Multimodal Classification of Remote Sensing Images: A Review and Future Directions

By Luis Gómez-Chova, Devis Tuia, Gabriele Moser, Gustau Camps-Valls. 2015

399 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology, Multimodal Learning

How researchers see this paper

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

In Artificial Intelligence

1. Danfeng Hong
2. Jocelyn Chanussot
3. Claudio Persello
4. Lorenzo Bruzzone
5. Devis Tuia
6. Jing Yao
7. Bing Zhang
8. Liangpei Zhang

In Computer Vision and Pattern Recognition

1. Jocelyn Chanussot
2. Gong Cheng
3. Junwei Han
4. Liangpei Zhang
5. Danfeng Hong
6. Xiaoqiang Lu
7. Guisong Xia
8. Gui-Song Xia

In Media Technology

1. Jocelyn Chanussot
2. Danfeng Hong
3. Liangpei Zhang
4. Pedram Ghamisi
5. Antonio J. Plaza
6. Gong Cheng
7. Junwei Han
8. Yushi Chen

In Multimodal Learning

1. Danfeng Hong
2. Jocelyn Chanussot
3. Devis Tuia
4. Xiaoqiang Lu
5. Liangpei Zhang

Citations in the corpus

• Remote Sensing Image Scene Classification: Benchmark and State of the Art (2017, background): “of land use and land cover (LULC) scenes from airborne or space platforms [3].”
• Domain Adaptation for the Classification of Remote Sensing Data: An Overview of Recent Advances (2016, background): “We believe that DA is of the highest importance to future Earth observation since multimodality and repeated imaging have become unavoidable [7].”
• More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing Imagery Classification (2020, background): “The joint exploitation of multiple modalities enables us to characterize the scene at a more detailed and precise level unachievable using single modality data [17].”
• Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities (2020, background): “[2] surveyed multimodal remote sensing image classification and summarized the leading algorithms for this field.”
• Deep Hyperspectral Image Sharpening (2018, neutral): “There is an increasing demand for HSI sharpening due to their use in many applications, such as change detection [11], anomaly detection [12], and objection classification [13].”
• Remote Sensing and Cropping Practices: A Review (2018, background): “However, the joint exploitation of several data sources has led to new data processing issues, such as massive data storage and computationally-intensive processing [212].”
• A review of neural networks in plant disease detection using hyperspectral data (2018, background): “Recent literature [90,156,157] is available on evaluation of deep learning models with digital photography, image analysis and hyperspectral imaging for plant disease detection.”
• Extended Vision Transformer (ExViT) for Land Use and Land Cover Classification: A Multimodal Deep Learning Framework (2023, background): “with a deluge of multisource and multimodal RS data [1].”
• Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks (2016, background): “Very recently, some deep models have been proposed for hyperspectral remote sensing image processing [49].”
• Deep learning in remote sensing: a review (2017, background): “Data fusion is one of the fast-moving areas of remote sensing [114]–[116].”
• SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing Imagery (2022, methodology): “, pixel-level fusion, feature-level fusion, and decisionlevel fusion methods [28].”
• Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review (2022, methodology): “In this paper, we attempted to give a detailed definition on basis of previous works (Gómez-Chova et al., 2015; Dalla Mura et al., 2015).”