# Semantic Labeling in Very High Resolution Images via a Self-Cascaded Convolutional Neural Network

arXiv: https://arxiv.org/abs/1807.11236 · HTML: https://mlsanity.com/paper/1807.11236/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2018.

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

- [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)
- [SOSD-Net: Joint Semantic Object Segmentation and Depth Estimation from Monocular images](https://mlsanity.com/paper/2101.07422/) (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)
- [Consecutive Pretraining: A Knowledge Transfer Learning Strategy with Relevant Unlabeled Data for Remote Sensing Domain](https://mlsanity.com/paper/2207.03860/) (2022)
- [CM-UNet: Hybrid CNN-Mamba UNet for Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2405.10530/) (2024)
- [Decomposition-based Unsupervised Domain Adaptation for Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2404.04531/) (2024)
- [An Attention-Fused Network for Semantic Segmentation of Very-High-Resolution Remote Sensing Imagery](https://mlsanity.com/paper/2105.04132/) (2021)
- [Remote Sensing Image Segmentation Using Vision Mamba and Multi-Scale Multi-Frequency Feature Fusion](https://mlsanity.com/paper/2410.05624/) (2024)
- [Scale-aware Neural Network for Semantic Segmentation of Multi-resolution Remote Sensing Images](https://mlsanity.com/paper/2103.07935/) (2021)
- [Building-road Collaborative Extraction from Remotely Sensed Images via Cross-Interaction](https://mlsanity.com/paper/2307.12256/) (2023)
- [A hierarchical deep learning framework for the consistent classification of land use objects in geospatial databases](https://mlsanity.com/paper/2104.06991/) (2021)
- [Semantic Labeling of High Resolution Images Using EfficientUNets and Transformers](https://mlsanity.com/paper/2206.09731/) (2022)
- [MultiScale Probability Map guided Index Pooling with Attention-based learning for Road and Building Segmentation](https://mlsanity.com/paper/2302.09411/) (2023)
- [A Contrastive Learning Scheme with Transformer Innate Patches](https://mlsanity.com/paper/2303.14806/) (2023)
- [Threshold Attention Network for Semantic Segmentation of Remote Sensing Images](https://mlsanity.com/paper/2501.07984/) (2025)
- [Feature-Augmented Deep Networks for Multiscale Building Segmentation in High-Resolution UAV and Satellite Imagery](https://mlsanity.com/paper/2505.05321/) (2025)
- [EMRA-proxy: Enhancing Multi-Class Region Semantic Segmentation in Remote Sensing Images with Attention Proxy](https://mlsanity.com/paper/2505.17665/) (2025)
- [Parameter-Efficient Modality-Balanced Symmetric Fusion for Multimodal Remote Sensing Semantic Segmentation](https://mlsanity.com/paper/2603.17705/) (2026)

## Abstract (arXiv)

Semantic labeling for very high resolution (VHR) images in urban areas, is of significant importance in a wide range of remote sensing applications. However, many confusing manmade objects and intricate fine-structured objects make it very difficult to obtain both coherent and accurate labeling results. For this challenging task, we propose a novel deep model with convolutional neural networks (CNNs), i.e., an end-to-end self-cascaded network (ScasNet). Specifically, for confusing manmade objects, ScasNet improves the labeling coherence with sequential global-to-local contexts aggregation. Technically, multi-scale contexts are captured on the output of a CNN encoder, and then they are successively aggregated in a self-cascaded manner. Meanwhile, for fine-structured objects, ScasNet boosts the labeling accuracy with a coarse-to-fine refinement strategy. It progressively refines the target objects using the low-level features learned by CNN's shallow layers. In addition, to correct the latent fitting residual caused by multi-feature fusion inside ScasNet, a dedicated residual correction scheme is proposed. It greatly improves the effectiveness of ScasNet. Extensive experimental results on three public datasets, including two challenging benchmarks, show that ScasNet achieves the state-of-the-art performance.