Lightweight change detection models are essential for industrial applications and edge devices. Reducing the model size while maintaining high accuracy is a key challenge in developing lightweight change detection models. However, many existing methods oversimplify the model architecture, leading to a loss of information and reduced performance. Therefore, developing a lightweight model that can effectively preserve the input information is a challenging problem. To address this challenge, we propose LightCDNet, a novel lightweight change detection model that effectively preserves the input information. LightCDNet consists of an early fusion backbone network and a pyramid decoder for end-to-end change detection. The core component of LightCDNet is the Deep Supervised Fusion Module (DSFM), which guides the early fusion of primary features to improve performance. We evaluated LightCDNet on the LEVIR-CD dataset and found that it achieved comparable or better performance than state-of-the-art models while being 10 to 117 times smaller in size.

Detection of forest changes is an important part of the restoration of forest land resources. Timely detection of forest changes is helpful for standardized management of government departments. However, due to the interference of seasonal factors, there are a large number of pseudo changes in the forest change detection results. Current methods of change detection focus on extracting more accurate boundaries of change. In order to suppress pseudo changes in the results of forest change detection, we constructed a model Forest-CD based on the encoder–decoder structure with the help of background information. The Forest-CD encoder uses the Swin transformer as a backbone for extracting the change features and effectively simulating global information. The decoder uses the feature pyramid network to recover feature scale and fuse information at multiple scales. Experiments on a large forest change dataset show that Forest-CD has a higher $F1$ -Score (0.614) compared to other change detection models. The visualization results show that Forest-CD can well suppress pseudo changes caused by seasonal factors in forestry scenes and can more accurately capture the boundaries of change.

Dynamic detection of forest change is the fundamental method of monitoring forest resources and an essential means of preserving the accuracy and timeliness of forest land resource data. This study focuses on a deep learning-based method for dynamic forest change detection using Sentinel-2 satellite data, especially within mountainous areas. First, the performance of various deep learning models (U-Net++, U-Net, LinkNet, DeepLabV3+, and STANet) and various loss functions (CrossEntropyLoss(CELoss), DiceLoss, FocalLoss, and their combinations) are compared on a self-made dataset. Next, the best model and loss function is used to predict the annual forest change in Hunan Province from 2017 to 2021, and the detection results are evaluated in 12 sample areas. Finally, forest changes are detected in Sentinel-2 images for each quarter of 2017–2021. In addition, a dynamic detection map of forest change in Hunan Province from 2017 to 2021 is drawn. The results reveal that the U-Net++ model and the CELoss performed the best on the self-made dataset, with a Precision of 0.795, a Recall of 0.748, and an F1-score of 0.771. The results of annual and quarterly forest change detection were consistent with the changes in the Sentinel-2 images with accurate boundaries. This result demonstrates the high practicality and generalizability of the method used in this paper. This paper achieves a rapid and accurate extraction of multi-temporal Sentinel-2 image forest change areas based on the U-Net++ model, which can be used as a benchmark for future large territorial areas monitoring and management of forest resources.

The use of remote sensing images to detect forest changes is of great significance for forest resource management. With the development and implementation of deep learning algorithms in change detection, a large number of models have been designed to detect changes in multi-phase remote sensing images. Although synthetic aperture radar (SAR) data have strong potential for application in forest change detection tasks, most existing deep learning-based models have been designed for optical imagery. Therefore, to effectively combine optical and SAR data in forest change detection, this paper proposes a double Siamese branch-based change detection network called DSNUNet. DSNUNet uses two sets of feature branches to extract features from dual-phase optical and SAR images and employs shared weights to combine features into groups. In the proposed DSNUNet, different feature extraction branch widths were used to compensate for a difference in the amount of information between optical and SAR images. The proposed DSNUNet was validated by experiments on the manually annotated forest change detection dataset. According to the obtained results, the proposed method outperformed other change detection methods, achieving an F1-score of 76.40%. In addition, different combinations of width between feature extraction branches were analyzed in this study. The results revealed an optimal performance of the model at initial channel numbers of the optical imaging branch and SAR image branch of 32 and 8, respectively. The prediction results demonstrated the effectiveness of the proposed method in accurately predicting forest changes and suppressing cloud interferences to some extent.

Camellia oleifera is a vital economic crop of southern China. Accurate mapping and monitoring of Camellia oleifera plantations are essential for promoting sustainable operations within the Camellia oleifera industry. However, traditional remote sensing interpretation methods are no longer feasible for the large-scale extraction of plantation areas. This study proposes a novel deep learning-based method that utilizes GF-2 remote sensing imagery to achieve precise mapping and efficient monitoring of Camellia oleifera plantations. First, we conducted a comparative analysis of the performance of various semantic segmentation models using a self-compiled dataset of Camellia oleifera plantations. Subsequently, we proceeded to validate the prediction results obtained from the most effective deep-learning network model for Camellia oleifera plantations in Hengyang City. Finally, we incorporated DEM data to analyze the spatial distribution patterns. The findings indicate that the U-Net++ network model outperforms other semantic segmentation methods when applied to our self-generated dataset of Camellia oleifera plantations. It achieves a recall rate of 0.89, a precision rate of 0.92, and an mIOU of 0.83, demonstrating the effectiveness of the proposed method in identifying and monitoring Camellia oleifera plantations. By combining the predicted results with the data from DEM, we discovered that these plantations are typically situated at elevations ranging from 50 to 200 m, with slopes below 25°, and facing south or southeast. Moreover, a significant positive spatial correlation and clustering phenomenon are observed among the townships in Hengyang City. The method proposed in this study facilitates rapid and precise identification and monitoring of Camellia oleifera plantations, offering significant theoretical support and a scientific foundation for the management and ecological conservation of Camellia oleifera plantations.