# Dynamic Multi-Context Segmentation of Remote Sensing Images based on Convolutional Networks

arXiv: https://arxiv.org/abs/1804.04020 · HTML: https://mlsanity.com/paper/1804.04020/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2018.

## Built on by (most cited first, 15 of 15)

- [UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery](https://mlsanity.com/paper/2109.08937/) (2021)
- [A Billion-scale Foundation Model for Remote Sensing Images](https://mlsanity.com/paper/2304.05215/) (2023)
- [A Review on Deep Learning in UAV Remote Sensing](https://mlsanity.com/paper/2101.10861/) (2021)
- [PyramidMamba: Rethinking Pyramid Feature Fusion with Selective Space State Model for Semantic Segmentation of Remote Sensing Imagery](https://mlsanity.com/paper/2406.10828/) (2024)
- [Not Just Learning from Others but Relying on Yourself: A New Perspective on Few-Shot Segmentation in Remote Sensing](https://mlsanity.com/paper/2310.12452/) (2023)
- [An Attention-Fused Network for Semantic Segmentation of Very-High-Resolution Remote Sensing Imagery](https://mlsanity.com/paper/2105.04132/) (2021)
- [Self-Training Guided Disentangled Adaptation for Cross-Domain Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2301.05526/) (2023)
- [ChessMix: Spatial Context Data Augmentation for Remote Sensing Semantic Segmentation](https://mlsanity.com/paper/2108.11535/) (2021)
- [Implicit Ray-Transformers for Multi-view Remote Sensing Image Segmentation](https://mlsanity.com/paper/2303.08401/) (2023)
- [MetaSegNet: Metadata-collaborative Vision-Language Representation Learning for Semantic Segmentation of Remote Sensing Images](https://mlsanity.com/paper/2312.12735/) (2023)
- [Panchromatic and Multispectral Image Fusion via Alternating Reverse Filtering Network](https://mlsanity.com/paper/2210.08181/) (2022)
- [Core-Set Selection for Data-efficient Land Cover Segmentation](https://mlsanity.com/paper/2505.01225/) (2025)
- [Semantic Labeling of High Resolution Images Using EfficientUNets and Transformers](https://mlsanity.com/paper/2206.09731/) (2022)
- [SSG2: A new modelling paradigm for semantic segmentation](https://mlsanity.com/paper/2310.08671/) (2023)
- [Joint-Optimized Unsupervised Adversarial Domain Adaptation in Remote Sensing Segmentation with Prompted Foundation Model](https://mlsanity.com/paper/2411.05878/) (2024)

## Abstract (arXiv)

Semantic segmentation requires methods capable of learning high-level features while dealing with large volume of data. Towards such goal, Convolutional Networks can learn specific and adaptable features based on the data. However, these networks are not capable of processing a whole remote sensing image, given its huge size. To overcome such limitation, the image is processed using fixed size patches. The definition of the input patch size is usually performed empirically (evaluating several sizes) or imposed (by network constraint). Both strategies suffer from drawbacks and could not lead to the best patch size. To alleviate this problem, several works exploited multi-context information by combining networks or layers. This process increases the number of parameters resulting in a more difficult model to train. In this work, we propose a novel technique to perform semantic segmentation of remote sensing images that exploits a multi-context paradigm without increasing the number of parameters while defining, in training time, the best patch size. The main idea is to train a dilated network with distinct patch sizes, allowing it to capture multi-context characteristics from heterogeneous contexts. While processing these varying patches, the network provides a score for each patch size, helping in the definition of the best size for the current scenario. A systematic evaluation of the proposed algorithm is conducted using four high-resolution remote sensing datasets with very distinct properties. Our results show that the proposed algorithm provides improvements in pixelwise classification accuracy when compared to state-of-the-art methods.