Remote Sensing Image Change Detection Based on General Deep Forest Module

Remote sensing image change detection, as one of the important branches of remote sensing technology, has been greatly developed and improved in recent years. The effectiveness of change detection is unstable, as deep learning components exhibit a high degree of sensitivity to hyperparameters, necessitating substantial fine-tuning to achieve optimal outcomes. Additionally, large and intricate deep learning models are not appropriate for smaller-scale datasets. In this paper, a remote sensing image change detection method based on general deep forest module (GDFM) is proposed. In the proposed GDFM, the common remote sensing image change detection method is firstly used for preliminary detection to obtain the detection result to be optimized, and the optimized change detection result is then obtained by using the classification function of the deep forest. This work aims to achieve a general enhancement of the performance of existing change detection methods by utilizing the characteristics of multi-grained scanning in deep forest and the data classification capabilities of cascaded forests. At the same time, this model alleviates the problems of parameter tuning complexity and inapplicability to small datasets in additional deep learning modules. It is worth noting that this general method is not a direct splicing process, but combines the advantages of the two parts and uses the characteristics of the deep forest to achieve the goal. Experiments are conducted on multiple common remote sensing change detection models. The results show that the proposed GDFM has significant improvements in metrics, which include varying degrees of improvement in F1 score by around 1% -14%.