基于压缩图像的遥感云图检测

基于压缩图像的遥感云图检测(论文13000字)
摘 要：在遥感图像中很多地表目标经常被云层所覆盖，使图像的后续应用变的非常困难，因此需要我们将遥感图像中的云识别出来。然而遥感图像往往占用大量的空间，应用深度学习需要高水平的设备来进行识别。因此需要对遥感图像做高效的压缩处理从而解决这一问题，本文通过小波变换对图像进行四级压缩，并结合U-Net神经网络对压缩后的遥感图像进行云的识别检测。从而达到降低计算量，加速神经网络的演算速率，节约时间和降低设备的需求。通过本文的方法，在保持识别准确度的同时，不仅将推理速度提升到了0.055秒/百万像素，且网络的最大内存成本也降低到了2Mb。
关键词：云的检测、深度学习、图像压缩、U-Net神经网络、小波变换
 
Remote Sensing Cloud Image Detection Based on Compressed Image
Abstract ：In remote sensing images, many surface targets are often covered by clouds, which makes the subsequent application of images very difficult. Therefore, we need to identify the clouds in remote sensing images. However, remote sensing images often take up a large amount of space. To solve this problem, remote sensing images need to be compressed efficiently. Therefore, this paper USES wavelet transform to compress the images at four levels and combines with u-net neural network to identify and detect the compressed remote sensing images in the cloud. In this way, the computation can be reduced, the speed of neural network can be accelerated, the time can be saved, and the demand of equipment can be reduced. The method proposed in this paper not only improves the reasoning speed to 0.055 seconds/megapixels, but also reduces the maximum memory cost of the network to 2Mb while maintaining the recognition accuracy.
Key Word:  Cloud detection、Deep learning、Image compression、U-Net Neural Network、Wavelet transform
 

目 录
1 绪论    1
1.1 研究背景和意义    1
1.2  国内外研究的现状    1
1.2.1 小波变换压缩的国内外现状    1
1.2.2 神经网络的国内外现状    1
1.3 论文章节规划    2
2小波变换    3
2.1小波变换的基本原理    3
2.1.1一维连续小波    3
2.1.2 高维连续小波变换    4
2.2小波变换的图像压缩    5
2.3图像压缩的MATLAB实现    6
3 云图检测    7
3.1阈值分割法    7
3.2全卷积神经网络    8
3.3神经网络结构    8
3.3.1卷积与池化    8
3.3.2激活函数    9
3.3.3转置卷积    10
3.4神经网络训练    10
3.4.1前向传播过程    10
3.4.2反向传播过程    10
3.5 UNet模型    11
4云图检测实验及对比结果    13
4.1基于深度学习的遥感云图检测    13
4.1.1数据设置    13
4.1.2神经网络训练    15
4.1.3实验结果分析    15
4.2基于压缩图像的遥感云图检测    17
4.2.1图像压缩    17
4.2.2实验结果分析    17
5总结与展望    19
参考文献    20
致谢    21