Deep Clustering With Intra-class Distance Constraint for Hyperspectral Images
This work addresses clustering challenges in hyperspectral remote sensing, an incremental improvement by integrating prior knowledge constraints into deep learning for better feature extraction.
The paper tackles the degradation of clustering performance in high-dimensional hyperspectral images by proposing a deep clustering algorithm that incorporates an intra-class distance constraint into an auto-encoder network, transforming data into a low-dimensional space more suitable for clustering. Experimental results show the algorithm is intensely competitive with state-of-the-art methods.
The high dimensionality of hyperspectral images often results in the degradation of clustering performance. Due to the powerful ability of deep feature extraction and non-linear feature representation, the clustering algorithm based on deep learning has become a hot research topic in the field of hyperspectral remote sensing. However, most deep clustering algorithms for hyperspectral images utilize deep neural networks as feature extractor without considering prior knowledge constraints that are suitable for clustering. To solve this problem, we propose an intra-class distance constrained deep clustering algorithm for high-dimensional hyperspectral images. The proposed algorithm constrains the feature mapping procedure of the auto-encoder network by intra-class distance so that raw images are transformed from the original high-dimensional space to the low-dimensional feature space that is more conducive to clustering. Furthermore, the related learning process is treated as a joint optimization problem of deep feature extraction and clustering. Experimental results demonstrate the intense competitiveness of the proposed algorithm in comparison with state-of-the-art clustering methods of hyperspectral images.