Hyperspectral Image Classification Using Deep Matrix Capsules

Hyperspectral image (HSI) classification is used in multiple domains like precision agriculture, mineral exploration, remote sensing, and others. Conventionally, couvolutional neural networks (CNNs) were used in HSI classification, however they have limitations in exploiting spectral-spatial relationships, which is a key factor in understanding HSI. Even though deeper CNN architectures and use of 3-D-CNNs mitigate the above problem to a certain extent, they have increased computational complexity, which inhibits their use in resource-limited devices like IoT and edge computing devices. In this paper, we propose a novel method based on the concept of matrix capsules with Expectation-Maximization (EM) routing algorithm which is specifically designed to accommodate the nuances in the HSI data to efficiently tackle the aforementioned problems. The capsule units enable effective identification of spectral siguatures and part-whole relationships in the data while EM routing ensures viewpoint-invariance. Three representative HSI data sets are used to verify the effectiveness of the proposed method. The empirical results demonstrate that the proposed method is better than the current state-of-the-art methods in terms of accuracy while having 25 times fewer model parameters and requiring over 65 times less storage space. The source code can be found at https://github.com/DeepMatrixCapsules/DeepMatrixCapsules.