Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review

By Ava Vali, Sara Comai, Matteo Matteucci. Remote Sensing, 2020

398 citations recorded by the source corpus.

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.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/d9331c4fb24f87bd8da6d8dda3df730643f1b9b9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Xiao Qian
2. Tai-Xiang Jiang
3. Xile Zhao
4. Chao Tao

In Media Technology

1. Haifeng Li
2. Qing Zhu
3. Xiao Qian
4. Tai-Xiang Jiang
5. Xile Zhao
6. Chao Tao
7. Simone Scardapane
8. Valerio Marsocci

Citations in the corpus

• Global and Local Contrastive Self-Supervised Learning for Semantic Segmentation of HR Remote Sensing Images (2021, background): “tation technology plays an increasingly important role in global sustainable development, its need for a large number of globalwise, high-quality labeled samples is growing [25], [26].”
• SelfS2: Self-Supervised Transfer Learning for Sentinel-2 Multispectral Image Super-Resolution (2023, background): “classification [4], and change detection [5], [6], [7].”
• FALSE: False Negative Samples Aware Contrastive Learning for Semantic Segmentation of High-Resolution Remote Sensing Image (2022, neutral): “The dependence of this approach on massive, high-quality labeled samples has become a bottleneck for wide-scale application [5]–[7].”