Hyperspectral Image Spatial Super-Resolution via 3D Full Convolutional Neural Network

By Shaohui Mei, Xin Yuan, Jingyu Ji, Yifan Zhang, Shuai Wan, Qian Du. Remote Sensing, 2017

307 citations recorded by the source corpus.

Hyperspectral images are well-known for their fine spectral resolution to discriminate different materials. However, their spatial resolution is relatively low due to the trade-off in imaging sensor technologies, resulting in limitations in their applications. Inspired by recent achievements in convolutional neural network (CNN) based super-resolution (SR) for natural images, a novel three-dimensional full CNN (3D-FCNN) is constructed for spatial SR of hyperspectral images in this paper. Specifically, 3D convolution is used to exploit both the spatial context of neighboring pixels and spectral correlation of neighboring bands, such that spectral distortion when directly applying traditional CNN based SR algorithms to hyperspectral images in band-wise manners is alleviated. Furthermore, a sensor-specific mode is designed for the proposed 3D-FCNN such that none of the samples from the target scene are required for training. Fine-tuning by a small number of training samples from the target scene can further improve the performance of such a sensor-specific method. Extensive experimental results on four benchmark datasets from two well-known hyperspectral sensors, namely hyperspectral digital imagery collection experiment (HYDICE) and reflective optics system imaging spectrometer (ROSIS) sensors, demonstrate that our proposed 3D-FCNN outperforms several existing SR methods by ensuring higher quality both in reconstruction and spectral fidelity.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/05f6a3c65fc1243fcedf53df44ee535a2fafdb20

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review (2019, methodology): “In [86] an end-to-end approach based on 3D convolutions was suggested instead.”
A New Benchmark Based on Recent Advances in Multispectral Pansharpening: Revisiting Pansharpening With Classical and Emerging Pansharpening Methods (2021, background): “Needless to say, the deep learning (DL) wave has even flooded over into the remote sensing field [19], including pansharpening [89]–[100] and intimately related tasks, such as superresolution [101]–[103] or HS/ MS data fusion [104], [105].”
Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review (2020, methodology): “In addition to the spatial resolution, the authors of [162] apply the same idea using 3D-CNNs on HSI to also provide higher spectral quality.”