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Opening the Black-Box: A Systematic Review on Explainable AI in Remote Sensing

Published 21 Feb 2024 in cs.LG | (2402.13791v2)

Abstract: In recent years, black-box machine learning approaches have become a dominant modeling paradigm for knowledge extraction in remote sensing. Despite the potential benefits of uncovering the inner workings of these models with explainable AI, a comprehensive overview summarizing the explainable AI methods used and their objectives, findings, and challenges in remote sensing applications is still missing. In this paper, we address this gap by performing a systematic review to identify the key trends in the field and shed light on novel explainable AI approaches and emerging directions that tackle specific remote sensing challenges. We also reveal the common patterns of explanation interpretation, discuss the extracted scientific insights, and reflect on the approaches used for the evaluation of explainable AI methods. As such, our review provides a complete summary of the state-of-the-art of explainable AI in remote sensing. Further, we give a detailed outlook on the challenges and promising research directions, representing a basis for novel methodological development and a useful starting point for new researchers in the field.

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Summary

  • The paper identifies a growing trend in applying xAI methods, notably SHAP and CAM, to enhance transparency in remote sensing applications.
  • The paper highlights how adaptations in xAI techniques address RS-specific challenges such as scale, spectral properties, and temporal dependencies.
  • The paper calls for standardized, quantitative evaluation frameworks to improve trust and validate xAI methods in ML-driven Earth Observation.

Opening the Black-Box: A Systematic Review on Explainable AI in Remote Sensing

Introduction to the Study

Recent advancements have witnessed a surge in applying ML methods, notably black-box models, across various Earth Observation (EO) tasks. These models, while powerful, lack inherent transparency, making it difficult to interpret or trust their decisions fully. This gap underscores the need for explainable AI (xAI) methods within the remote sensing (RS) domain, which this paper addresses through a comprehensive systematic review.

Key Findings and Methodological Insights

The analysis reveals a sharp increase in xAI application within RS, focusing on method utilization, development trends, and practical implications. Interestingly, the review identifies a significant increase in publications related to xAI in RS, indicating growing interest and application in this field. Among the xAI methods applied, SHapley Additive exPlanations (SHAP) stands out for its frequent use, attributed to its model-agnostic nature and ability to provide local and global explanations.

One pivotal finding is the adoption of model-specific xAI methods, particularly Class Activation Mapping (CAM) variants, which have seen tailored modifications to address unique RS challenges such as scale, topology, spectral properties, and temporal dependencies. These adaptations highlight the need for xAI methods that account for the characteristic complexities of RS data.

Challenges in Applying xAI to RS

Critical challenges remain in fully integrating xAI within RS, particularly concerning data properties unique to RS, such as scale and temporal dependencies. These challenges underscore the limitations of current xAI methods, primarily designed for natural images and not directly transferable to RS imagery. Furthermore, the review discloses a gap in methodological developments for handling the temporal and spectral richness of RS data.

Evaluation and Validation of xAI Methods

A noteworthy aspect of existing literature is the reliance on anecdotal evidence for valuating xAI outcomes, lacking a standardized and robust evaluation framework. This review calls attention to the necessity for quantitative metrics and user studies to validate and compare xAI methods objectively. Hence, fostering the development of such evaluation frameworks emerges as a critical need to move the field forward.

Practical Implications and Future Directions

This review illuminates the practical value of xAI in RS, extending beyond model interpretation to include enhancing model reliability, facilitating scientific discovery, and ensuring compliance with regulatory standards. Furthermore, it signals promising research directions, particularly in developing xAI approaches tailored to the RS domain, standardizing evaluation methodologies, and integrating xAI with physics-aware ML and uncertainty quantification.

Concluding Remarks

In sum, this study provides a foundational overview of the state-of-the-art in xAI application within the RS domain. By highlighting key trends, challenges, and potential research directions, it sets the stage for future methodological advancements that could significantly impact how RS data is analyzed and interpreted. Bridging the identified gaps and developing xAI methods attuned to the nuances of RS data promises to unlock deeper insights and foster trust in ML-driven EO applications.

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