Panoptic segmentation is a recent and powerful task that tackles individual object recognition (“things”) and multiple backgrounds (“stuff”) simultaneously. Remote sensing studies with panoptic segmentation are still restricted and recent, with great application perspectives. In this sense, we propose the first multispectral panoptic segmentation study, considering the “thing” and “stuff” classes in the beach scenario and evaluating different sets of spectral bands. Our methodology included developing a dataset with 3800 (3200 for training, 300 for validation, and 300 for testing) with 128 × 128 spatial dimensions and eight spectral bands considering fourteen classes (6 “thing” and 8 “stuff” classes). We used WorldView-3 images from Praia do Futuro, Fortaleza, and pan-sharpening to improve spatial resolution. Five different spectral band configurations were considered: (1) all eight bands, (2) $RGB+NIR1+NIR2$, (3) $RGB+NIR1$, (4) $RGB+NIR2$, and (5) only RGB. The model training used the Panoptic-FPN architecture with the same hyperparameter settings considering three backbones (ResNeXt-101, ResNet-101, and ResNet-50). The best result considered the ResNeXt-101 with all spectral bands. However, the results from the first four configurations were very similar, and the RGB alone was the only configuration with significantly lower results. We also evaluated 15 semantic segmentation models for a benchmark comparison for the Beach Dataset. We show in visual results that even though the semantic models may be precise, they fail at identifying unique targets, especially in crowded locations such as the beach. The panoptic segmentation allowed a necessary detailing and counting of tourist infrastructures and mapping of other background features, establishing an essential tool for inspecting beach areas.

全景分割是一项近期且强大的任务，它同时处理单个对象识别（“事物”）和多个背景（“事物”）。全景分割的遥感研究仍然受到限制和最近，具有很大的应用前景。从这个意义上说，我们提出了第一个多光谱全景分割研究，考虑了海滩场景中的“事物”和“东西”类别，并评估了不同的光谱带集。我们的方法包括开发一个包含 3800 个数据集（3200 个用于训练，300 个用于验证，300 个用于测试），具有 128 × 128 的空间维度和 8 个光谱带，考虑 14 个类别（6 个“事物”和 8 个“东西”类别）。我们使用来自 Praia do Futuro、Fortaleza 和全色锐化的 WorldView-3 图像来提高空间分辨率。$RG乙+ñ我R1+ñ我R2$, (3)$RG乙+ñ我R1$, (4)$RG乙+ñ我R2$, 和 (5) 只有 RGB。模型训练使用具有相同超参数设置的 Panoptic-FPN 架构，考虑了三个主干（ResNeXt-101、ResNet-101 和 ResNet-50）。最好的结果考虑了具有所有光谱带的 ResNeXt-101。然而，前四种配置的结果非常相似，只有 RGB 是唯一一个结果显着降低的配置。我们还评估了 15 个语义分割模型，用于海滩数据集的基准比较。我们在视觉结果中表明，即使语义模型可能是精确的，它们也无法识别独特的目标，尤其是在海滩等拥挤的地方。全景分割允许对旅游基础设施进行必要的详细说明和计数以及其他背景特征的映射，