Abstract
Semantic segmentation in marine environments is crucial for the autonomous navigation of unmanned surface vessels (USVs) and coastal Earth Observation events such as oil spills. However, existing methods, often relying on deep CNNs and transformer-based architectures, face challenges in deployment due to their high computational costs and resource-intensive nature. These limitations hinder the practicality of real-time, low-cost applications in real-world marine settings. To address this, we propose LEMMA, a lightweight semantic segmentation model designed specifically for accurate remote sensing segmentation under resource constraints. The proposed architecture leverages Laplacian Pyramids to enhance edge recognition, a critical component for effective feature extraction in complex marine environments for disaster response, environmental surveillance, and coastal monitoring. By integrating edge information early in the feature extraction process, LEMMA eliminates the need for computationally expensive feature map computations in deeper network layers, drastically reducing model size, complexity and inference time. LEMMA demonstrates state-of-the-art performance across datasets captured from diverse platforms while reducing trainable parameters and computational requirements by up to 71x, GFLOPs by up to 88.5\%, and inference time by up to 84.65\%, as compared to existing models. Experimental results highlight its effectiveness and real-world applicability, including 93.42\% IoU on the Oil Spill dataset and 98.97\% mIoU on Mastr1325.
Abstract (translated)
海洋环境中的语义分割对于无人水面艇（USV）的自主导航以及海岸带地球观测事件（如油污泄漏）至关重要。然而，现有方法通常依赖深度卷积神经网络（CNN）和基于Transformer的架构，因其高昂的计算成本和资源密集型特性，在部署时面临挑战。这些限制阻碍了其在真实海洋场景中实现实时、低成本应用的实用性。为解决此问题，我们提出了LEMMA——一个专门针对资源受限条件下精确遥感分割而设计的轻量级语义分割模型。该架构利用拉普拉斯金字塔增强边缘识别能力，这是复杂海洋环境中灾难响应、环境监测和海岸监控有效特征提取的关键环节。通过在特征提取早期整合边缘信息，LEMMA消除了深层网络层中对计算量大的特征图运算的需求，显著降低了模型规模、复杂度和推理时间。与现有模型相比，LEMMA在来自不同平台采集的数据集上展现出最先进性能，同时将可训练参数和计算需求最多减少71倍，GFLOPs最多降低88.5%，推理时间最多缩短84.65%。实验结果凸显了其有效性和实际应用价值，在油污数据集上达到93.42%的IoU，在Mastr1325数据集上达到98.97%的mIoU。
URL
https://arxiv.org/abs/2603.25689
