TINYCD: a (not so) deep learning model for change detection

Abstract

In this paper, we present a lightweight and effective change detection model, called TinyCD. This model has been designed to be faster and smaller than current state-of-the-art change detection models due to industrial needs. Despite being from 13 to 140 times smaller than the compared change detection models, and exposing at least a third of the computational complexity, our model outperforms the current state-of-the-art models by at least $1\%$ on both F1-score and IoU on the LEVIR-CD dataset, and more than $8\%$ on the WHU-CD dataset. To reach these results, TinyCD uses a Siamese U-Net architecture exploiting low-level features in a globally temporal and locally spatial way. In addition, it adopts a new strategy to mix features in the space-time domain both to merge the embeddings obtained from the Siamese backbones, and, coupled with an MLP block, it forms a novel space-semantic attention mechanism, the Mix and Attention Mask Block (MAMB).

Appendices

Appendix 1 Backbones comparison

In this Appendix we report the results obtained by varying the backbone adopted in the model. In each backbone we select only few initial blocks in order to work with features that are not very complex and not excessively aggregated from a spatial point of view. We decide to select the all the initial blocks up to the first having spatial resolution 32$\times$32. Due to the different compositions of the considered networks, the final size of the models changes it the range starting from a minimum of 32k parameters up to 1.3 M.

As we can see from Table 9, the results obtained are stable from the performances point of view. The backbones of the EfficientNet family appear to be, in accordance with the experiments carried out on our proprietary dataset, those that achieve the best performances. However, the other backbone types also produce comparable results making our approach:

• robust with respect to the backbone used;

• flexible with respect to the required size and computational complexity.

In this comparison we have not considered Transformers-type backbones such as [70, 71]. The reason for this choice lies in the fact that the philosophy of the Transformers is a global philosophy, as opposed to the blocks we propose which are instead local. As mentioned in Sect. 7, an integration of these two philosophies will be the subject of future works.

Table 9 Comparison of different backbones on LEVIR-CD dataset

Appendix 2 Hyperparameters tuning

In this Appendix we report the details about the hyperparameters tuning experiments. One of the advantages of using limited computational complexity models is being able to fine-tune hyperparameters using relatively few computational resources and in a reasonable time from an industrial point of view. In our experiments we tune the learning rate, the weight decay, and the usage of the amsgrad strategy. The framework used to run the experiments and optimize the hyperparameters is NNI [65].

Since we execute only 100 epochs per run, we chose a higher learning rate range $(10^{-3},4\cdot 10^{-3})$, in order to explore whether a higher than standard learning rate leads to faster model convergence. As for the weight decay, we follow a conservative choice by setting the range between $10^{-2}$ and $8\cdot 10^{-3}$. We also test other simple loss functions for model training such as Mean Square Error (MSE), Intersection over Union (IoU) and a combination of IoU and BCE.

In Fig. 5 we show the various combinations of hyperparameters explored in a batch of 30 experiments, and the relative performances on the LEVIR-CD validation set. Analyzing the results, we note that BCE and MSE, regardless of the other parameters, obtain superior performance compared to the IoU. In addition, the BCE + IoU combination, although better than IoU, also scores lower than the BCE and MSE. Regarding the other hyperparameters, as can be seen in particular from Fig. 6, our model obtains robust performances with respect to all the tested combinations. Finally, we note that in the conducted experiments, BCE has lower variance in terms of F1-score with respect to the choices of the other hyperparameters. This represents another motivation for us to chose BCE as loss function.

Fig. 5

Different combination of parameters and their impact on the F1-score on the LEVIR-CD dataset

Fig. 6

Behavior of the final F1-score in the different experiments conducted to tune the hyperparameters. The drop in the F1-score is due to the use of IoU as loss function