# Effective Use of Dilated Convolutions for Segmenting Small Object Instances in Remote Sensing Imagery

arXiv: https://arxiv.org/abs/1709.00179 · HTML: https://mlsanity.com/paper/1709.00179/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2017.

## Built on by (most cited first, 20 of 20)

- [Samba: Semantic Segmentation of Remotely Sensed Images with State Space Model](https://mlsanity.com/paper/2404.01705/) (2024)
- [BEVDetNet: Bird's Eye View LiDAR Point Cloud based Real-time 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/2104.10780/) (2021)
- [Semantic Segmentation with Labeling Uncertainty and Class Imbalance](https://mlsanity.com/paper/2102.04566/) (2021)
- [Hyper-Convolution Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/2105.10559/) (2021)
- [Hi-ResNet: Edge Detail Enhancement for High-Resolution Remote Sensing Segmentation](https://mlsanity.com/paper/2305.12691/) (2023)
- [Building Extraction from Remote Sensing Images via an Uncertainty-Aware Network](https://mlsanity.com/paper/2307.12309/) (2023)
- [Implicit Ray-Transformers for Multi-view Remote Sensing Image Segmentation](https://mlsanity.com/paper/2303.08401/) (2023)
- [DDCNet: Deep Dilated Convolutional Neural Network for Dense Prediction](https://mlsanity.com/paper/2107.04715/) (2021)
- [Dilated filters for edge detection algorithms](https://mlsanity.com/paper/2106.07395/) (2021)
- [Sci-Net: Scale Invariant Model for Buildings Segmentation from Aerial Imagery](https://mlsanity.com/paper/2111.06812/) (2021)
- [Hyper-Convolutions via Implicit Kernels for Medical Imaging](https://mlsanity.com/paper/2202.02701/) (2022)
- [RAT+: Train Dense, Infer Sparse -- Recurrence Augmented Attention for Dilated Inference](https://mlsanity.com/paper/2602.18196/) (2026)
- [Multi-class motion-based semantic segmentation for ureteroscopy and laser lithotripsy](https://mlsanity.com/paper/2104.01268/) (2021)
- [PixelGame: Infrared small target segmentation as a Nash equilibrium](https://mlsanity.com/paper/2205.13124/) (2022)
- [CASSOD-Net: Cascaded and Separable Structures of Dilated Convolution for Embedded Vision Systems and Applications](https://mlsanity.com/paper/2104.14126/) (2021)
- [RelationRS: Relationship Representation Network for Object Detection in Aerial Images](https://mlsanity.com/paper/2110.06730/) (2021)
- [QDCNN: Quantum Dilated Convolutional Neural Network](https://mlsanity.com/paper/2110.15667/) (2021)
- [A Hybrid Convolutional Neural Network with Meta Feature Learning for Abnormality Detection in Wireless Capsule Endoscopy Images](https://mlsanity.com/paper/2207.09769/) (2022)
- [Neural-FacTOR: Neural Representation Learning for Website Fingerprinting Attack over TOR Anonymity](https://mlsanity.com/paper/2209.12482/) (2022)
- [A Performance Increment Strategy for Semantic Segmentation of Low-Resolution Images from Damaged Roads](https://mlsanity.com/paper/2411.16295/) (2024)

## Abstract (arXiv)

Thanks to recent advances in CNNs, solid improvements have been made in semantic segmentation of high resolution remote sensing imagery. However, most of the previous works have not fully taken into account the specific difficulties that exist in remote sensing tasks. One of such difficulties is that objects are small and crowded in remote sensing imagery. To tackle with this challenging task we have proposed a novel architecture called local feature extraction (LFE) module attached on top of dilated front-end module. The LFE module is based on our findings that aggressively increasing dilation factors fails to aggregate local features due to sparsity of the kernel, and detrimental to small objects. The proposed LFE module solves this problem by aggregating local features with decreasing dilation factor. We tested our network on three remote sensing datasets and acquired remarkably good results for all datasets especially for small objects.