We interpret a deep-learning semantic segmentation model used to classify
coastline satellite images into land and water. This is to build trust in the
model and gain new insight into the process of coastal water body extraction.
Specifically, we seek to understand which spectral bands are important for
predicting segmentation masks. This is done using a permutation importance
approach. Results show that the NIR is the most important spectral band.
Permuting this band lead to a decrease in accuracy of 38.12 percentage points.
This is followed by Water Vapour, SWIR 1, and Blue bands with 2.58, 0.78 and
0.19 respectively. Water Vapour is not typically used in water indices and
these results suggest it may be useful for water body extraction. Permuting,
the Coastal Aerosol, Green, Red, RE1, RE2, RE3, RE4, and SWIR 2 bands did not
decrease accuracy. This suggests they could be excluded from future model
builds reducing complexity and computational requirements.

利用深度学习语义分割模型对海岸线卫星图像进行分类，以建立对模型的信任并获取关于海岸水体提取过程的新见解。通过排列重要性方法，研究了哪些光谱波段对预测分割掩模最为重要，结果显示 NIR 是最重要的光谱波段，其排列导致准确性下降 38.12 个百分点，其次是水汽、SWIR 1 和蓝色波段，分别下降 2.58、0.78 和 0.19 个百分点，这表明水汽在水体提取中可能非常有用。排列海岸气溶胶、绿色、红色、RE1、RE2、RE3、RE4 和 SWIR 2 波段并没有降低准确性，这表明在将来的模型构建中可以排除它们，减少复杂度和计算要求。