Spatiotemporal Fusion of MODIS and Landsat-7 Reflectance Images via Compressed Sensing

Wei, Jingbo; Wang, Lizhe; Liu, Peng; Chen, Xiaodao; Li, Wei; Zomaya, Albert Y.

doi:10.1109/tgrs.2017.2742529

Cited by 47 publications

(28 citation statements)

References 44 publications

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“…However, satellite data with high temporal and spatial resolutions are lacking due to the cost, long revisit cycles [4], frequent cloud contamination [5], capacity of satellite platforms [6], and technological difficulties in developing satellites. This situation presents significant disadvantages and challenges in observing and monitoring ground status in a timely and effective manner [7]. Moreover, detecting the condition of rice growth in the southern area of China is difficult because of missing data caused by frequent cloudiness and precipitation.…”

Section: Introductionmentioning

confidence: 99%

A Modified Spatiotemporal Fusion Algorithm Using Phenological Information for Predicting Reflectance of Paddy Rice in Southern China

Liu

et al. 2018

Remote Sensing

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Satellite data for studying surface dynamics in heterogeneous landscapes are missing due to frequent cloud contamination, low temporal resolution, and technological difficulties in developing satellites. A modified spatiotemporal fusion algorithm for predicting the reflectance of paddy rice is presented in this paper. The algorithm uses phenological information extracted from a moderate-resolution imaging spectroradiometer enhanced vegetation index time series to improve the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). The algorithm is tested with satellite data on Yueyang City, China. The main contribution of the modified algorithm is the selection of similar neighborhood pixels by using phenological information to improve accuracy. Results show that the modified algorithm performs better than ESTARFM in visual inspection and quantitative metrics, especially for paddy rice. This modified algorithm provides not only new ideas for the improvement of spatiotemporal data fusion method, but also technical support for the generation of remote sensing data with high spatial and temporal resolution.

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Section: Introductionmentioning

confidence: 99%

A Modified Spatiotemporal Fusion Algorithm Using Phenological Information for Predicting Reflectance of Paddy Rice in Southern China

Liu

et al. 2018

Remote Sensing

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“…This can be explained by the level of details in Table 9. We employ " StandardDeviation Mean " to represent the level of details of a target image as in [19]. It is clear that image 1 has the highest level of details in NIR band and the most similar levels of details in the three bands.…”

Section: Discussionmentioning

confidence: 99%

“…The weights W 21 and W 32 we used are same as those in [19], which take the average of the two predicted difference images. Input images and the target image for spatiotemporal fusion (t 1 < t 2 < t 3 ).…”

Section: Problem Definitionmentioning

confidence: 99%

“…Attempts have been made to improve the performance of the SCDL based models. For examples, Block Sparse Bayesian Learning for Semi-Coupled Dictionary Learning (BSBL-SCDL) [18] employs the structural sparsity of the sparse coefficients as a priori knowledge and Compressed Sensing for Spatiotemporal Fusion (CSSF) [19] considers explicitly the down-sampling process within the framework of compressed sensing for reconstruction. In comparison with the weighting methods, the advantage of the dictionary-learning-based methods is that they retrieve the hidden relationship between image pairs from the sparse coding space to better capture structure changes.Besides the aforementioned methods, some researchers employed other approaches to fuse multi-source data.…”

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confidence: 99%

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Sparsity-Based Spatiotemporal Fusion via Adaptive Multi-Band Constraints

et al. 2018

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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 ℓ 1 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.

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“…New models and frameworks are required for integrating data to study air pollution, vector-borne diseases, ecological and climate relationships, and hydrological systems. For example, weighted-and unmixing-based as well as sparse representation can be used to fuse data with different spatiotemporal resolutions from the Landsat-7 and MODIS satellite observations (Wei et al 2018) for ecological and land use studies.…”

Section: Future Directionsmentioning

confidence: 99%