Sparsity-Based Spatiotemporal Fusion via Adaptive Multi-Band Constraints
Publication in refereed journal

替代計量分析
.

其它資訊
摘要Remote sensing is an important means to monitor the dynamics of the earth surface. It is still challenging for single-sensor systems to provide spatially high resolution images with high revisit frequency because of the technological limitations. Spatiotemporal fusion is an effective approach to obtain remote sensing images high in both spatial and temporal resolutions. Though dictionary learning fusion methods appear to be promising for spatiotemporal fusion, they do not consider the structure similarity between spectral bands in the fusion task. To capitalize on the significance of this feature, a novel fusion model, named the adaptive multi-band constraints fusion model (AMCFM), is formulated to produce better fusion images in this paper. This model considers structure similarity between spectral bands and uses the edge information to improve the fusion results by adopting adaptive multi-band constraints. Moreover, to address the shortcomings of the ℓ1 norm which only considers the sparsity structure of dictionaries, our model uses the nuclear norm which balances sparsity and correlation by producing an appropriate coefficient in the reconstruction step. We perform experiments on real-life images to substantiate our conceptual augments. In the empirical study, the near-infrared (NIR), red and green bands of Landsat Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS) are fused and the prediction accuracy is assessed by both metrics and visual effects. The experiments show that our proposed method performs better than state-of-the-art methods. It also sheds light on future research.
出版社接受日期06.10.2018
著者Hanchi Ying, Yee Leung, Feilong Cao, Tung Fung and Jie Xue
期刊名稱Remote Sensing
出版年份2018
月份10
卷號10
期次10
出版社MDPI
出版地Basel, Switzerland
文章號碼1646
國際標準期刊號2072-4292
語言美式英語
關鍵詞adaptive multi-band constraints, dictionary learning, sparse representation, spatiotemporal fusion

上次更新時間 2020-25-10 於 03:16