基于机器学习的遥感图像识别检测技术研究及应用

Study and Application of the Technology of Remote Sensing Image Recognition and Detection Based on Machine Learning

基于统计的传统无监督机器学习识别分类技术虽经持续改进对于高分遥感图像效果仍不佳,深度学习具备仿人类神经网络多层抽象能力和无监督自学习特点,具有从大量无标签高光谱遥感数据中自主学习和构建其特征的能力,再结合常用分类算法进行识别分类,比传统方法具有相对更高的准确率.

Although the traditional unsupervised machine learning recognition classification technology based on statistics has been improved continuously, the effect is still poor for high resolution remote sensing images, deep learning has the ability to imitate the multi-level ab-straction and unsupervised self-learning features of the human neural network, and has the abil-ity of autonomous learning and constructing its characteristics from a large number of non-label hyper-spectral remote sensing data. Combined with common classification algorithm for classifi-cation, it has a relatively higher accuracy than that of the traditional method.