# Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images

arXiv: https://arxiv.org/abs/1904.03982 · HTML: https://mlsanity.com/paper/1904.03982/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2019.

## Built on by (most cited first, 8 of 8)

- [A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification](https://mlsanity.com/paper/2105.14327/) (2021)
- [Spectral-Spatial Global Graph Reasoning for Hyperspectral Image Classification](https://mlsanity.com/paper/2106.13952/) (2021)
- [Deep Diversity-Enhanced Feature Representation of Hyperspectral Images](https://mlsanity.com/paper/2301.06132/) (2023)
- [Bio-Inspired Representation Learning for Visual Attention Prediction](https://mlsanity.com/paper/2103.05310/) (2021)
- [Ensemble and Random Collaborative Representation-Based Anomaly Detector for Hyperspectral Imagery](https://mlsanity.com/paper/2101.01976/) (2021)
- [Spectral Graph Reasoning Network for Hyperspectral Image Classification](https://mlsanity.com/paper/2407.02647/) (2024)
- [Enhancing Knowledge Transfer in Hyperspectral Image Classification via Cross-scene Knowledge Integration](https://mlsanity.com/paper/2512.08989/) (2025)
- [DMS2F-HAD: A Dual-branch Mamba-based Spatial-Spectral Fusion Network for Hyperspectral Anomaly Detection](https://mlsanity.com/paper/2602.04102/) (2026)

## Abstract (arXiv)

In hyperspectral remote sensing data mining, it is important to take into account of both spectral and spatial information, such as the spectral signature, texture feature and morphological property, to improve the performances, e.g., the image classification accuracy. In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier. However, multiple features from various domains definitely have different physical meanings and statistical properties, and thus such concatenation hasn't efficiently explore the complementary properties among different features, which should benefit for boost the feature discriminability. Furthermore, it is also difficult to interpret the transformed results of the concatenated vector. Consequently, finding a physically meaningful consensus low dimensional feature representation of original multiple features is still a challenging task. In order to address the these issues, we propose a novel feature learning framework, i.e., the simultaneous spectral-spatial feature selection and extraction algorithm, for hyperspectral images spectral-spatial feature representation and classification. Specifically, the proposed method learns a latent low dimensional subspace by projecting the spectral-spatial feature into a common feature space, where the complementary information has been effectively exploited, and simultaneously, only the most significant original features have been transformed. Encouraging experimental results on three public available hyperspectral remote sensing datasets confirm that our proposed method is effective and efficient.