# Multi-Temporal Scene Classification and Scene Change Detection with Correlation based Fusion

arXiv: https://arxiv.org/abs/2006.02176 · HTML: https://mlsanity.com/paper/2006.02176/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2020.

## Built on by (most cited first, 13 of 13)

- [Changes to Captions: An Attentive Network for Remote Sensing Change Captioning](https://mlsanity.com/paper/2304.01091/) (2023)
- [3D VSG: Long-term Semantic Scene Change Prediction through 3D Variable Scene Graphs](https://mlsanity.com/paper/2209.07896/) (2022)
- [GraphHop: An Enhanced Label Propagation Method for Node Classification](https://mlsanity.com/paper/2101.02326/) (2021)
- [MultiScene: A Large-scale Dataset and Benchmark for Multi-scene Recognition in Single Aerial Images](https://mlsanity.com/paper/2104.02846/) (2021)
- [Adapting Vision Transformer for Efficient Change Detection](https://mlsanity.com/paper/2312.04869/) (2023)
- [Aerial Scene Understanding in The Wild: Multi-Scene Recognition via Prototype-based Memory Networks](https://mlsanity.com/paper/2104.11200/) (2021)
- [Human Object Interaction Detection using Two-Direction Spatial Enhancement and Exclusive Object Prior](https://mlsanity.com/paper/2105.03089/) (2021)
- [Dsfer-Net: A Deep Supervision and Feature Retrieval Network for Bitemporal Change Detection Using Modern Hopfield Networks](https://mlsanity.com/paper/2304.01101/) (2023)
- [Unsupervised CD in satellite image time series by contrastive learning and feature tracking](https://mlsanity.com/paper/2304.11375/) (2023)
- [NFANet: A Novel Method for Weakly Supervised Water Extraction from High-Resolution Remote Sensing Imagery](https://mlsanity.com/paper/2201.03686/) (2022)
- [SoftMatch Distance: A Novel Distance for Weakly-Supervised Trend Change Detection in Bi-Temporal Images](https://mlsanity.com/paper/2303.04737/) (2023)
- [Hard Region Aware Network for Remote Sensing Change Detection](https://mlsanity.com/paper/2305.19513/) (2023)
- [Lightweight Adapter Learning for More Generalized Remote Sensing Change Detection](https://mlsanity.com/paper/2504.19598/) (2025)

## Abstract (arXiv)

Classifying multi-temporal scene land-use categories and detecting their semantic scene-level changes for imagery covering urban regions could straightly reflect the land-use transitions. Existing methods for scene change detection rarely focus on the temporal correlation of bi-temporal features, and are mainly evaluated on small scale scene change detection datasets. In this work, we proposed a CorrFusion module that fuses the highly correlated components in bi-temporal feature embeddings. We firstly extracts the deep representations of the bi-temporal inputs with deep convolutional networks. Then the extracted features will be projected into a lower dimension space to computed the instance-level correlation. The cross-temporal fusion will be performed based on the computed correlation in CorrFusion module. The final scene classification are obtained with softmax activation layers. In the objective function, we introduced a new formulation for calculating the temporal correlation. The detailed derivation of backpropagation gradients for the proposed module is also given in this paper. Besides, we presented a much larger scale scene change detection dataset and conducted experiments on this dataset. The experimental results demonstrated that our proposed CorrFusion module could remarkably improve the multi-temporal scene classification and scene change detection results.