Deep learning in remote sensing: a review (1710.03959v1)

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.

Deep Learning in Remote Sensing: A Review

The paper "Deep Learning in Remote Sensing: A Review" by Xiao Xiang Zhu et al. provides a comprehensive analysis of the applications and challenges of deploying deep learning techniques in the field of remote sensing. This review is timely given the increased availability of large-scale remote sensing data and the computational breakthroughs that have enabled deep learning techniques to excel in fields such as computer vision and natural language processing.

Key Insights and Challenges

The application of deep learning to remote sensing is driven by the need to address complex, data-intensive tasks such as climate change monitoring, urbanization studies, and resource management. These tasks often involve analyzing multi-modal data from various sensors, such as optical, hyperspectral, and synthetic aperture radar (SAR) sensors. The authors categorize the challenges of employing deep learning in remote sensing into six major areas:

1. Multi-modality: Remote sensing data often come from different types of sensors with varying geometries and content, making data fusion a challenging but necessary step.
2. Geo-location: The geographical nature of remote sensing data necessitates integrating pixel information with other geospatial data sources.
3. Quality Control: Remote sensing data include controlled measurements, and incorporating prior knowledge of sensor adequacy can enhance analysis accuracy.
4. Temporal Dynamics: Continuous data acquisition programs like the Copernicus Program make time-series analysis crucial for extracting meaningful trends.
5. Big Data: The sheer volume of remote sensing data necessitates fast algorithms that can scale globally.
6. Retrieval of Physical Quantities: Unlike many computer vision tasks, remote sensing often requires the retrieval of geo-physical or bio-chemical quantities, necessitating domain-specific adaptations of deep learning models.

Deep Learning Architectures in Remote Sensing

The paper details the evolution and adaptation of various deep learning architectures for remote sensing tasks:

• Autoencoders and Variants: Techniques such as sparse autoencoders and deep belief networks have been utilized for feature extraction from hyperspectral data.
• Convolutional Neural Networks (CNNs): CNNs have been extensively employed for spatial feature extraction, particularly in tasks such as land cover classification, object detection, and scene classification.
• Recurrent Neural Networks (RNNs): For tasks involving sequential data such as time-series analysis, RNNs and their variants like LSTM have shown promise.

Applications in Remote Sensing

Hyperspectral Image Analysis

The advancements in hyperspectral sensors have facilitated detailed spectroscopic analysis of Earth's surface, crucial for applications like land cover classification and anomaly detection. Deep learning techniques have significantly improved the accuracy and efficiency of these tasks:

• Classification: Both supervised (using CNNs) and unsupervised (using autoencoders and RNNs) approaches have shown promising results.
• Change Detection: Deep learning models, particularly RNNs, have shown potential in capturing temporal dynamics in multi-temporal image sequences.
• Anomaly Detection: Initial attempts using CNNs have been promising in detecting spectral anomalies.

SAR Image Interpretation

SAR data offers unique challenges due to its coherent imaging technique. Nevertheless, deep learning has made significant strides:

• Automatic Target Recognition (ATR): CNN-based architectures have achieved high accuracy in recognizing military targets and other objects in SAR imagery.
• Terrain Classification: Both coarse (using features like CNNs) and fine-grained classification tasks (using complex-valued neural networks) have benefited from deep learning.

3D Reconstruction and Image Fusion

The integration of deep learning in 3D reconstruction and image fusion tasks has been transformative:

• Pansharpening and Super-Resolution: CNNs have been applied to enhance the spatial resolution of multispectral images.
• Semantic 3D Reconstruction: Combining semantic information with 3D data generation has allowed for more detailed and contextually relevant 3D models.

Implications and Future Directions

The deployment of deep learning in remote sensing holds numerous practical and theoretical implications:

• Scalability: Deep learning algorithms must be scalable to handle global datasets efficiently.
• Transferability: Models need to generalize well across different geographical regions.
• Data Labeling: The scarcity of labeled remote sensing data necessitates semi-supervised or unsupervised learning methods to utilize the vast amounts of unlabeled data.
• Fusion with Domain Knowledge: Integrating physical models within deep learning frameworks can provide hybrid models that leverage the strengths of both approaches.
• Advances in Computational Power: The increasing availability of high-performance computing resources facilitates the training of large, complex deep learning models.

Conclusion

The paper underscores the transformative potential of deep learning in remote sensing but also highlights the numerous challenges and areas requiring further research. The advancement of deep learning techniques tailored specifically for remote sensing data is critical for unlocking the full potential of remote sensing applications in monitoring and managing Earth's resources. As deep learning frameworks and computational resources continue to evolve, the integration of domain-specific knowledge will be pivotal in addressing the unique challenges posed by remote sensing data.