# Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition and Remote Sensing Scene Classification

arXiv: https://arxiv.org/abs/1706.01171 · HTML: https://mlsanity.com/paper/1706.01171/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2017.

## Built on by (most cited first, 7 of 7)

- [All Grains, One Scheme (AGOS): Learning Multi-grain Instance Representation for Aerial Scene Classification](https://mlsanity.com/paper/2205.03371/) (2022)
- [A Lightweight ReLU-Based Feature Fusion for Aerial Scene Classification](https://mlsanity.com/paper/2106.07879/) (2021)
- [Histogram Layers for Neural Engineered Features](https://mlsanity.com/paper/2403.17176/) (2024)
- [Unleashing Foundation Vision Models: Adaptive Transfer for Diverse Data-Limited Scientific Domains](https://mlsanity.com/paper/2512.22664/) (2025)
- [Survey on Remote Sensing Scene Classification: From Traditional Methods to Large Generative AI Models](https://mlsanity.com/paper/2603.26751/) (2026)
- [Learning Instance Representation Banks for Aerial Scene Classification](https://mlsanity.com/paper/2205.13744/) (2022)
- [DDIPNet and DDIPNet+: Discriminant Deep Image Prior Networks for Remote Sensing Image Classification](https://mlsanity.com/paper/2212.10411/) (2022)

## Abstract (arXiv)

Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The d facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Binary Patterns encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Our final combination outperforms the state-of-the-art without employing fine-tuning or ensemble of RGB network architectures.