2017/05/30 by Haifeng Li, Xin Dou, Li, Haifeng +13 · 80 citations
Computer Science · Engineering · #Advanced Image and Video Retrieval Techniques #Artificial intelligence #Automated Road and Building Extraction #Benchmark (surveying) #Cartography #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Contextual image classification #Convolutional neural network #Data mining #Database #FOS: Computer and information sciences #Histogram #Image (mathematics) #Pattern recognition (psychology) #Remote sensing #Remote-Sensing Image Classification #Scalability #Scale (ratio) #cs.CV
paper · pdf · doi:10.48550/arxiv.1705.10450
published in arXiv (Cornell University) (Cornell University) · 41 pages, 19 figures, 7 tables
openalex publication_date 2017/05/30 · openalex created_date 2017/06/05 · arxiv created 2020/01/10 · arxiv updated 2020/01/13 · openalex updated_date 2026/07/28
In recent years, deep convolutional neural network (DCNN) has seen a breakthrough progress in natural image recognition because of three points: universal approximation ability via DCNN, large-scale database (such as ImageNet), and supercomputing ability powered by GPU. The remote sensing field is still lacking a large-scale benchmark compared to ImageNet and Place2. In this paper, we propose a remote sensing image classification benchmark (RSI-CB) based on massive, scalable, and diverse crowdsource data. Using crowdsource data, such as Open Street Map (OSM) data, ground objects in remote sensing images can be annotated effectively by points of interest, vector data from OSM, or other crowdsource data. The annotated images can 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 two sub-datasets with 256 by 256 and 128 by 128 sizes because different DCNNs require different image sizes. The former contains 6 categories with 35 subclasses of more than 24,000 images. The latter contains 6 categories with 45 subclasses of more than 36,000 images. 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 many experiments to compare RSI-CB with the SAT-4, SAT-6, and UC-Merced datasets on handcrafted features, such as scale-invariant feature transform, color histogram, local binary patterns, and GIST, and classical DCNN models, such as AlexNet, VGGNet, GoogLeNet, and ResNet.