-Based Reflectance Fusion Algorithm with Spatiotemporally Extended Training Samples. Remote Sens. 2018, 10, 1207.

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
Search more from Scilit
 
Search
Back to TopTop