# MSSFC-Net:Enhancing Building Interpretation with Multi-Scale Spatial-Spectral Feature Collaboration

arXiv: https://arxiv.org/abs/2504.00759 · HTML: https://mlsanity.com/paper/2504.00759/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 34 of its 72 references that are in the map.
- First posted on arXiv 1 Apr 2025.

## Builds on (most cited first, 30 of 34)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [Uformer: A General U-Shaped Transformer for Image Restoration](https://mlsanity.com/paper/2106.03106/) (2021)
- [ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks](https://mlsanity.com/paper/1910.03151/) (2019)
- [Fully Convolutional Siamese Networks for Change Detection](https://mlsanity.com/paper/1810.08462/) (2018)
- [Remote Sensing Image Change Detection with Transformers](https://mlsanity.com/paper/2103.00208/) (2021)
- [A Transformer-Based Siamese Network for Change Detection](https://mlsanity.com/paper/2201.01293/) (2022)
- [UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery](https://mlsanity.com/paper/2109.08937/) (2021)
- [Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks](https://mlsanity.com/paper/2105.02358/) (2021)
- [Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion Models](https://mlsanity.com/paper/2310.06313/) (2023)
- [IMAGDressing-v1: Customizable Virtual Dressing](https://mlsanity.com/paper/2407.12705/) (2024)
- [Changer: Feature Interaction is What You Need for Change Detection](https://mlsanity.com/paper/2209.08290/) (2022)
- [Multi-Attention-Network for Semantic Segmentation of Fine Resolution Remote Sensing Images](https://mlsanity.com/paper/2009.02130/) (2020)
- [Boosting Consistency in Story Visualization with Rich-Contextual Conditional Diffusion Models](https://mlsanity.com/paper/2407.02482/) (2024)
- [MAP-Net: Multi Attending Path Neural Network for Building Footprint Extraction from Remote Sensed Imagery](https://mlsanity.com/paper/1910.12060/) (2019)
- [Building extraction with vision transformer](https://mlsanity.com/paper/2111.15637/) (2021)
- [ELGC-Net: Efficient Local-Global Context Aggregation for Remote Sensing Change Detection](https://mlsanity.com/paper/2403.17909/) (2024)
- [Detecting Building Changes with Off-Nadir Aerial Images](https://mlsanity.com/paper/2301.10922/) (2023)
- [RSBuilding: Towards General Remote Sensing Image Building Extraction and Change Detection with Foundation Model](https://mlsanity.com/paper/2403.07564/) (2024)
- [SiamixFormer: a fully-transformer Siamese network with temporal Fusion for accurate building detection and change detection in bi-temporal remote sensing images](https://mlsanity.com/paper/2208.00657/) (2022)

## Abstract (arXiv)

Building interpretation from remote sensing imagery primarily involves two fundamental tasks: building extraction and change detection. However, most existing methods address these tasks independently, overlooking their inherent correlation and failing to exploit shared feature representations for mutual enhancement. Furthermore, the diverse spectral,spatial, and scale characteristics of buildings pose additional challenges in jointly modeling spatial-spectral multi-scale features and effectively balancing precision and recall. The limited synergy between spatial and spectral representations often results in reduced detection accuracy and incomplete change localization.To address these challenges, we propose a Multi-Scale Spatial-Spectral Feature Cooperative Dual-Task Network (MSSFC-Net) for joint building extraction and change detection in remote sensing images. The framework integrates both tasks within a unified architecture, leveraging their complementary nature to simultaneously extract building and change features. Specifically,a Dual-branch Multi-scale Feature Extraction module (DMFE) with Spatial-Spectral Feature Collaboration (SSFC) is designed to enhance multi-scale representation learning, effectively capturing shallow texture details and deep semantic information, thus improving building extraction performance. For temporal feature aggregation, we introduce a Multi-scale Differential Fusion Module (MDFM) that explicitly models the interaction between differential and dual-temporal features. This module refines the network's capability to detect large-area changes and subtle structural variations in buildings. Extensive experiments conducted on three benchmark datasets demonstrate that MSSFC-Net achieves superior performance in both building extraction and change detection tasks, effectively improving detection accuracy while maintaining completeness.