EoCD: Encoder only Remote Sensing Change Detection

TL;DR

EoCD introduces a simplified encoder-only approach for remote sensing change detection, combining early fusion with a parameter-free multiscale feature fusion to reduce complexity while maintaining high performance.

Contribution

The paper proposes an encoder-only change detection method that eliminates the need for complex decoders, balancing accuracy and computational efficiency.

Findings

EoCD achieves competitive change detection accuracy across multiple datasets.

The method significantly reduces model complexity and inference time.

Performance depends mainly on the encoder architecture, not the decoder.

Abstract

Being a cornerstone of temporal analysis, change detection has been playing a pivotal role in modern earth observation. Existing change detection methods rely on the Siamese encoder to individually extract temporal features followed by temporal fusion. Subsequently, these methods design sophisticated decoders to improve the change detection performance without taking into consideration the complexity of the model. These aforementioned issues intensify the overall computational cost as well as the network's complexity which is undesirable. Alternatively, few methods utilize the early fusion scheme to combine the temporal images. These methods prevent the extra overhead of Siamese encoder, however, they also rely on sophisticated decoders for better performance. In addition, these methods demonstrate inferior performance as compared to late fusion based methods. To bridge these gaps, we introduce encoder only change detection (EoCD) that is a simple and effective method for the change detection task. The proposed method performs the early fusion of the temporal data and replaces the decoder with a parameter-free multiscale feature fusion module thereby significantly reducing the overall complexity of the model. EoCD demonstrate the optimal balance between the change detection performance and the prediction speed across a variety of encoder architectures. Additionally, EoCD demonstrate that the performance of the model is predominantly dependent on the encoder network, making the decoder an additional component. Extensive experimentation on four challenging change detection datasets reveals the effectiveness of the proposed method.