Few-Shot Learning in Remote Sensing: Trends, Gaps, and Future Directions | HackerNoon

Table of Links

1. Abstract and Introduction
2. Backgrounds
3. Type of remote sensing sensor data
4. Benchmark remote sensing datasets for evaluating learning models
5. Evaluation metrics for few-shot remote sensing
6. Recent few-shot learning techniques in remote sensing
7. Few-shot based object detection and segmentation in remote sensing
8. Discussions
9. Numerical experimentation of few-shot classification on UAV-based dataset
10. Explainable AI (XAI) in Remote Sensing
11. Conclusions and Future Directions
12. Acknowledgements, Declarations, and References

8 Discussions

In this section, we aim to highlight interesting observations, common trends, and potential research gaps based on the in-depth analysis of the existing fewshot classification techniques across the three remote sensing data domains. The insights discussed in this section can serve as a guide for both current and future researchers in this field.

• Most of the methods described in the literature use different feature extraction models, with CNN-based models often serving as the backbone, as we’ve already talked about. Convolution-based few-shot learning models are still popular for classification tasks in all three domains. These models are capable of quickly adapting to new classes with few training examples, making them suitable for real-world applications. However, graph-based methods are becoming more popular for classifying SAR images, and they have only recently been used to classify VHR images. Graph-based methods are advantageous because they are able to capture the spatial relationships between objects, which is essential for classifying SAR and VHR images. Recently, vision transformer-based and incremental learning-based methods have emerged as alternatives for hyperspectral image classification. These methods have shown promise in achieving high accuracy with minimal training data, making them attractive for applications where labeled data is limited.

• The evaluation of the discussed works in hyperspectral image classification generally employs three commonly utilized metrics: overall accuracy (OA), average accuracy (AA), and kappa coefficient (κ). These metrics are frequently used to evaluate the classification performance of the proposed algorithms. In contrast, for VHR and SAR-based image classification, the classification accuracy (OA) is often utilized as the primary evaluation metric, although there are a few exceptions. Moreover, in most of the evaluation strategies adopted by the researchers, the proposed algorithms are run multiple times along with the state-of-the-art (SOTA) techniques, and the corresponding mean accuracy and its standard deviation are reported. This approach provides a more reliable and robust estimate of the classification performance, taking into account any potential variations in the results obtained across multiple runs.

• In contrast to hyperspectral classification, it has been observed that there are currently few or no vision ViT-based few-shot classification methods proposed for SAR and VHR images. This could be attributed to the challenges associated with acquiring sufficient datasets for implementing effective and accurate ViT-based architectures for SAR images. Similarly, for VHR images, although there are existing models that use ViT-based classification, they are non-few-shot approaches such as the vanilla ViT-based model proposed by Zhang et al. [3]. Consequently, there are considerable opportunities for researchers to explore the potential of few-shot ViTbased approaches for addressing the challenges associated with VHR remote sensing data classification.

• The current state of research on few-shot classification approaches in the field of remote sensing does not seem to include much work on UAV or low-altitude aircraft-based images, as far as current knowledge suggests. This may be due to the unique nature of such images, which have been pointed out in previous studies such as [95]. The differences in object sizes and perspectives, as well as the limited computational resources available for UAV-based operations, may be contributing factors to the scarcity of research in this area. In addition, the relatively smaller size of the UAV-based datasets may have posed challenges for few-shot learning methods, which often require a sufficiently large dataset to learn meaningful feature representations. However, with the increasing availability of UAV-based data, there may be opportunities for developing novel few-shot classification methods that can effectively leverage such data.

• Furthermore, while few-shot learning has been studied extensively in the context of supervised classification, there is also potential for exploring its application in other remote sensing tasks such as unsupervised or semi-supervised learning, object detection, and semantic segmentation. Few-shot learning can provide an effective means of leveraging limited labeled data in these tasks, which can potentially lead to more accurate and efficient algorithms for remote sensing applications. Overall, while significant progress has been made in the application of few-shot learning to remote sensing data, there are still many research gaps and opportunities for further investigation. The exploration of new few-shot learning approaches, as well as the extension of existing methods to new applications and domains, can lead to more accurate and efficient algorithms for remote sensing tasks.

• The utilization of XAI methodologies in conjunction with few-shot learning models for remote sensing applications can considerably enhance the interpretability of such models, thereby increasing their applicability in domains that are sensitive to potential risks. However, despite the significant promise held by XAI for few-shot learning in remote sensing, the current body of research in this field remains relatively nascent and further endeavors are necessary to fully realize its potential benefits.

8.1 Computational considerations in few-shot learning

Few-shot learning, as a niche within the broader domain of machine learning, warrants unique computational requirements. These requirements become particularly pertinent when the applications have real-time constraints. One of the most critical real-time applications lies in disaster monitoring using UAVs. The immediacy of feedback in such scenarios can drastically affect outcomes, emphasizing the significance of processing time.

Deep learning, which forms the foundation for many few-shot learning techniques, inherently demands high computational resources. Techniques such as

CNNs are notorious for their computational intensity during both the training and inference phases. This computational cost can sometimes be a bottleneck, especially when rapid responses are essential. However, the evolving landscape of few-shot learning has seen the emergence of strategies aiming to mitigate these computational challenges:

• Meta-learning, exemplified by approaches like MAML [125], offers an innovative solution. By optimizing model parameters to allow swift adaptation to novel tasks, these methods significantly reduce the computational overhead. This ensures that models can be fine-tuned efficiently, even when faced with new datasets.

• Wang et al.’s [70] proposition of employing lightweight model architectures coupled with knowledge distillation techniques emerges as another viable strategy. By minimizing redundancies and unnecessary parameters, these models are streamlined to be more computationally efficient without compromising their predictive power.

• Graph-based methodologies, such as GraphSAGE [50], and further extensions into GNN-based approaches [80], provide alternatives to traditional CNNs. These methods, in certain dataset contexts, have demonstrated reduced computational complexity, making them attractive options.

Despite these advancements, it is noteworthy that a significant portion of few-shot learning methodologies has not been explicitly tailored for optimizing processing time. Recognizing this gap, future research could pivot towards crafting architectures specifically designed for real-time UAV applications. Several avenues could be pursued to enhance computational efficiency. These include embracing model compression techniques, such as pruning and quantization [126], leveraging efficient neural architecture search methods [127], and exploring hardware-software co-design strategies [128] to fine-tune models for particular computational platforms. In all these endeavors, the overarching goal remains consistent: achieving rapid inference times without sacrificing model accuracy.