A Method to Reduce the Misclassification of Forest Change Detection Based on High-Resolution Images

Forest change detection is crucial in environmental protection and ecological monitoring, but this task faces many challenges. First, forests are significantly affected by seasonal factors, and seasonal defoliation of trees can lead to misclassification, making it difficult to accurately identify actual forest changes. Crop harvests are also prone to misclassification due to their spectral similarity to forest changes, further complicating the detection task. The high degree of irregularity in forest change patches increases the difficulty of accurately detecting forest change boundary. Traditional convolutional neural network (CNN) backbone networks, such as VGG and ResNet, faced with the above challenges, are prone to misclassification in forest change detection tasks due to their small receptive fields relatively. To alleviate this problem, we propose the ConvNeXt-Uphead, a forest change detection model which can reduce the misclassification by extending the receptive field to improved ability to capture more information around changing areas. The experimental result demonstrate that the ConvNeXt-Uphead model excels in accurately identifying forest changes. Visualization results further show that its boundary detection is more precise and this way can better capture subtle changes.