# Unsupervised Deep Feature Extraction for Remote Sensing Image Classification

arXiv: https://arxiv.org/abs/1511.08131 · HTML: https://mlsanity.com/paper/1511.08131/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2015.

## Built on by (most cited first, 16 of 16)

- [Self-supervised Learning in Remote Sensing: A Review](https://mlsanity.com/paper/2206.13188/) (2022)
- [Self-supervised Audiovisual Representation Learning for Remote Sensing Data](https://mlsanity.com/paper/2108.00688/) (2021)
- [Self-supervised remote sensing feature learning: Learning Paradigms, Challenges, and Future Works](https://mlsanity.com/paper/2211.08129/) (2022)
- [Learning crop type mapping from regional label proportions in large-scale SAR and optical imagery](https://mlsanity.com/paper/2208.11607/) (2022)
- [Land Cover Mapping in Limited Labels Scenario: A Survey](https://mlsanity.com/paper/2103.02429/) (2021)
- [Selection of contributing factors for predicting landslide susceptibility using machine learning and deep learning models](https://mlsanity.com/paper/2309.06062/) (2023)
- [Airplane Detection Based on Mask Region Convolution Neural Network](https://mlsanity.com/paper/2108.12817/) (2021)
- [CEU-Net: Ensemble Semantic Segmentation of Hyperspectral Images Using Clustering](https://mlsanity.com/paper/2203.04873/) (2022)
- [A generic self-supervised learning (SSL) framework for representation learning from spectra-spatial feature of unlabeled remote sensing imagery](https://mlsanity.com/paper/2306.15836/) (2023)
- [Spatial-Spectral Diffusion Contrastive Representation Network for Hyperspectral Image Classification](https://mlsanity.com/paper/2502.19699/) (2025)
- [Community Detection in Medical Image Datasets: Using Wavelets and Spectral Methods](https://mlsanity.com/paper/2112.12021/) (2021)
- [Bayesian Layer Graph Convolutioanl Network for Hyperspetral Image Classification](https://mlsanity.com/paper/2211.07316/) (2022)
- [Unsupervised ore/waste classification on open-cut mine faces using close-range hyperspectral data](https://mlsanity.com/paper/2302.04936/) (2023)
- [GAIA: A Foundation Model for Operational Atmospheric Dynamics](https://mlsanity.com/paper/2505.18179/) (2025)
- [Leveraging Convolutional and Graph Networks for an Unsupervised Remote Sensing Labelling Tool](https://mlsanity.com/paper/2508.00506/) (2025)
- [Automated Landfill Detection Using Deep Learning: A Comparative Study of Lightweight and Custom Architectures with the AerialWaste Dataset](https://mlsanity.com/paper/2508.18315/) (2025)

## Abstract (arXiv)

This paper introduces the use of single layer and deep convolutional networks for remote sensing data analysis. Direct application to multi- and hyper-spectral imagery of supervised (shallow or deep) convolutional networks is very challenging given the high input data dimensionality and the relatively small amount of available labeled data. Therefore, we propose the use of greedy layer-wise unsupervised pre-training coupled with a highly efficient algorithm for unsupervised learning of sparse features. The algorithm is rooted on sparse representations and enforces both population and lifetime sparsity of the extracted features, simultaneously. We successfully illustrate the expressive power of the extracted representations in several scenarios: classification of aerial scenes, as well as land-use classification in very high resolution (VHR), or land-cover classification from multi- and hyper-spectral images. The proposed algorithm clearly outperforms standard Principal Component Analysis (PCA) and its kernel counterpart (kPCA), as well as current state-of-the-art algorithms of aerial classification, while being extremely computationally efficient at learning representations of data. Results show that single layer convolutional networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels, and are preferred when the classification requires high resolution and detailed results. However, deep architectures significantly outperform single layers variants, capturing increasing levels of abstraction and complexity throughout the feature hierarchy.