Remote sensing hyperspectral cameras acquire high spectral-resolution data that reveal valuable composition information on the targets (e.g., for Earth observation and environmental applications). The intrinsic high dimensionality and the lack of sufficient numbers of labeled/training samples prevent efficient processing of hyperspectral images (HSIs). HSI clustering can alleviate these limitations. In this study, we propose a multi-scale spectral-spatial association network (MS$^{2}$A-Net) to cluster HSIs. The backbone of MS$^{2}$A-Net is an autoencoder architecture that allows the network to capture the non-linear relation between data points in an unsupervised manner. The network applies a multi-stream approach. One stream extracts spectral information by deploying a spectral association unit. The other stream derives multi-scale contextual and spatial information by employing dilated (atrous) convolutional kernels. The obtained feature representation generated by MS$^{2}$A-Net is fed into a standard k-means clustering algorithm to produce the final clustering result. Extensive experiments on four HSIs for different types of applications (i.e., geological-, rural-, and urban-mapping) demonstrate the superior performance of MS$^{2}$A-Net over the state-of-the-art shallow/deep learning-based clustering approaches in terms of clustering accuracy.
If you use this code please cite the following paper, K. Rafiezadehshahi, P. Ghamisi, B. Rasti, R. Gloaguen and P. Scheunders, "MS 2 A-Net: Multi-scale spectral-spatial association network for hyperspectral image clustering," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, doi: 10.1109/JSTARS.2022.3198137.