# Crop Land Change Detection with MC&N-PSPNet
###### Abstract
[12] proposed a DeepLabV3+ network that incorporates a class feature attention mechanism, which showed better performance in segmenting boundary areas of different features in high-resolution remote sensing images in experiments on a public dataset (GID), but the model was unable to accurately segment in some small scenes. [PERSON] et al. [13] proposed Pyramid-SCDFormer, a twin network model based on Transformer, which solves the problem of accurate recognition of small-scale objects and changing boundaries by using the self-attention mechanism to capture multi-scale features. Literature combined Siamese networks and U-net++ networks to design the SNUNet-CD network, which achieved independent feature extraction from different images and combined multi-level semantic information [14]. [PERSON] et al. [15] proposed a twin neural network change detection model SSCNN-S that combines spectral and spatial information, which effectively retains spatial information and improves change detection accuracy without loss of efficiency. Based on the Non-Local [16] mechanism, a spatiotemporal attention module was developed to combine multi-scale feature information with a pyramid pooling module [17]. [PERSON] et al. [18] took Changzhou City as the study area and improved the U-Net network by using the residual structure and attention mechanism, proposing the RMAU-Net network model to realize the fine extraction of cropland. Although these studies have improved the accuracy of remote sensing image segmentation to some extent, there are still defects in the continuity and omission of segmentation at the edges of land cover and small targets. Agricultural area classification and remote sensing monitoring play a crucial role in modern agricultural management. Accurate agricultural area classification can help policymakers to formulate more precise agricultural policies, improve the efficiency of agricultural production, reduce the waste of resources, and promote sustainable agricultural development. However, existing classification models often show certain limitations when facing complex agricultural environments and changes, such as insufficient classification accuracy, discontinuous smoothing of feature boundaries, and inaccurate change detection. The general aim is to better maintain high-dimensional features, ensure that the feature edge segmentation is continuous and not missed, ensure the light weight of the network, and improve the efficiency. Thus, in this paper, firstly, the backbone network ResNet50 is replaced by the more lightweight MobileNetV2 [19] model. Secondly, the CBAM (Convolutional Block Attention Module) module is added to the MobileNetV2 backbone feature extraction network to enhance the parsing ability of the agricultural area as a way to improve the shallow feature classification accuracy and, lastly, the attention mechanism NAM is added after feature fusion to capture richer and more accurate features at different layers to capture richer and more differentiated feature information, which improves the accuracy and robustness of the model without increasing the complexity of the model. Based on extracting the results of the two periods of agricultural areas, the change detection results are obtained by the difference method. Based on the improvement of the method in this paper, excellent performance indexes are achieved in the experiments, which are of great significance for realizing efficient agricultural monitoring.