Semantic segmentation is a fundamental task in computer vision and image scenery detection. Many applications, such as urban planning, change detection, and environmental monitoring require accurate segmentation. Hence, most segmentation tasks are performed by humans. Currently, with the growth of deep convolutional neural network (DCNN), there are many works aimed to find the best network architecture fitting for this task. In this work, the GoogLeNet classifier is used to perform better segmentation as well as a classification for satellite images. The Wiener filter is used here for image denoising. Data Augmentation is performed to extract high information about the input picture. The output of the above steps helps in classification i.e. it identifies the scenery of the input image with four labels. The result shows that the GoogLeNet based image classification has reduced error rate and it also increases the accuracy of output. Additionally, the efficiency of the Wiener filters also described clearly in the result.

语义分割是计算机视觉和图像风景检测中的基本任务。许多应用程序，例如城市规划，变更检测和环境监控，都需要精确的细分。因此，大多数分割任务是由人执行的。当前，随着深度卷积神经网络（DCNN）的发展，有许多旨在寻找最适合该任务的网络体系结构的工作。在这项工作中，使用GoogLeNet分类器执行更好的分割以及对卫星图像进行分类。维纳滤波器在这里用于图像降噪。执行数据增强以提取有关输入图片的大量信息。上述步骤的输出有助于分类，即，它使用四个标签识别输入图像的风景。结果表明，基于GoogLeNet的图像分类降低了错误率，还提高了输出的准确性。此外，结果中还清楚地描述了维纳滤波器的效率。