论文标题
使用M3GP演变的超功能改善遥感中烧伤区域的检测
Improving the Detection of Burnt Areas in Remote Sensing using Hyper-features Evolved by M3GP
论文作者
论文摘要
使用卫星图像时发现的一个问题是整个图像和不同图像的辐射变化。为了改善烧毁区域分类的遥感模型，我们设定了两个目标。首先是了解特征空间与模型的预测能力之间的关系，从而使我们可以解释在不同数据集中训练和测试时学习和概括之间的差异。我们发现，在多个图像中构建的数据集上的培训提供了更好地概括的模型。这些结果是通过可视化值在特征空间上的分散体来解释的。第二个目标是进化高功能，以改善各种测试集上不同分类器的性能。我们发现高功能是有益的，即使针对其他方法优化了超级功能，也可以获得具有XGBoost的最佳模型。
One problem found when working with satellite images is the radiometric variations across the image and different images. Intending to improve remote sensing models for the classification of burnt areas, we set two objectives. The first is to understand the relationship between feature spaces and the predictive ability of the models, allowing us to explain the differences between learning and generalization when training and testing in different datasets. We find that training on datasets built from more than one image provides models that generalize better. These results are explained by visualizing the dispersion of values on the feature space. The second objective is to evolve hyper-features that improve the performance of different classifiers on a variety of test sets. We find the hyper-features to be beneficial, and obtain the best models with XGBoost, even if the hyper-features are optimized for a different method.
