Panchromatic and Hyperspectral Image Fusion: Outcome of the 2022 WHISPERS Hyperspectral Pansharpening Challenge

Abstract

This paper presents the scientific outcomes of the 2022 Hyperspectral Pansharpening Challenge organized by the 12th IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (IEEE WHISPERS 2022). The 2022 Hyperspectral Pansharpening Challenge aims at fusing a panchromatic image with hyperspectral data to get a high spatial resolution hyperspectral cube with the same spatial resolution of the panchromatic image while preserving the spectral information of hyperspectral data. Four datasets acquired by the PRISMA mission owned and managed by the Italian Space Agency have been prepared for participants. They will be made available for the benefit of the scientific community. Each dataset contains a panchromatic image and a hyperspectral cube with different spatial resolutions. More than 100 registrations have been received for the event. Four teams submitted their outcomes. Since no team actually outperformed the baseline provided by the organizers, the challenge was declared inconclusive and no winner was recognized.

Qualitative and quantitative analysis of artificial neural network-based post-classification comparison to detect the earth surface variations using hyperspectral and multispectral datasets

“…However, some of the attempts have also been made in the literature, such as the fusion of hyperspectral with high-resolution multispectral or panchromatic bands 71 , 72 . But this does not necessarily guarantee an enhancement in the spatial resolution and there are a lot of challenges are associate with the integration of the spatial and spectral information 73 , 74 . Therefore, the current framework may be challenging for the detection of class categories with fewer land holdings as per the current spatial resolution (30 m) of Hyperion EO-1 and Landsat-8.…”

“…With this study, it has also been observed that neural network has more potential in handling high-dimensional datasets and solving real-time complex problems. The deep neural networks or incorporation of subpixel-based classifiers may solve the problem of mixed pixel interference and handle large volumes of data in real time 74 – 76 The subpixel-, pixel-, and superpixel-based image fusion methods may also be followed to improve the detection of land surface changes via the hyperspectral dataset 77 .…”

“…71,72 But this does not necessarily guarantee an enhancement in the spatial resolution and there are a lot of challenges are associate with the integration of the spatial and spectral information. 73,74 Therefore, the current framework may be challenging for the detection of class categories with…”

“…The deep neural networks or incorporation of subpixel-based classifiers may solve the problem of mixed pixel interference and handle large volumes of data in real time. [74][75][76] The subpixel-, pixel-, and superpixel-based image fusion methods may also be followed to improve the detection of land surface changes via the hyperspectral dataset. 77 Moreover, the proposed technique can also be explored for various other areas, such as soil type and erosion, disease detection, and crop identification.…”

Hyperspectral Anomaly Detection via Sparse Representation and Collaborative Representation

“…ECENTLY, the remote sensing image interpretation methods covering panchromatic image [1], [2], multispectral images [3], [4] and hyperspectral images (HSIs) [5] have achieved remarkable achievements. Among them, HSIs can provide rich spectral information (i.e., about 10-nm spectral resolution), which makes it possible to identify the ground objects with different characteristics by means of the spectral information [6].…”