# EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral Unmixing

arXiv: https://arxiv.org/abs/1708.01894 · HTML: https://mlsanity.com/paper/1708.01894/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2017.

## Built on by (most cited first, 29 of 29)

- [Self-supervised Learning in Remote Sensing: A Review](https://mlsanity.com/paper/2206.13188/) (2022)
- [Self-supervised remote sensing feature learning: Learning Paradigms, Challenges, and Future Works](https://mlsanity.com/paper/2211.08129/) (2022)
- [Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package](https://mlsanity.com/paper/2308.09375/) (2023)
- [Entropic Descent Archetypal Analysis for Blind Hyperspectral Unmixing](https://mlsanity.com/paper/2209.11002/) (2022)
- [Aerial Scene Parsing: From Tile-level Scene Classification to Pixel-wise Semantic Labeling](https://mlsanity.com/paper/2201.01953/) (2022)
- [Model-Based Deep Autoencoder Networks for Nonlinear Hyperspectral Unmixing](https://mlsanity.com/paper/2104.08409/) (2021)
- [Unsupervised Diffusion and Volume Maximization-Based Clustering of Hyperspectral Images](https://mlsanity.com/paper/2203.09992/) (2022)
- [DeepInspect: An AI-Powered Defect Detection for Manufacturing Industries](https://mlsanity.com/paper/2311.03725/) (2023)
- [AE-RED: A Hyperspectral Unmixing Framework Powered by Deep Autoencoder and Regularization by Denoising](https://mlsanity.com/paper/2307.00269/) (2023)
- [Pixel-to-Abundance Translation: Conditional Generative Adversarial Networks Based on Patch Transformer for Hyperspectral Unmixing](https://mlsanity.com/paper/2312.13127/) (2023)
- [Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders](https://mlsanity.com/paper/2403.04526/) (2024)
- [SAWU-Net: Spatial Attention Weighted Unmixing Network for Hyperspectral Images](https://mlsanity.com/paper/2304.11320/) (2023)
- [Learning Interpretable Deep Disentangled Neural Networks for Hyperspectral Unmixing](https://mlsanity.com/paper/2310.02340/) (2023)
- [Improving Autoencoder Training Performance for Hyperspectral Unmixing with Network Reinitialisation](https://mlsanity.com/paper/2109.13748/) (2021)
- [GAUSS: Guided Encoder-Decoder Architecture for Hyperspectral Unmixing with Spatial Smoothness](https://mlsanity.com/paper/2204.07713/) (2022)
- [Multitemporal Latent Dynamical Framework for Hyperspectral Images Unmixing](https://mlsanity.com/paper/2505.20902/) (2025)
- [Knowledge-Guided Masked Autoencoder with Linear Spectral Mixing and Spectral-Angle-Aware Reconstruction](https://mlsanity.com/paper/2512.12445/) (2025)
- [SCA-Net: A Self-Correcting Two-Layer Autoencoder for Hyper-spectral Unmixing](https://mlsanity.com/paper/2102.05713/) (2021)
- [Nonlinear Hyperspectral Unmixing based on Multilinear Mixing Model using Convolutional Autoencoders](https://mlsanity.com/paper/2303.08156/) (2023)
- [Deep Nonlinear Hyperspectral Unmixing Using Multi-task Learning](https://mlsanity.com/paper/2402.03398/) (2024)
- [An Elliptic Kernel Unsupervised Autoencoder-Graph Convolutional Network Ensemble Model for Hyperspectral Unmixing](https://mlsanity.com/paper/2406.06742/) (2024)
- [Neural Network for Blind Unmixing: a novel MatrixConv Unmixing (MCU) Approach](https://mlsanity.com/paper/2503.08745/) (2025)
- [SWAN: Self-supervised Wavelet Neural Network for Hyperspectral Image Unmixing](https://mlsanity.com/paper/2510.22607/) (2025)
- [Hyperspectral Unmixing with 3D Convolutional Sparse Coding and Projected Simplex Volume Maximization](https://mlsanity.com/paper/2512.05674/) (2025)
- [Diffusion Posterior Sampler for Hyperspectral Unmixing with Spectral Variability Modeling](https://mlsanity.com/paper/2512.09871/) (2025)
- [Optical Inversion and Spectral Unmixing of Spectroscopic Photoacoustic Images with Physics-Informed Neural Networks](https://mlsanity.com/paper/2602.16357/) (2026)
- [WS-Net: Weak-Signal Representation Learning and Gated Abundance Reconstruction for Hyperspectral Unmixing via State-Space and Weak Signal Attention Fusion](https://mlsanity.com/paper/2603.09037/) (2026)
- [Polyhedral Unmixing: Bridging Semantic Segmentation with Hyperspectral Unmixing via Polyhedral-Cone Partitioning](https://mlsanity.com/paper/2603.29438/) (2026)
- [A Brain-Inspired Deep Separation Network for Single Channel Raman Spectra Unmixing](https://mlsanity.com/paper/2604.22324/) (2026)

## Abstract (arXiv)

Data acquired from multi-channel sensors is a highly valuable asset to interpret the environment for a variety of remote sensing applications. However, low spatial resolution is a critical limitation for previous sensors and the constituent materials of a scene can be mixed in different fractions due to their spatial interactions. Spectral unmixing is a technique that allows us to obtain the material spectral signatures and their fractions from hyperspectral data. In this paper, we propose a novel endmember extraction and hyperspectral unmixing scheme, so called textitEndNet, that is based on a two-staged autoencoder network. This well-known structure is completely enhanced and restructured by introducing additional layers and a projection metric (i.e., spectral angle distance (SAD) instead of inner product) to achieve an optimum solution. Moreover, we present a novel loss function that is composed of a Kullback-Leibler divergence term with SAD similarity and additional penalty terms to improve the sparsity of the estimates. These modifications enable us to set the common properties of endmembers such as non-linearity and sparsity for autoencoder networks. Lastly, due to the stochastic-gradient based approach, the method is scalable for large-scale data and it can be accelerated on Graphical Processing Units (GPUs). To demonstrate the superiority of our proposed method, we conduct extensive experiments on several well-known datasets. The results confirm that the proposed method considerably improves the performance compared to the state-of-the-art techniques in literature.