# Dense Dilated Convolutions Merging Network for Land Cover Classification

arXiv: https://arxiv.org/abs/2003.04027 · HTML: https://mlsanity.com/paper/2003.04027/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2020.

## Built on by (most cited first, 19 of 19)

- [UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery](https://mlsanity.com/paper/2109.08937/) (2021)
- [A Novel Transformer Based Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Images](https://mlsanity.com/paper/2104.12137/) (2021)
- [Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images](https://mlsanity.com/paper/2106.12413/) (2021)
- [ABCNet: Attentive Bilateral Contextual Network for Efficient Semantic Segmentation of Fine-Resolution Remote Sensing Images](https://mlsanity.com/paper/2102.02531/) (2021)
- [SIRST-5K: Exploring Massive Negatives Synthesis with Self-supervised Learning for Robust Infrared Small Target Detection](https://mlsanity.com/paper/2403.05416/) (2024)
- [PyramidMamba: Rethinking Pyramid Feature Fusion with Selective Space State Model for Semantic Segmentation of Remote Sensing Imagery](https://mlsanity.com/paper/2406.10828/) (2024)
- [Aerial Scene Parsing: From Tile-level Scene Classification to Pixel-wise Semantic Labeling](https://mlsanity.com/paper/2201.01953/) (2022)
- [MetaSegNet: Metadata-collaborative Vision-Language Representation Learning for Semantic Segmentation of Remote Sensing Images](https://mlsanity.com/paper/2312.12735/) (2023)
- [Scale-aware Neural Network for Semantic Segmentation of Multi-resolution Remote Sensing Images](https://mlsanity.com/paper/2103.07935/) (2021)
- [Voxels Intersecting along Orthogonal Levels Attention U-Net for Intracerebral Haemorrhage Segmentation in Head CT](https://mlsanity.com/paper/2208.06313/) (2022)
- [Self-Supervised In-Domain Representation Learning for Remote Sensing Image Scene Classification](https://mlsanity.com/paper/2302.01793/) (2023)
- [LMFNet: An Efficient Multimodal Fusion Approach for Semantic Segmentation in High-Resolution Remote Sensing](https://mlsanity.com/paper/2404.13659/) (2024)
- [Edge-guided and Class-balanced Active Learning for Semantic Segmentation of Aerial Images](https://mlsanity.com/paper/2405.18078/) (2024)
- [DDU-Net: A Domain Decomposition-Based CNN for High-Resolution Image Segmentation on Multiple GPUs](https://mlsanity.com/paper/2407.21266/) (2024)
- [A hierarchical deep learning framework for the consistent classification of land use objects in geospatial databases](https://mlsanity.com/paper/2104.06991/) (2021)
- [Multi-modal land cover mapping of remote sensing images using pyramid attention and gated fusion networks](https://mlsanity.com/paper/2111.03845/) (2021)
- [A2-FPN for Semantic Segmentation of Fine-Resolution Remotely Sensed Images](https://mlsanity.com/paper/2102.07997/) (2021)
- [Supervised and Contrastive Self-Supervised In-Domain Representation Learning for Dense Prediction Problems in Remote Sensing](https://mlsanity.com/paper/2301.12541/) (2023)
- [A Contrastive Learning Scheme with Transformer Innate Patches](https://mlsanity.com/paper/2303.14806/) (2023)

## Abstract (arXiv)

Land cover classification of remote sensing images is a challenging task due to limited amounts of annotated data, highly imbalanced classes, frequent incorrect pixel-level annotations, and an inherent complexity in the semantic segmentation task. In this article, we propose a novel architecture called the dense dilated convolutions' merging network (DDCM-Net) to address this task. The proposed DDCM-Net consists of dense dilated image convolutions merged with varying dilation rates. This effectively utilizes rich combinations of dilated convolutions that enlarge the network's receptive fields with fewer parameters and features compared with the state-of-the-art approaches in the remote sensing domain. Importantly, DDCM-Net obtains fused local- and global-context information, in effect incorporating surrounding discriminative capability for multiscale and complex-shaped objects with similar color and textures in very high-resolution aerial imagery. We demonstrate the effectiveness, robustness, and flexibility of the proposed DDCM-Net on the publicly available ISPRS Potsdam and Vaihingen data sets, as well as the DeepGlobe land cover data set. Our single model, trained on three-band Potsdam and Vaihingen data sets, achieves better accuracy in terms of both mean intersection over union (mIoU) and F1-score compared with other published models trained with more than three-band data. We further validate our model on the DeepGlobe data set, achieving state-of-the-art result 56.2% mIoU with much fewer parameters and at a lower computational cost compared with related recent work. Code available at https://github.com/samleoqh/DDCM-Semantic-Segmentation-PyTorch