# Multi-Granularity Canonical Appearance Pooling for Remote Sensing Scene Classification

arXiv: https://arxiv.org/abs/2004.04491 · HTML: https://mlsanity.com/paper/2004.04491/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2020.

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

- [An Empirical Study of Remote Sensing Pretraining](https://mlsanity.com/paper/2204.02825/) (2022)
- [LSKNet: A Foundation Lightweight Backbone for Remote Sensing](https://mlsanity.com/paper/2403.11735/) (2024)
- [Consecutive Pretraining: A Knowledge Transfer Learning Strategy with Relevant Unlabeled Data for Remote Sensing Domain](https://mlsanity.com/paper/2207.03860/) (2022)
- [Embedded Self-Distillation in Compact Multi-Branch Ensemble Network for Remote Sensing Scene Classification](https://mlsanity.com/paper/2104.00222/) (2021)
- [All Grains, One Scheme (AGOS): Learning Multi-grain Instance Representation for Aerial Scene Classification](https://mlsanity.com/paper/2205.03371/) (2022)
- [AllSpark: A Multimodal Spatio-Temporal General Intelligence Model with Ten Modalities via Language as a Reference Framework](https://mlsanity.com/paper/2401.00546/) (2023)
- [A Light-weight Deep Learning Model for Remote Sensing Image Classification](https://mlsanity.com/paper/2302.13028/) (2023)
- [A Robust and Low Complexity Deep Learning Model for Remote Sensing Image Classification](https://mlsanity.com/paper/2211.02820/) (2022)
- [Enhanced Multi-level Features for Very High Resolution Remote Sensing Scene Classification](https://mlsanity.com/paper/2305.00679/) (2023)
- [Remote Sensing Image Classification using Transfer Learning and Attention Based Deep Neural Network](https://mlsanity.com/paper/2206.13392/) (2022)
- [Contextual Learning in Fourier Complex Field for VHR Remote Sensing Images](https://mlsanity.com/paper/2210.15972/) (2022)
- [Semi-Supervised Crowd Counting from Unlabeled Data](https://mlsanity.com/paper/2108.13969/) (2021)
- [Feature Transformation for Cross-domain Few-shot Remote Sensing Scene Classification](https://mlsanity.com/paper/2203.02270/) (2022)
- [Multi-Granularity Mutual Refinement Network for Zero-Shot Learning](https://mlsanity.com/paper/2511.08163/) (2025)
- [Revisiting Aerial Scene Classification on the AID Benchmark](https://mlsanity.com/paper/2601.18263/) (2026)
- [Dual Contrastive Network for Few-Shot Remote Sensing Image Scene Classification](https://mlsanity.com/paper/2603.23161/) (2026)

## Abstract (arXiv)

Recognising remote sensing scene images remains challenging due to large visual-semantic discrepancies. These mainly arise due to the lack of detailed annotations that can be employed to align pixel-level representations with high-level semantic labels. As the tagging process is labour-intensive and subjective, we hereby propose a novel Multi-Granularity Canonical Appearance Pooling (MG-CAP) to automatically capture the latent ontological structure of remote sensing datasets. We design a granular framework that allows progressively cropping the input image to learn multi-grained features. For each specific granularity, we discover the canonical appearance from a set of pre-defined transformations and learn the corresponding CNN features through a maxout-based Siamese style architecture. Then, we replace the standard CNN features with Gaussian covariance matrices and adopt the proper matrix normalisations for improving the discriminative power of features. Besides, we provide a stable solution for training the eigenvalue-decomposition function (EIG) in a GPU and demonstrate the corresponding back-propagation using matrix calculus. Extensive experiments have shown that our framework can achieve promising results in public remote sensing scene datasets.