The availability of massive earth observing satellite data provide huge opportunities for land use and land cover mapping. However, such mapping effort is challenging due to the existence of various land cover classes, noisy data, and the lack of proper labels. Also, each land cover class typically has its own unique temporal pattern and can be identified only during certain periods. In this article, we introduce a novel architecture that incorporates the UNet structure with Bidirectional LSTM and Attention mechanism to jointly exploit the spatial and temporal nature of satellite data and to better identify the unique temporal patterns of each land cover. We evaluate this method for mapping crops in multiple regions over the world. We compare our method with other state-of-the-art methods both quantitatively and qualitatively on two real-world datasets which involve multiple land cover classes. We also visualise the attention weights to study its effectiveness in mitigating noise and identifying discriminative time period.
翻译:大规模地球观测卫星数据的可用性为土地利用和土地覆盖物绘图提供了巨大机会,然而,由于存在各种土地覆盖物类别、数据噪音和缺乏适当标签,这种测绘工作具有挑战性。此外,每个土地覆盖物类别通常都有自己独特的时间模式,只能在特定时期才能确定。在本篇文章中,我们引入了一个新的结构,将UNet结构与双向LSTM和注意机制结合起来,共同利用卫星数据的空间和时间性质,更好地确定每个土地覆盖物的独特时间模式。我们评估了绘制世界各地作物分布图的这一方法。我们将我们的方法与其他最先进的方法进行定量和定性的比较,在涉及多个土地覆盖层的两个真实世界数据集中进行这种比较。我们还将注意力放在研究其在减少噪音和确定歧视性时间期方面的有效性。