Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions, Harbin Normal University, Harbin 150025, China.
College of Geographic Science, Harbin Normal University, Harbin 150025, China.
Comput Intell Neurosci. 2022 May 18;2022:5303872. doi: 10.1155/2022/5303872. eCollection 2022.
Wetlands have important ecological value. The application of wetland remote sensing is essential for the timely and accurate analysis of the current situation in wetlands and dynamic changes in wetland resources, but high-resolution remote sensing images display nonobvious boundaries between wetland types. However, high classification accuracy and time efficiency cannot be guaranteed simultaneously. Extraction of wetland type information based on high-spatial-resolution remote sensing images is a bottleneck that has hindered wetland development research and change detection. This paper proposes an automatic and efficient method for extracting wetland type information. First, the object-oriented multiscale segmentation method is used to realize the fine segmentation of high-resolution remote sensing images, and then the deep convolutional neural network model AlexNet is used to classify automatically the types of wetland images. The method is verified in a case study involving field-measured data, and the classification results are compared with those of traditional classification methods. The results show that the proposed method can more accurately and efficiently extract different wetland types in high-resolution remote sensing images than the traditional classification methods. The proposed method will be helpful in the extension and application of wetland remote sensing technology and will provide technical support for the protection, development, and utilization of wetland resources.
湿地具有重要的生态价值。湿地遥感的应用对于及时、准确地分析湿地现状和湿地资源的动态变化至关重要，但高分辨率遥感图像显示湿地类型之间的边界不明显。然而，高分类精度和时间效率不能同时保证。基于高空间分辨率遥感图像提取湿地类型信息是阻碍湿地发展研究和变化检测的瓶颈。本文提出了一种自动高效的湿地类型信息提取方法。首先，利用面向对象的多尺度分割方法实现高分辨率遥感图像的精细分割，然后利用深度卷积神经网络模型 AlexNet 对湿地图像的类型进行自动分类。该方法在一个包含实地测量数据的案例研究中得到了验证，并将分类结果与传统分类方法进行了比较。结果表明，与传统分类方法相比，该方法能够更准确、更高效地提取高分辨率遥感图像中的不同湿地类型。该方法将有助于湿地遥感技术的扩展和应用，为湿地资源的保护、开发和利用提供技术支持。
