# Crop Land Change Detection with MC&N-PSPNet
###### Abstract  
To enhance the accuracy of agricultural area classification and enable remote sensing monitoring of agricultural regions, this paper investigates classification models and their application in change detection within rural areas, proposing the MC&N-PSPNet (CBAM into MobileNetV2 and NAM into PSPNet) network model. Initially, the HRSC (High Resolution Semantic Change Detection) dataset labels undergo binary redrawing. Subsequently, to efficiently extract image features, the original PSPNet (Pyramid Scene Parsing Network) backbone network, ResNet50 (Residual Network-50), is substituted with the MobileNetV2 (Inverted Residuals and Linear Bottlenecks) model. Furthermore, to enhance the model's training efficiency and classification accuracy, the NAM (Normalization-Based Attention Module) attention mechanism is introduced into the improved PSPNet model to obtain the categories of land cover changes in remote sensing images before and after the designated periods. Finally, the final change detection results are obtained by performing a different operation on the classification results for different periods. Through experimental analysis, this paper demonstrates the proposed method's superior capability in segmenting agricultural areas, which is crucial for effective agricultural area change detection. The model achieves commendable performance metrics, including overall accuracy, Kappa value, MIoU, and MPA values of 95.03%, 88.15%, 93.55%, and 88.90%, respectively, surpassing other models. Moreover, the model exhibits robust performance in final change detection, achieving an overall accuracy and Kappa value of 93.24% and 92.29%, respectively. The results of this study show that the MC&N-PSPNet model has significant advantages in the detection of changes in agricultural zones, which provides a scientific basis and technical support for agricultural resource management and policy formulation. deep learning network model; self-attention; semantic segmentation; change detection +
Footnote †: journal: MedicalThere are two main categories of methods for recognizing the type of change in features [4]: first classification and then change, as well as direct change detection. The former is to classify different simultaneous images separately, and then compare the classification knots of the before and after images pixel by pixel. In the latter case, change detection is performed directly by superimposing images of different time phases, simultaneously detecting and classifying the features of the changed part and the features of the unchanged part, so as to minimize the impact of classification errors on the change detection results. In post-classification change detection, traditional remote sensing image segmentation methods include those based on spectral, spatial, and a combination of spectral and spatial image segmentation [5; 6; 7]. Spectral-based image segmentation, or pixel-based image segmentation, primarily analyzes individual pixels. Spatial-based image segmentation attempts to incorporate spatial location information by detecting edges between regions for image segmentation. However, traditional methods often suffer from low robustness, over-segmentation, and high computational demands [8]. In recent years, with the rapid development of deep learning and neural networks, many scholars at home and abroad have begun to use deep learning for semantic segmentation based on multitemporal images to detect the change region and achieve good research results. [PERSON] et al. [9] proposed the Res-UNet network by replacing the basic units of the ResNet [10] network with the convolutional layers of the U-Net [11] network, combining the underlying features obtained from down-sampling with the up-sampled inputs through jumping connection. The improved U-Net network achieved high classification accuracy in high-resolution remote sensing images. [PERSON] et al.