School of Geosciences and Info-Physics, Central South University, Changsha 410083, China.
Sensors (Basel). 2020 Mar 12;20(6):1594. doi: 10.3390/s20061594.
Image classification is a fundamental task in remote sensing image processing. In recent years, deep convolutional neural networks (DCNNs) have experienced significant breakthroughs in natural image recognition. The remote sensing field, however, is still lacking a large-scale benchmark similar to ImageNet. In this paper, we propose a remote sensing image classification benchmark (RSI-CB) based on massive, scalable, and diverse crowdsourced data. Using crowdsourced data, such as Open Street Map (OSM) data, ground objects in remote sensing images can be annotated effectively using points of interest, vector data from OSM, or other crowdsourced data. These annotated images can, then, be used in remote sensing image classification tasks. Based on this method, we construct a worldwide large-scale benchmark for remote sensing image classification. This benchmark has large-scale geographical distribution and large total image number. It contains six categories with 35 sub-classes of more than 24,000 images of size 256 × 256 pixels. This classification system of ground objects is defined according to the national standard of land-use classification in China and is inspired by the hierarchy mechanism of ImageNet. Finally, we conduct numerous experiments to compare RSI-CB with the SAT-4, SAT-6, and UC-Merced data sets. The experiments show that RSI-CB is more suitable as a benchmark for remote sensing image classification tasks than other benchmarks in the big data era and has many potential applications.
图像分类是遥感图像处理中的一项基本任务。近年来，深度卷积神经网络（DCNN）在自然图像识别方面取得了重大突破。然而，遥感领域仍然缺乏类似于 ImageNet 的大规模基准。在本文中，我们提出了一种基于大规模、可扩展和多样化众包数据的遥感图像分类基准（RSI-CB）。使用众包数据，如 Open Street Map（OSM）数据，可以使用兴趣点、OSM 的矢量数据或其他众包数据有效地对遥感图像中的地物进行标注。这些标注图像可以用于遥感图像分类任务。基于这种方法，我们构建了一个全球范围内的遥感图像分类基准。该基准具有大规模的地理分布和大量的总图像数量。它包含六个类别，35 个子类别，超过 24,000 张 256×256 像素的图像。这个地物分类系统是根据中国的土地利用分类国家标准定义的，灵感来自于 ImageNet 的层次机制。最后，我们进行了大量实验，将 RSI-CB 与 SAT-4、SAT-6 和 UC-Merced 数据集进行了比较。实验表明，RSI-CB 比大数据时代的其他基准更适合作为遥感图像分类任务的基准，具有许多潜在的应用。
