Example-Based Explainable AI and its Application for Remote Sensing Image Classification

• URL: http://arxiv.org/abs/2302.01526v1
• Date: Fri, 3 Feb 2023 03:48:43 GMT
• Title: Example-Based Explainable AI and its Application for Remote Sensing Image Classification
• Authors: Shin-nosuke Ishikawa, Masato Todo, Masato Taki, Yasunobu Uchiyama, Kazunari Matsunaga, Peihsuan Lin, Taiki Ogihara, Masao Yasui
• Abstract summary: We show an example of an instance in a training dataset that is similar to the input data to be inferred. Using a remote sensing image dataset from the Sentinel-2 satellite, the concept was successfully demonstrated.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a method of explainable artificial intelligence (XAI), "What I Know (WIK)", to provide additional information to verify the reliability of a deep learning model by showing an example of an instance in a training dataset that is similar to the input data to be inferred and demonstrate it in a remote sensing image classification task. One of the expected roles of XAI methods is verifying whether inferences of a trained machine learning model are valid for an application, and it is an important factor that what datasets are used for training the model as well as the model architecture. Our data-centric approach can help determine whether the training dataset is sufficient for each inference by checking the selected example data. If the selected example looks similar to the input data, we can confirm that the model was not trained on a dataset with a feature distribution far from the feature of the input data. With this method, the criteria for selecting an example are not merely data similarity with the input data but also data similarity in the context of the model task. Using a remote sensing image dataset from the Sentinel-2 satellite, the concept was successfully demonstrated with reasonably selected examples. This method can be applied to various machine-learning tasks, including classification and regression.

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