Title: Unsupervised Multimodal Change Detection: Leveraging SAM for Comprehensive Earth Monitoring

by | Jan 18, 2024 | Computer Science

Unsupervised multimodal change detection is pivotal for time-sensitive tasks
and comprehensive multi-temporal Earth monitoring. In this study, we explore
unsupervised multimodal change detection between two key remote sensing data
sources: optical high-resolution imagery and OpenStreetMap (OSM) data.
Specifically, we propose to utilize the vision foundation model Segmentation
Anything Model (SAM), for addressing our task. Leveraging SAM’s exceptional
zero-shot transfer capability, high-quality segmentation maps of optical images
can be obtained. Thus, we can directly compare these two heterogeneous data
forms in the so-called segmentation domain. We then introduce two strategies
for guiding SAM’s segmentation process: the ‘no-prompt’ and ‘box/mask prompt’
methods. The two strategies are designed to detect land-cover changes in
general scenarios and to identify new land-cover objects within existing
backgrounds, respectively. Experimental results on three datasets indicate that
the proposed approach can achieve more competitive results compared to
representative unsupervised multimodal change detection methods.

Unsupervised multimodal change detection plays a crucial role in time-sensitive tasks and comprehensive multi-temporal Earth monitoring. This study focuses on exploring unsupervised multimodal change detection between optical high-resolution imagery and OpenStreetMap (OSM) data. The goal is to leverage the Segmentation Anything Model (SAM), a vision foundation model, for this purpose.

SAM is known for its exceptional zero-shot transfer capability, which allows obtaining high-quality segmentation maps of optical images. By utilizing SAM, the study enables a direct comparison between the two different types of data in the segmentation domain. This approach opens up new possibilities for analyzing and detecting changes in the landscape through the integration of multiple data sources.

To guide SAM’s segmentation process, two strategies are introduced: the ‘no-prompt’ and ‘box/mask prompt’ methods. The ‘no-prompt’ method aims to detect land-cover changes in general scenarios, while the ‘box/mask prompt’ method focuses on identifying new land-cover objects within existing backgrounds. These strategies enhance the capability of SAM to accurately identify and classify various changes in the Earth’s surface.

Experimental results on three datasets validate the effectiveness of the proposed approach compared to representative unsupervised multimodal change detection methods. The ability to achieve more competitive results underscores the potential of this research in advancing the field of multimedia information systems and its interdisciplinary nature.

This study has significant implications for various fields such as remote sensing, geospatial analysis, and environmental monitoring. By combining optical imagery with OSM data, researchers and professionals can gain a deeper understanding of land-cover changes and their impact on the environment. The integration of different data sources also promotes a more comprehensive analysis of various land-use patterns and urban development.

Moreover, this research aligns with the broader field of animations, artificial reality, augmented reality, and virtual realities. The ability to detect and analyze changes in the Earth’s surface is crucial in creating realistic and immersive virtual environments. These technologies heavily rely on accurate representations of the real world, and unsupervised multimodal change detection contributes to enhancing the fidelity of these virtual realities.

In conclusion, this study showcases the importance of unsupervised multimodal change detection and its potential in various fields. By leveraging the SAM model and integrating optical high-resolution imagery with OSM data, researchers can achieve more accurate and comprehensive analysis of land-cover changes. This research not only advances the field of multimedia information systems but also has implications for animations, artificial reality, augmented reality, and virtual realities. The multi-disciplinary nature of this study highlights the significance of collaboration between different fields to tackle complex tasks in a rapidly changing world.
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“Time Travelling Pixels (TTP): A Novel Approach for High-Precision Change Detection in Remote

“Time Travelling Pixels (TTP): A Novel Approach for High-Precision Change Detection in Remote

by | Dec 30, 2023 | Computer Science

Introducing Time Travelling Pixels (TTP): A Novel Approach to High-Precision Change Detection in Remote Sensing

Change detection plays a crucial role in observing and analyzing surface transformations in remote sensing. While deep learning-based methods have advanced the field significantly, accurately detecting changes in complex spatio-temporal scenarios remains a challenge. To address this, researchers have turned to foundation models with their exceptional versatility and generalization capabilities. However, there is still a gap to be bridged in terms of data and tasks. In this paper, we propose Time Travelling Pixels (TTP), a groundbreaking approach that incorporates the latent knowledge of the SAM foundation model into change detection. This method effectively tackles the domain shift in knowledge transfer and overcomes the challenge of expressing both homogeneous and heterogeneous characteristics of multi-temporal images. The exceptional results achieved on the LEVIR-CD dataset testify to the effectiveness of TTP. You can access the code for TTP at this URL.

Abstract:Change detection, a prominent research area in remote sensing, is pivotal in observing and analyzing surface transformations. Despite significant advancements achieved through deep learning-based methods, executing high-precision change detection in spatio-temporally complex remote sensing scenarios still presents a substantial challenge. The recent emergence of foundation models, with their powerful universality and generalization capabilities, offers potential solutions. However, bridging the gap of data and tasks remains a significant obstacle. In this paper, we introduce Time Travelling Pixels (TTP), a novel approach that integrates the latent knowledge of the SAM foundation model into change detection. This method effectively addresses the domain shift in general knowledge transfer and the challenge of expressing homogeneous and heterogeneous characteristics of multi-temporal images. The state-of-the-art results obtained on the LEVIR-CD underscore the efficacy of the TTP. The Code is available at url{this https URL}.

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