Abstract
Hyperspectral images with hundreds of spectral bands have been proven to yield high performance in material classification. However, despite intensive advancement in hardware, the spatial resolution is still somewhat low, as compared to that of color and multispectral (MS) imagers. In this paper, we aim at presenting some ideas that may further enhance the performance of some remote sensing applications such as border monitoring and Mars exploration using hyperspectral images. One popular approach to enhancing the spatial resolution of hyperspectral images is pansharpening. We present a brief review of recent image resolution enhancement algorithms, including single super-resolution and multi-image fusion algorithms, for hyperspectral images. Advantages and limitations of the enhancement algorithms are highlighted. Some limitations in the pansharpening process include the availability of high resolution (HR) panchromatic (pan) and/or MS images, the registration of images from multiple sources, the availability of point spread function (PSF), and reliable and consistent image quality assessment. We suggest some proactive ideas to alleviate the above issues in practice. In the event where hyperspectral images are not available, we suggest the use of band synthesis techniques to generate HR hyperspectral images from low resolution (LR) MS images. Several recent interesting applications in border monitoring and Mars exploration using hyperspectral images are presented. Finally, some future directions in this research area are highlighted.
Highlights
Remote sensing using multispectral (MS) and hyperspectral (HS) images can help fire detection [1], anomaly detection [2,3,4,5,6], chemical agent detection [7], border monitoring [8], target detection [9,10,11], and change detection [12,13,14]
In the event where hyperspectral images are not available, we suggest the use of band synthesis techniques to generate high resolution (HR) hyperspectral images from low resolution (LR) MS images
Hyperspectral images have been proven to be very useful for target detection and classification
Summary
Remote sensing using multispectral (MS) and hyperspectral (HS) images can help fire detection [1], anomaly detection [2,3,4,5,6], chemical agent detection [7], border monitoring [8], target detection [9,10,11], and change detection [12,13,14]. In many remote sensing applications, it will be ideal for images to have high resolution spatially, spectrally, and temporally. In the spatial resolution enhancement area for hyperspectral images, there are quite a few new developments. Collecting HR pan, color, and MS images requires a pro-active approach, which we would like to advocate in this paper Another issue is related to image registration. There are still some practical issues such as changes due to natural vs man-made factors Another notable one is computational requirements, as hyperspectral images involve a lot of bands. These include algorithms for single image super-resolution and pansharpening.
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