Deep learning in remote sensing: a review

Abstract

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.

Accepted for publication IEEE Geoscience and Remote Sensing Magazine

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Cited by in corpus (6)

• Deep Learning for Hyperspectral Image Classification: An Overview
• Deep Learning for Classification of Hyperspectral Data: A Comparative Review
• Classification of Hyperspectral and LiDAR Data Using Coupled CNNs
• Understanding urban landuse from the above and ground perspectives: a deep learning, multimodal solution
• Correcting rural building annotations in OpenStreetMap using convolutional neural networks
• Mapping Saturn using deep learning