基于轻量语义分割网络的遥感土地覆盖分类

高分辨率遥感图像有丰富的空间特征, 针对遥感土地覆盖方法中模型复杂, 边界模糊和多尺度分割等问题, 提出了一种基于边界与多尺度信息的轻量化语义分割网络. 首先, 使用轻量化的MobileNetV3分类器, 采用深度可分离卷积来减少计算量. 其次, 使用自顶向下和自底向上的特征金字塔结构来进行多尺度分割. 接着, 设计了一个边界增强模块, 为分割任务提供丰富的边界细节信息. 然后, 设计了一个特征融合模块, 融合边界与多尺度语义特征. 最后, 使用交叉熵损失函数和Dice损失函数来处理样本不平衡的问题. 在 WHDLD数据集的平均交并比达到了59.64%, 总体精度达到了87.68%. 在DeepGlobe数据集的平均交并比达到了70.42%, 总体精度达到了88.81%. 实验结果表明, 该模型能快速有效地实现遥感图像土地覆盖分类.

Remote Sensing Land Cover Classification Based on Lightweight Semantic Segmentation Network

High-resolution remote sensing images have rich spatial features. To solve the problems of complex models, blurred boundaries, and multi-scale segmentation in remote sensing land cover methods, this study proposes a lightweight semantic segmentation network based on boundary and multi-scale information. First, the method uses a lightweight MobileNetV3 classifier and depthwise separable convolutions to reduce computation. Second, the method adopts top-down and bottom-up feature pyramid structures for multi-scale segmentation. Next, a boundary enhancement module is designed to provide rich boundary detail information for the segmentation task. Then, the method designs a feature fusion module to fuse boundary and multi-scale semantic features. Finally, the method applies cross-entropy and Dice loss functions to deal with the sample imbalance. The mean intersection over union of the WHDLD dataset reaches 59.64%, and the overall accuracy reaches 87.68%. The mean intersection over union of the DeepGlobe dataset reaches 70.42%, and the overall accuracy reaches 88.81%. The experimental results show that the model can quickly and effectively realize the land cover classification of remote sensing images.