Kasra Rafiezadeh Shahi, Pedram Ghamisi, Behnood Rasti, Paul Scheunders, and Richard Gloaguen

Hyperspectral imaging is an important technology in the field of geosciences and remote sensing. However, the high-dimensional nature of hyperspectral images (HSIs) together with the limited availability of training/labeled samples challenge an efficient processing of HSIs. To alleviate these challenges, we propose a deep multi-resolution clustering network (DMC-Net) to analyze HSIs. DMC-Net, without requiring training/labeled samples for the training process, captures the non-linear intrinsic relation within data points in an HSI and analyzes the image at various resolutions by applying atrous convolutions. Furthermore, DMC-Net preserves the spectral information by directly incorporating extracted features from the original HSI into the reconstruction phase. In terms of clustering accuracy, experimental results on two real HSIs demonstrate the superior performance of DMC-Net compared to the state-of-the-art deep learning-based clustering approaches.

IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 199-202, 2022-07-17.

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IARAI Authors
Dr. Pedram Ghamisi
Research
Algorithms, Remote Sensing
Keywords
Convolutional Autoencoder, Deep Learning, Hyperspectral Image, Image Clustering, Remote Sensing

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