# Crop Land Change Detection with MC&N-PSPNet
## 5 Conclusions  
Based on the PSPNet model, this paper achieves rapid and accurate extraction of agricultural areas through the replacement of the backbone feature extraction network, improvement of the backbone network, and addition of an attention mechanism. The overall accuracy and Kappa value of the final change detection reached 93.24% and 92.29%, respectively, outperforming other competitive methods in terms of performance and achieving a better trade-off between model complexity and performance. This research result also has a vital theoretical value in monitoring agricultural areas by using remote sensing technology and further improves the theoretical system of a remote sensing classification model. The main conclusions are as follows:  
(1) Replacement of backbone feature extraction network. The backbone feature extraction network in the original PSPNet is replaced by ResNet50 with the improved MobileNetV2. This substitution addresses the issue of minimal intra-class differences within the study area, which hinder the expression of the advantages of deep network structures. Moreover, it enhances model accuracy while reducing parameter count and shortening training time.  
(2) Addition of NAM module. The NAM module is integrated into the PSPNet network structure, enhancing model training accuracy and efficiency without increasing network complexity. This achieves the original goal of creating a more lightweight network.  
(3) To sum up, detecting changes in cropland requires the application of suitable techniques for seamless monitoring. While 2D change detection has been widely used, incorporating 3D detection techniques could provide additional valuable insights. Accurate mapping of changes in cropland is essential for understanding the underlying causes of and for analyzing both the ecological and socio-economic consequences of these changes. This understanding is critical for devising effective land management and policy-making strategies. Future work will focus on the extraction of diverse features to enhance the robustness of change detection models. By incorporating multi-temporal, multi-spectral, and spatial features, we aim to capture more complex patterns of change. Additionally, we plan to explore and implement more advanced classifiers, which have shown great promise in other remote sensing applications. These efforts will contribute to improving the accuracy and reliability of cropland change detection, ultimately supporting sustainable agricultural practices and land use management.  
[PERSON], [PERSON] and [PERSON] devised the project, the main conceptual ideas, and the proof outline; [PERSON] performed the training of the neural network and the writing of the first draft; [PERSON] and [PERSON] analyzed the background literature; [PERSON] and [PERSON] were responsible for communicating and coordinating with all co-authors in a timely manner; [PERSON], [PERSON] and [PERSON] critically revised the article; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] contributed to the preparation of and figure items at various stages. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Funds for Central Non-Profit Scientific Institution of China (Y2023 PT05), the Education Bureau of Liaoning Province (LJKMZ0220638), Liaoning Institute of Science and Technology (2307B29).