Remote Sensing Image Scene Classification: Benchmark and State of the Art

TL;DR

This paper reviews recent progress in remote sensing image scene classification, introduces a large-scale diverse dataset called NWPU-RESISC45, and evaluates several methods to establish benchmarks for future research.

Contribution

It provides a comprehensive review, introduces a new large-scale dataset with high diversity, and offers baseline evaluations for remote sensing scene classification.

Findings

The NWPU-RESISC45 dataset contains 31,500 images across 45 classes.

The dataset exhibits high variation in image conditions and class similarity.

Baseline method evaluations establish reference performance levels.

Abstract

Remote sensing image scene classification plays an important role in a wide range of applications and hence has been receiving remarkable attention. During the past years, significant efforts have been made to develop various datasets or present a variety of approaches for scene classification from remote sensing images. However, a systematic review of the literature concerning datasets and methods for scene classification is still lacking. In addition, almost all existing datasets have a number of limitations, including the small scale of scene classes and the image numbers, the lack of image variations and diversity, and the saturation of accuracy. These limitations severely limit the development of new approaches especially deep learning-based methods. This paper first provides a comprehensive review of the recent progress. Then, we propose a large-scale dataset, termed "NWPU-RESISC45", which is a publicly available benchmark for REmote Sensing Image Scene Classification (RESISC), created by Northwestern Polytechnical University (NWPU). This dataset contains 31,500 images, covering 45 scene classes with 700 images in each class. The proposed NWPU-RESISC45 (i) is large-scale on the scene classes and the total image number, (ii) holds big variations in translation, spatial resolution, viewpoint, object pose, illumination, background, and occlusion, and (iii) has high within-class diversity and between-class similarity. The creation of this dataset will enable the community to develop and evaluate various data-driven algorithms. Finally, several representative methods are evaluated using the proposed dataset and the results are reported as a useful baseline for future research.