BS-Nets: An End-to-End Framework for Band Selection of Hyperspectral Image

Visual Saliency-Based Extended Morphological Profiles for Unsupervised Feature Learning of Hyperspectral Images

Sparse Feature Learning of Hyperspectral Imagery via Multiobjective-Based Extreme Learning Machine

Hyperspectral image (HSI) consists of hundreds of narrow spectral band components with rich spectral and spatial information. Extreme Learning Machine (ELM) has been widely used for HSI analysis. However, the classical ELM is difficult to use for sparse feature leaning due to its randomly generated hidden layer. In this paper, we propose a novel unsupervised sparse feature learning approach, called Evolutionary Multiobjective-based ELM (EMO-ELM), and apply it to HSI feature extraction. Specifically, we represent the task of constructing the ELM Autoencoder (ELM-AE) as a multiobjective optimization problem that takes the sparsity of hidden layer outputs and the reconstruction error as two conflicting objectives. Then, we adopt an Evolutionary Multiobjective Optimization (EMO) method to solve the two objectives, simultaneously. To find the best solution from the Pareto solution set and construct the best trade-off feature extractor, a curvature-based method is proposed to focus on the knee area of the Pareto solutions. Benefited from the EMO, the proposed EMO-ELM is less prone to fall into a local minimum and has fewer trainable parameters than gradient-based AEs. Experiments on two real HSIs demonstrate that the features learned by EMO-ELM not only preserve better sparsity but also achieve superior separability than many existing feature learning methods.

Graph Convolutional Subspace Clustering: A Robust Subspace Clustering Framework for Hyperspectral Image

Efficient Graph Convolutional Self-Representation for Band Selection of Hyperspectral Image

Deep Multigrained Cascade Forest for Hyperspectral Image Classification

Spectral-Spatial Clustering of Hyperspectral Image Based on Laplacian Regularized Deep Subspace Clustering

Discriminative Spectral-Spatial Attention-Aware Residual Network For Hyperspectral Image Classification

Band Selection of Hyperspectral Images Using Multiobjective Optimization-Based Sparse Self-Representation

Hierarchical Ensemble of Extreme Learning Machine

A multiobjective optimization-based sparse extreme learning machine algorithm

A Novel Deep Learning Approach: Stacked Evolutionary Auto-encoder

Extreme Learning Machine Based on Evolutionary Multi-objective Optimization