Masked Autoencoders for Sensor-Agnostic Remote Sensing Image Retrieval

Основные понятия

Masked autoencoders can be effectively adapted to model inter-modal and intra-modal characteristics of multi-sensor remote sensing image archives for sensor-agnostic image retrieval.

Аннотация

The paper explores the effectiveness of masked autoencoders (MAEs) for sensor-agnostic remote sensing (RS) image retrieval, which aims to search for semantically similar images across different sensor modalities.
Key highlights:

Presents a systematic overview on the possible adaptations of the vanilla MAE to exploit masked image modeling on multi-sensor RS image archives, denoted as cross-sensor masked autoencoders (CSMAEs).
Introduces different CSMAE models based on architectural adjustments, adaptations on image masking, and reformulation of masked image modeling.
Provides extensive experimental analysis of the CSMAE models, including sensitivity analysis, ablation study, and comparison with other approaches.
Derives guidelines to utilize masked image modeling for both uni-modal and cross-modal RS image retrieval problems.
The core idea is to adapt MAEs to simultaneously model inter-modal and intra-modal image characteristics on multi-modal RS image archives. This is achieved by incorporating cross-modal reconstruction objectives in addition to the standard uni-modal reconstruction objectives in MAEs. The authors explore different design choices for the CSMAE models, such as the use of sensor-common or sensor-specific encoders/decoders, various multi-modal masking correspondences, and inclusion of inter-modal latent similarity preservation. The experimental results demonstrate the effectiveness of the proposed CSMAE models for sensor-agnostic RS image retrieval tasks.

Статистика

"Increasing the number of heads and the considered embedding dimension by using ViT-B12 instead of ViT-Ti12 or ViT-S12 leads to higher score of CSMAE-CECD on each retrieval task."
"Decreasing the cross-sensor encoder depth (i.e., increasing the depth of specific encoders) in CSMAE-SECD leads to higher F1 scores on all the retrieval tasks at the cost of an increase in the number of model parameters."
"When LMIM is employed for inter-modal latent similarity preservation, using the temperature τ value of 0.5 leads to the highest results compared to the other values τ independently of the utilized feature vector type."

Цитаты

"Masked autoencoders (MAEs) have recently attracted great attention for remote sensing (RS) image representation learning, and thus embody a significant potential for content-based image retrieval (CBIR) from ever-growing RS image archives."
"The effectiveness of MAEs for cross-sensor CBIR, which aims to search semantically similar images across different image modalities, has not been explored yet."
"The core idea is to adapt MAEs to simultaneously model inter-modal and intra-modal image characteristics on multi-modal RS image archives."

Ключевые выводы из

Exploring Masked Autoencoders for Sensor-Agnostic Image Retrieval in Remote Sensing

by Jako... в arxiv.org 04-12-2024

https://arxiv.org/pdf/2401.07782.pdf

Дополнительные вопросы

How can the proposed CSMAE models be extended to leverage temporal information of multi-sensor remote sensing image archives for sensor-agnostic image retrieval

To leverage temporal information of multi-sensor remote sensing image archives for sensor-agnostic image retrieval, the proposed CSMAE models can be extended by incorporating temporal embeddings into the masked image modeling process. By including temporal information, the models can learn to capture the temporal variations in the images acquired by different sensors over time. This can enhance the representation learning process and enable the models to better understand the evolution of the landscape or scene being captured. Additionally, the CSMAE models can be adapted to consider the sequential nature of the images in the multi-sensor archives, allowing them to encode and decode image sequences for more comprehensive image retrieval tasks.

What are the potential challenges and limitations of the CSMAE models in handling significant domain shifts between different sensor modalities

One potential challenge of the CSMAE models in handling significant domain shifts between different sensor modalities is the need for robust feature extraction and representation learning. When there are substantial differences in the characteristics of images captured by different sensors, the models may struggle to effectively capture the shared semantic content across modalities. Domain adaptation techniques may need to be incorporated into the CSMAE framework to align the feature spaces of different sensor modalities and mitigate the effects of domain shifts. Additionally, the models may face limitations in generalizing to unseen sensor modalities or extreme domain variations, requiring careful calibration and adaptation strategies to maintain performance across diverse sensor types.

Can the CSMAE framework be adapted to enable zero-shot or few-shot cross-sensor image retrieval scenarios where labeled training data is scarce

The CSMAE framework can be adapted to enable zero-shot or few-shot cross-sensor image retrieval scenarios where labeled training data is scarce by incorporating transfer learning and meta-learning techniques. In a zero-shot setting, where no labeled data from the target sensor modality is available, the models can leverage pre-trained representations from source sensor modalities and adapt them to the target modality through domain adaptation or few-shot learning. By fine-tuning the models on a small amount of labeled data from the target sensor, the CSMAE framework can learn to generalize and retrieve images across different sensor modalities with limited supervision. Meta-learning approaches can also be employed to enable rapid adaptation to new sensor modalities with minimal labeled data, enhancing the models' ability to perform effectively in zero-shot or few-shot scenarios.