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RSI-CB: A Large-Scale Remote Sensing Image Classification Benchmark Using Crowdsourced Data
- Source :
- Sensors (Basel, Switzerland), Sensors, Volume 20, Issue 6, Sensors, Vol 20, Iss 6, p 1594 (2020)
- Publication Year :
- 2020
- Publisher :
- MDPI, 2020.
-
Abstract
- 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 &times<br />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.
- Subjects :
- Computer science
deep convolution neural network
crowdsourced data
Big data
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
0211 other engineering and technologies
02 engineering and technology
lcsh:Chemical technology
Biochemistry
Convolutional neural network
Field (computer science)
Article
Analytical Chemistry
benchmark
0202 electrical engineering, electronic engineering, information engineering
remote sensing image classification
lcsh:TP1-1185
Electrical and Electronic Engineering
Instrumentation
021101 geological & geomatics engineering
Remote sensing
Pixel
Contextual image classification
business.industry
Atomic and Molecular Physics, and Optics
Remote sensing (archaeology)
Benchmark (computing)
020201 artificial intelligence & image processing
business
Subjects
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 20
- Issue :
- 6
- Database :
- OpenAIRE
- Journal :
- Sensors (Basel, Switzerland)
- Accession number :
- edsair.doi.dedup.....8748daf80711883dbb4f07b905aca0e6