Gharahbagh Abdorreza Alavi, Hajihashemi Vahid, Machado José J M, Tavares João Manuel R S
Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal.
Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, 4200-465 Porto, Portugal.
Sensors (Basel). 2025 Mar 22;25(7):1988. doi: 10.3390/s25071988.
Land cover classification (LCC) using satellite images is one of the rapidly expanding fields in mapping, highlighting the need for updating existing computational classification methods. Advances in technology and the increasing variety of applications have introduced challenges, such as more complex classes and a demand for greater detail. In recent years, deep learning and Convolutional Neural Networks (CNNs) have significantly enhanced the segmentation of satellite images. Since the training of CNNs requires sophisticated and expensive hardware and significant time, using pre-trained networks has become widespread in the segmentation of satellite image. This study proposes a hybrid synergistic semantic segmentation method based on the Deeplab v3+ network and a clustering-based post-processing scheme. The proposed method accurately classifies various land cover (LC) types in multispectral satellite images, including Pastures, Other Built-Up Areas, Water Bodies, Urban Areas, Grasslands, Forest, Farmland, and Others. The post-processing scheme includes a spectral bag-of-words model and K-medoids clustering to refine the Deeplab v3+ outputs and correct possible errors. The simulation results indicate that combining the post-processing scheme with deep learning improves the Matthews correlation coefficient (MCC) by approximately 5.7% compared to the baseline method. Additionally, the proposed approach is robust to data imbalance cases and can dynamically update its codewords over different seasons. Finally, the proposed synergistic semantic segmentation method was compared with several state-of-the-art segmentation methods in satellite images of Italy's Lake Garda (Lago di Garda) region. The results showed that the proposed method outperformed the best existing techniques by at least 6% in terms of MCC.
利用卫星图像进行土地覆盖分类（LCC）是制图领域中迅速发展的领域之一，这凸显了更新现有计算分类方法的必要性。技术的进步和应用的日益多样化带来了挑战，例如类别更加复杂以及对细节要求更高。近年来，深度学习和卷积神经网络（CNN）显著增强了卫星图像的分割效果。由于CNN的训练需要复杂且昂贵的硬件以及大量时间，因此在卫星图像分割中使用预训练网络已变得很普遍。本研究提出了一种基于Deeplab v3+网络和基于聚类的后处理方案的混合协同语义分割方法。该方法能够准确地对多光谱卫星图像中的各种土地覆盖（LC）类型进行分类，包括牧场、其他建成区、水体、城市区域、草原、森林、农田和其他类型。后处理方案包括光谱词袋模型和K-中心点聚类，以细化Deeplab v3+的输出并纠正可能的错误。模拟结果表明，与基线方法相比，将后处理方案与深度学习相结合可使马修斯相关系数（MCC）提高约5.7%。此外，所提出的方法对数据不平衡情况具有鲁棒性，并且可以在不同季节动态更新其码字。最后，将所提出的协同语义分割方法与意大利加尔达湖（Lago di Garda）地区卫星图像中的几种最新分割方法进行了比较。结果表明，所提出的方法在MCC方面比现有的最佳技术至少高出6%。
