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Deep Learning for Land Use and Land Cover Classification based on Hyperspectral and Multispectral Earth Observation Data: A Review
| Content Provider | MDPI |
|---|---|
| Author | Vali, Ava Comai, Sara Matteucci, Matteo |
| Copyright Year | 2020 |
| Description | Lately, with deep learning outpacing the other machine learning techniques in classifying images, we have witnessed a growing interest of the remote sensing community in employing these techniques for the land use and land cover classification based on multispectral and hyperspectral images; the number of related publications almost doubling each year since 2015 is an attest to that. The advances in remote sensing technologies, hence the fast-growing volume of timely data available at the global scale, offer new opportunities for a variety of applications. Deep learning being significantly successful in dealing with Big Data, seems to be a great candidate for exploiting the potentials of such complex massive data. However, there are some challenges related to the ground-truth, resolution, and the nature of data that strongly impact the performance of classification. In this paper, we review the use of deep learning in land use and land cover classification based on multispectral and hyperspectral images and we introduce the available data sources and datasets used by literature studies; we provide the readers with a framework to interpret the-state-of-the-art of deep learning in this context and offer a platform to approach methodologies, data, and challenges of the field. |
| Starting Page | 2495 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs12152495 |
| Journal | Remote Sensing |
| Issue Number | 15 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-08-03 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Remote Sensing Data Hyperspectral Data Multispectral Data Lulc Classification Machine Learning Deep Learning Convolutional Neural Networks End-to-end Learning Feature Engineering Ground-truth Scarcity Data Fusion |
| Content Type | Text |
| Resource Type | Article |
