应用生成对抗网络（GAN）创建合成训练数据以提升ANN分类器从Landsat卫星影像中提取建成区像素的性能
Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery
AI总结：
本研究提出一种利用简单GAN架构生成合成训练像素的方法，以解决Landsat图像训练数据稀缺问题，有效提升了ANN分类器提取建成区像素的准确率和kappa系数。
摘要：
使用低分辨率Landsat图像训练神经网络进行基于像素的分类任务十分困难，因为代表单一类别且未与其他类别混合的可用像素数量较少，导致训练数据规模通常很小。由于训练数据的稀缺性，神经网络可能无法达到预期的准确度。使用旨在生成与训练样本数据具有相同分布的合成数据的生成网络，可以克服这一限制。在本研究中，我们提出了一种方法，通过开发简单的GAN架构来提升ANN分类器识别Landsat$7$图像中建成区像素的性能，该架构在使用原始建成区像素样本集训练后，能够生成合成训练像素。为了确保生成像素集与原始像素集对应的所有波段的边缘分布和联合分布无法区分，分别进行了非参数Kolmogorov Smirnov检验和基于Ball Divergence的分布等价性检验。观察到，随着将生成的建成区像素集加入原始数据集，ANN模型用于建成区分类的总体准确率和kappa系数分别从$0.9331$持续提升至$0.9983$和从$0.8277$提升至$0.9958$。
英文摘要：
Training a neural network for pixel based classification task using low resolution Landsat images is difficult as the size of the training data is usually small due to less number of available pixels that represent a single class without any mixing with other classes. Due to this scarcity of training data, neural network may not be able to attain expected level of accuracy. This limitation could be overcome using a generative network that aims to generate synthetic data having the same distribution as the sample data with which it is trained. In this work, we have proposed a methodology for improving the performance of ANN classifier to identify built-up pixels in the Landsat$7$ image with the help of developing a simple GAN architecture that could generate synthetic training pixels when trained using original set of sample built-up pixels. To ensure that the marginal and joint distributions of all the bands corresponding to the generated and original set of pixels are indistinguishable, non-parametric Kolmogorov Smirnov Test and Ball Divergence based Equality of Distributions Test have been performed respectively. It has been observed that the overall accuracy and kappa coefficient of the ANN model for built-up classification have continuously improved from $0.9331$ to $0.9983$ and $0.8277$ to $0.9958$ respectively, with the inclusion of generated sets of built-up pixels to the original one.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
