As an important part of the ecosystem, wetlands provide varies of ecological functions while they have also been increasingly threatened and degraded. Therefore, it is necessary to protect wetlands with effective monitoring measures such as classification. Considering the quick development of multispectral sensors, multispectral images are available to classify wetlands. The current method, however, requires large quantities of samples and can therefore only be applied in some categories. The gist of this study is to propose and evaluate a feature sequence-based three-dimensional (3D) convolutional method for wetland classification from multispectral images. First, 3D convolution kernels based on feature sequence are designed. Then, an optimal 3D convolutional neural network framework is constructed after fine-tuning, based on which the training and initial classification are executed successively. Next, a correction algorithm based on data similarity matching theory is introduced to correct the misclassifications. Finally, the performance of each step has been evaluated and classification results of the proposed method have been compared with other existing methods. Results have shown that the overall accuracies of the proposed method have exceeded 74% for the research categories and it performs better than other methods when there are complex wetlands combined of multiple contents.

作为生态系统的重要组成部分，湿地提供了各种生态功能，同时也越来越受到威胁和退化。因此，有必要通过分类等有效的监视手段来保护湿地。考虑到多光谱传感器的快速发展，可用多光谱图像对湿地进行分类。但是，当前方法需要大量样本，因此只能应用于某些类别。这项研究的要点是提出和评估基于特征序列的三维（3D）卷积方法，用于从多光谱图像进行湿地分类。首先，设计了基于特征序列的3D卷积核。然后，经过微调，构造了一个最佳的3D卷积神经网络框架，以此为基础，依次执行训练和初始分类。接下来，介绍了一种基于数据相似性匹配理论的校正算法来校正错误分类。最后，评估了每个步骤的性能，并将该方法的分类结果与其他现有方法进行了比较。结果表明，该方法在研究类别中的总体准确率已超过74％，并且在存在多个内容的复杂湿地时，其性能优于其他方法。评估了每个步骤的性能，并将该方法的分类结果与其他现有方法进行了比较。结果表明，该方法在研究类别中的总体准确率已超过74％，并且在存在多个内容的复杂湿地时，其性能优于其他方法。评估了每个步骤的性能，并将该方法的分类结果与其他现有方法进行了比较。结果表明，该方法在研究类别中的总体准确率已超过74％，并且在存在多个内容的复杂湿地时，其性能优于其他方法。