融合不精准先验知识的Landsat 8 OLI影像深度学习分类方法

遥感影像解译是获得土地利用和土地覆盖（LULC）信息最为重要的途径之一，而自动化分类是提高LULC信息获取效率的关键。实际场景中包含大量不精准的先验知识，提取并融合其中的可用知识能进一步提高影像分类方法的精度、自动化率和规模应用能力。基于上述情况，提出了一种融合不精准先验知识的Landsat 8 OLI影像深度学习分类方法。该方法可自动规避先验知识中的不精准单元，在图斑约束空间内实现了分类样本的自动化区域选择和特征提取，并获得了高置信度知识，然后利用这些分类样本训练深度残差网络，从而实现大区域影像的精确分类。以常州市新北区为例进行实验，选用该区域2009年土地利用现状数据作为先验数据，2014年Landsat 8 OLI影像作为待分类影像。实验结果表明，所提方法可融合不精准先验知识，对大面积连片LULC信息分类精确，主要地类图斑界限准确，全图分类图斑精度达到了88.7%，Kappa系数为0.842。该方法能配合深度学习方法实现高精度Landsat 8 OLI遥感影像分类。

Deep learning classification method of Landsat 8 OLI images based on inaccurate prior knowledge

Remote sensing image interpretation plays an important role in the acquisition of Land Use and Land Cover (LULC) information, and automatic classification serves as the key to improve the efficiency of LULC information acquisition. The actual scenes have a great mount of inaccurate prior knowledge. Extracting and integrating the available knowledge in the prior knowledge can help to further improve the accuracy, automation rate and scale application ability of image classification methods. Based on the above situation, a new deep learning classification method of Landsat 8 OLI images based on inaccurate prior knowledge was proposed. For the proposed method, inaccurate units in prior knowledge were avoided automatically, realizing automatic region selection and feature extraction of classified samples and obtaining high confidence knowledge in the constraint space of patches. Then, the deep residual network was trained by using these classified samples, and the accurate classification of large-area images was achieved. In the experiment, Xinbei district of Changzhou city was taken as the example, the data of 2009 land use status of this district was selected as the prior data, and the 2014 Landsat 8 OLI image of this district was selected as the to-be-classified image. The experimental results show that the proposed method has advantages such as the integration of inaccurate prior knowledge and the accurate classification of large-area contiguous LULC information. Besides, it can obtain the accurate boundary of main land use patches, and has the accuracy for patch classification in the whole image of 88.7% and the Kappa coefficient of 0.842.The proposed method can cooperate with deep learning method to achieve high precision Landsat 8 OLI remote sensing image classification.