# A Hierarchical Approach to Remote Sensing Scene Classification

arXiv: https://arxiv.org/abs/2103.15463 · HTML: https://mlsanity.com/paper/2103.15463/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 82 references that are in the map.
- First posted on arXiv 29 Mar 2021.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Remote Sensing Image Scene Classification: Benchmark and State of the Art](https://mlsanity.com/paper/1703.00121/) (2017)
- [AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification](https://mlsanity.com/paper/1608.05167/) (2016)
- [Caffe: Convolutional Architecture for Fast Feature Embedding](https://mlsanity.com/paper/1408.5093/) (2014)
- [Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities](https://mlsanity.com/paper/2005.01094/) (2020)
- [Meta-Learning for Few-Shot Land Cover Classification](https://mlsanity.com/paper/2004.13390/) (2020)
- [Towards Better Exploiting Convolutional Neural Networks for Remote Sensing Scene Classification](https://mlsanity.com/paper/1602.01517/) (2016)
- [Land Use Classification in Remote Sensing Images by Convolutional Neural Networks](https://mlsanity.com/paper/1508.00092/) (2015)
- [Learning Structured Inference Neural Networks with Label Relations](https://mlsanity.com/paper/1511.05616/) (2015)
- [RSI-CB: A Large Scale Remote Sensing Image Classification Benchmark via Crowdsource Data](https://mlsanity.com/paper/1705.10450/) (2017)
- [A New Insight into Land Use Classification Based on Aggregated Mobile Phone Data](https://mlsanity.com/paper/1310.6129/) (2013)
- [Hierarchical Metric Learning for Optical Remote Sensing Scene Categorization](https://mlsanity.com/paper/1708.01494/) (2017)

## Abstract (arXiv)

Remote sensing scene classification deals with the problem of classifying land use/cover of a region from images. To predict the development and socioeconomic structures of cities, the status of land use in regions is tracked by the national mapping agencies of countries. Many of these agencies use land-use types that are arranged in multiple levels. In this paper, we examined the efficiency of a hierarchically designed Convolutional Neural Network (CNN) based framework that is suitable for such arrangements. We use the NWPU-RESISC45 dataset for our experiments and arranged this data set in a two-level nested hierarchy. Each node in the designed hierarchy is trained using DenseNet-121 architectures. We provide detailed empirical analysis to compare the performances of this hierarchical scheme and its non-hierarchical counterpart, together with the individual model performances. We also evaluated the performance of the hierarchical structure statistically to validate the presented empirical results. The results of our experiments show that although individual classifiers for different sub-categories in the hierarchical scheme perform considerably well, the accumulation of the classification errors in the cascaded structure prevents its classification performance from exceeding that of the non-hierarchical deep model