Fuzzy rule-based ELM and autoencoder for hyperspectral remote sensing image classification

Hyperspectral remote sensing (HRS) image classification is time-consuming and complex due to its wide spectral range and large number of samples. Factors such as heterogeneous behaviour, unbalanced class distributions, complex boundaries, multiple spectral bands, inconsistent samples, significant spatial variability, and interclass similarities further challenge image classification. The design of a robust classification model that addresses these challenges demands efficient pre-processing of input information, e.g., dimensionality reduction and representative feature extraction, with the ability to handle information uncertainty and to reduce computational complexity. In line with these objectives, we have proposed a classification model called FRELM-SAE, which utilises fuzzy granulation of input features, a rule-based extreme learning machine (ELM), and a stacked autoencoder (SAE). Fuzzy granulation and rule-based ELM address the generalisation aspects and the complexity of the decision-making process. The SAE reduces the noise from the input feature space and performs representative feature extraction. Various experiments demonstrate the proposed model’s performance in classifying two HRS images. The experimental results show its supremacy over related work across performance metrics, including overall accuracy, precision, recall, etc.