Mastcam image enhancement using estimated point spread functions

Kwan, Chiman; Dao, Minh N.; Chou, Bryan; Kwan, Li-Yun Martin; Ayhan, Bulent

doi:10.1109/uemcon.2017.8249023

Cited by 22 publications

(18 citation statements)

References 19 publications

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“…Validation of the generated image fusion products is performed either visually, i.e., using a qualitative assessment [106] or by employing a quantitative metric [1,107]. Among these metrics we can refer to spectral angle mapper [108], peak signal-to-noise ratio [109], structural similarity index used to assess the spatial distortion of the fused image [12], image quality index proposed by [110] and its vector extension [111], absolute difference [60,112], root mean squared error (RMSE) [112], cross-correlation [112] and Erreur Relative Globale Adimensionnelle de Synthese [113]. The performance of the developed data fusion methods is evaluated by either using real data or synthetic data [32,51,60].…”

Section: Quality Assessment Of Spatiotemporal Blended Imagesmentioning

confidence: 99%

Spatiotemporal Image Fusion in Remote Sensing

2019

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In this paper, we discuss spatiotemporal data fusion methods in remote sensing. These methods fuse temporally sparse fine-resolution images with temporally dense coarse-resolution images. This review reveals that existing spatiotemporal data fusion methods are mainly dedicated to blending optical images. There is a limited number of studies focusing on fusing microwave data, or on fusing microwave and optical images in order to address the problem of gaps in the optical data caused by the presence of clouds. Therefore, future efforts are required to develop spatiotemporal data fusion methods flexible enough to accomplish different data fusion tasks under different environmental conditions and using different sensors data as input. The review shows that additional investigations are required to account for temporal changes occurring during the observation period when predicting spectral reflectance values at a fine scale in space and time. More sophisticated machine learning methods such as convolutional neural network (CNN) represent a promising solution for spatiotemporal fusion, especially due to their capability to fuse images with different spectral values.

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Section: Quality Assessment Of Spatiotemporal Blended Imagesmentioning

confidence: 99%

Spatiotemporal Image Fusion in Remote Sensing

2019

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“…Here, the spatial resolution of each band in the HS image is improved without using additional images such as the pan band or some other color, or MS images from other imagers. Bicubic interpolation is the simplest and most widely used method which does not utilize the point spread function (PSF) [123]. One recent algorithm [124] utilizes PSF, if available, to improve the resolution of a single image by deblurring.…”

Section: Single Image Super-resolution Methodsmentioning

confidence: 99%

Methods and Challenges Using Multispectral and Hyperspectral Images for Practical Change Detection Applications

Kwan

2019

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Multispectral (MS) and hyperspectral (HS) images have been successfully and widely used in remote sensing applications such as target detection, change detection, and anomaly detection. In this paper, we aim at reviewing recent change detection papers and raising some challenges and opportunities in the field from a practitioner’s viewpoint using MS and HS images. For example, can we perform change detection using synthetic hyperspectral images? Can we use temporally-fused images to perform change detection? Some of these areas are ongoing and will require more research attention in the coming years. Moreover, in order to understand the context of our paper, some recent and representative algorithms in change detection using MS and HS images are included, and their advantages and disadvantages will be highlighted.

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“…Many methods have been developed. See references [5][6][7][8][9] and references therein. Another group focuses on integrating low spatial resolution high temporal resolution images with high spatial resolution low temporal resolution images.…”

Section: Introductionmentioning

confidence: 99%

Fusion of landsat and worldview images

Kwan¹,

Chou

Yang

et al. 2019

Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII

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Pansharpened Landsat images have 15 m spatial resolution with 16-day revisit periods. On the other hand, Worldview images have 0.5 m resolution after pansharpening but the revisit times are uncertain. We present some preliminary results for a challenging image fusion problem that fuses Landsat and Worldview (WV) images to yield a high temporal resolution image sequence at the same spatial resolution of WV images. Since the spatial resolution between Landsat and Worldview is 30 to 1, our preliminary results are mixed in that the objective performance metrics such as peak signal-to-noise ratio (PSNR), correlation coefficient (CC), etc. sometimes showed good fusion performance, but at other times showed poor results. This indicates that more fusion research is still needed in this niche application.

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Mastcam image enhancement using estimated point spread functions

Cited by 22 publications

References 19 publications

Spatiotemporal Image Fusion in Remote Sensing

Spatiotemporal Image Fusion in Remote Sensing

Methods and Challenges Using Multispectral and Hyperspectral Images for Practical Change Detection Applications

Fusion of landsat and worldview images

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