RSI-CB: A Large Scale Remote Sensing Image Classification Benchmark via Crowdsource Data

TL;DR

This paper introduces RSI-CB, a large-scale remote sensing image classification benchmark created using crowdsource data like Open Street Map, enabling effective annotation and comparison of various models on diverse datasets.

Contribution

The paper presents a novel large-scale remote sensing benchmark based on crowdsource data, with two datasets of different sizes and a hierarchical classification system inspired by ImageNet.

Findings

RSI-CB outperforms existing datasets like SAT-4, SAT-6, and UC-Merced in classification tasks.

Deep CNN models achieve higher accuracy on RSI-CB compared to handcrafted features.

The benchmark facilitates large-scale, diverse remote sensing image classification research.

Abstract

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.