Title: RSI-CB: A Large Scale Remote Sensing Image Classification Benchmark via Crowdsource Data,
Abstract: Remote sensing image classification is a fundamental task in remote sensing
image processing. Remote sensing field still lacks of such a large-scale
benchmark compared to ImageNet, Place2. We propose a remote sensing image
classification benchmark (RSI-CB) based on crowd-source data which is massive,
scalable, and diversity. Using crowdsource data, we can efficiently annotate
ground objects in remotes sensing image by point of interests, vectors data
from OSM or other crowd-source data. Based on this method, we construct a
worldwide large-scale benchmark for remote sensing image classification. In
this benchmark, there are two sub datasets with 256 * 256 and 128 * 128 size
respectively since different convolution neural networks requirement different
image size. The former sub dataset contains 6 categories with 35 subclasses
with total of more than 24,000 images; the later one contains 6 categories with
45 subclasses with total of more than 36,000 images. The six categories are
agricultural land, construction land and facilities, transportation and
facilities, water and water conservancy facilities, woodland and other land,
and each category has several subclasses. This classification system is defined
according to the national standard of land use classification in China, and is
inspired by the hierarchy mechanism of ImageNet. Finally, we have done a large
number of experiments to compare RSI-CB with SAT-4, UC-Merced datasets on
handcrafted features, such as such as SIFT, and classical CNN models, such as
AlexNet, VGG, GoogleNet, and ResNet. We also show CNN models trained by RSI-CB
have good performance when transfer to other dataset, i.e. UC-Merced, and good
generalization ability. The experiments show that RSI-CB is more suitable as a
benchmark for remote sensing image classification task than other ones in big
data era, and can be potentially used in practical applications.