Evaluation of the Spatial Pattern of the Resolution-Enhanced Thermal Data for Urban Area

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Abstract

With the development of urbanization, land surface temperature (LST), as a vital variable for the urban environment, is highly demanded by urban-related studies, especially the LST with both fine temporal and spatial resolutions. Thermal sharpening methods have been developed just under this demand. Until now, there are some thermal sharpening methods proposed especially for urban surface. However, the evaluation of their accuracy still stopped at the level that only considers the statistical aspect, but no spatial information has been included. It is widely acknowledged that the spatial pattern of the thermal environment in an urban area is relatively critical for urban-related studies (e.g., urban heat island studies). Thus, this paper chose three typical methods from the limited number of thermal sharpening methods designed for the urban area and made a comparison between them, together with a newly proposed thermal sharpening method, superresolution-based thermal sharpener (SRTS). These four methods are analyzed by data from different seasons to explore the seasoning impact. Also, the accuracy for different land covers is explored as well. Furthermore, accuracy evaluation was not only taken by statistical variables which are commonly used in other studies; evaluation of the spatial pattern, which is equally important for urban-related studies, was also carried out. This time, the spatial pattern not only was analyzed qualitatively but also has been quantified by some variables for the comparison of accuracy. It is found that all methods obtained lower accuracies for data in winter than for data in other seasons. Linear water features and areas along it are difficult to be detected correctly for most methods.

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Image super-resolution via sparse representation.

Jianchao Yang, John Wright, Thomas Huang … (2010)

This paper presents a new approach to single-image super-resolution, based on sparse signal representation. Research on image statistics suggests that image patches can be well-represented as a sparse linear combination of elements from an appropriately chosen over-complete dictionary. Inspired by this observation, we seek a sparse representation for each patch of the low-resolution input, and then use the coefficients of this representation to generate the high-resolution output. Theoretical results from compressed sensing suggest that under mild conditions, the sparse representation can be correctly recovered from the downsampled signals. By jointly training two dictionaries for the low- and high-resolution image patches, we can enforce the similarity of sparse representations between the low resolution and high resolution image patch pair with respect to their own dictionaries. Therefore, the sparse representation of a low resolution image patch can be applied with the high resolution image patch dictionary to generate a high resolution image patch. The learned dictionary pair is a more compact representation of the patch pairs, compared to previous approaches, which simply sample a large amount of image patch pairs, reducing the computational cost substantially. The effectiveness of such a sparsity prior is demonstrated for both general image super-resolution and the special case of face hallucination. In both cases, our algorithm generates high-resolution images that are competitive or even superior in quality to images produced by other similar SR methods. In addition, the local sparse modeling of our approach is naturally robust to noise, and therefore the proposed algorithm can handle super-resolution with noisy inputs in a more unified framework.