基于CNN的不同空间分辨率影像土地覆被分类研究

基于卷积神经网络（Convolutional Neural Networks, CNN）和5种不同空间分辨率的遥感影像，对西宁市东部一区域开展土地覆被分类研究，旨在探索CNN在不同空间分辨率下进行影像分类的差异性和对不同地物的提取能力。为提高样本的选择效率，引入了窗口滑动方法进行辅助选样。研究表明5种不同空间分辨率影像的总体分类精度均达89%以上,Kappa系数达0.86以上，分类精度较高。在所涉及的分辨率尺度范围内，空间分辨率越高，CNN分类结果越精细，并能保持较高的分类精度，表明CNN更适合高空间分辨率影像分类；但同时影像空间分辨率越高，地物表现出较高的类内变异性和低类间差异性，分类精度有降低的趋势。相比较而言，SPOT 6影像的分类精度最高，同时窗口滑动是一种有效的样本辅助选择方法。研究对今后同类工作具有一定的借鉴意义。

Land Cover Classification for Different Spatial Resolution Images from CNN

Based on convolutional neural networks and five different spatial resolution remote sensing images, the land use/land cover classification study was carried out on a small area in the eastern part of Xining City, aiming at exploring the differences of image classification by CNN with different spatial resolutions and CNN’s ability to extract different features. In order to improve the selection efficiency of the samples, a window sliding method was introduced to assist the samples selection. The research shows that the overall classification accuracy of the five different spatial resolution images is above 89%, the Kappa coefficient is above 0.86. The result further shows that within the resolution scale the higher the resolution, the performance of the CNN classification results for the details is better, and can maintain high classification accuracy, indicating that CNN is more suitable for high spatial resolution images; at the same time, the image spatial resolution is too high, the ground objects exhibit high intra-class variability and low inter-class variability, the classification accuracy tends to decrease. In comparison, CNN has the best classification effect on SPOT 6 images in this study, and window sliding is an effective sample-assisted selection method. This research has certain reference significance for similar research in the future.