Abstract
Remote sensing image scene classification, which aims at labeling remote sensing images with a set of semantic categories based on their contents, has broad applications in a range of fields. Propelled by the powerful feature learning capabilities of deep neural networks, remote sensing image scene classification driven by deep learning has drawn remarkable attention and achieved significant breakthroughs. However, to the best of our knowledge, a comprehensive review of recent achievements regarding deep learning for scene classification of remote sensing images is still lacking. Considering the rapid evolution of this field, this paper provides a systematic survey of deep learning methods for remote sensing image scene classification by covering more than 160 papers. To be specific, we discuss the main challenges of remote sensing image scene classification and survey (1) Autoencoder-based remote sensing image scene classification methods, (2) Convolutional Neural Network-based remote sensing image scene classification methods, and (3) Generative Adversarial Network-based remote sensing image scene classification methods. In addition, we introduce the benchmarks used for remote sensing image scene classification and summarize the performance of more than two dozen of representative algorithms on three commonly-used benchmark data sets. Finally, we discuss the promising opportunities for further research.
Highlights
R EMOTE sensing images, a valuable data source for earth observation, can help us to measure and observe detailedManuscript received April 1, 2020; revised May 30, 2020 and June 18, 2020; accepted June 24, 2020
For the last few decades, extensive research works on remote sensing image scene classification have been undertaken driven by its real-world applications, such as urban planning [5], [6], natural hazards detection [7]–[9], environment monitoring [10]–[12], vegetation mapping [13], [14], and geospatial object detection [15]–[22]
A thorough survey of deep learning for scene classification is still lacking. This motivates us to deeply analyze the main challenges faced for remote sensing image scene classification, systematically review those deep learning-based scene classification approaches, most of which are published during the last five years, introduce the mainstream scene classification benchmarks, and discuss several promising future directions of scene classification
Summary
R EMOTE sensing images, a valuable data source for earth observation, can help us to measure and observe detailed. CHENG et al.: REMOTE SENSING IMAGE SCENE CLASSIFICATION MEETS DEEP LEARNING a large-scale remote sensing image that contains clear semantic information on the earth surface [37], [38]. Li et al [75] surveyed the pixel-level, subpixel-level, and object-based methods of image classification and emphasized the contribution of spatio-contextual information to remote sensing image scene classification. A thorough survey of deep learning for scene classification is still lacking This motivates us to deeply analyze the main challenges faced for remote sensing image scene classification, systematically review those deep learning-based scene classification approaches, most of which are published during the last five years, introduce the mainstream scene classification benchmarks, and discuss several promising future directions of scene classification.
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