| dc.description.abstract | As satellite images provide periodical observations of a large area, Remote Sensing (RS) data can help analyze the developments of the Earth and its environmental variation. In recent years, with the advancement of deep learning technology, many Artificial Neural Networks (ANNs) have been proposed to support various remote sensing applications, such as image fusion, feature extraction, and Land Use and Land Cover (LULC) classification. However, the design of ANN models become more and more complex for better performance, ANNs are considered as “black boxes”, where their logics are hidden behind the scenes. To address this issue, Explainable Artificial Intelligence (XAI) methods were proposed to provide a peek into the black boxes. Therefore, the objective of this research is to understand the reasoning process of a RS ANN model with XAI methods, where the land cover classification is chosen to prove the concept. Existing XAI methods designed for Convolutional Neural Networks (CNNs) mainly focus on identifying important spatial features used in CNN models. However, spectral features are also important in RS image classification. Therefore, this research modifies existing XAI methods to extract important spectral features. As the first step, this research designs a deep learning network to retrieve spatial and spectral features from multi-spectral images and perform the land cover classification. The EuroSAT dataset from the Sentinel-2 satellite is applied in this research, where more than 90% classification accuracy is achieved. Afterward, this research modified three existing XAI methods including Guided Backpropagation, Gradient-weighted Class Activation Mapping (Grad-CAM), and Guided Grad-CAM to retrieve not only the spatial but also the spectral features learnt by the deep learning network. The analyses of results includes: (1) a qualitative analysis based on the visual saliency maps of each spectral band to interpret the reasoning basis of the deep learning network; (2) a quantitative analysis of ranking important spectral bands of each class based on the saliency maps; (3) finally, based on the important spectral bands identified by the proposed XAI methods, binary classification ANN models are trained for each class to verify the identified bands.
In summary, experimental results indicate that the models constructed with the identified spectral bands achieved similar accuracies compared to the model using all bands as inputs, where the classification accuracies for some classes even increased by 1.5-7%. Hence, XAI methods can capture the important spectral information in RS land cover classification and could help achieve better accuracy. Overall, the research demonstrates that besides the spatial features, the proposed XAI methods can also identify important spectral features for a better understanding to the underlying mechanisms of a RS land cover classification ANN model. | en_US |