人工智能-基于卷积神经网络的高分辨率遥感影像建筑物提取方法研究.pdf

ResUNet-GAN，以对抗的方式实现分割任务。其中生成网络 ResUNet 在编码器

As the sources of high-resolution remote sensing images continue to increase,

image acquisition is becoming easier. The feature information contained in massive

data has been widely used in many fields such as map mapping, resource exploration,

environmental monitoring, land and resources investigation, change detection and

disaster assessment. Buildings are an important part of urban feature information.

Manually extracting and updating buildings wastes a lot of human, material and

financial resources. Therefore, how to accurately, quickly and automatically extract

building information from satellite images has become one of key research directions

and has achieved certain results. Therefore, this paper makes deep research and analysis

on related algorithms in the field of deep learning semantic segmentation. The main

research work is as follows:

(1) This article introduces the basic principles and training methods of

convolutional neural networks. In order to solve the problem of lack of high-resolution

remote sensing image data, the related operations of building a remote sensing image

building data set are introduced in detail. It is mainly divided into image preprocessing

and image augmentation, including image denoising and image Enhancements, data

expansion, and data set partitioning enhance the richness and diversity of the dataset.

(2) In this paper, the characteristics of several existing popular network structures

information flow of feature maps with different resolutions is strengthened, which is

beneficial to the reconstruction of lost information and is more efficient than the

pyramid structure. Finally, a boundary loss function is proposed, which enables the

model to pay more attention to the building boundary during the training process, which

effectively improves the boundary jagged problem.

(3) Based on the idea of conditional generative adversarial network, this paper

designs a generative-segmentation adversarial network named ResUNet-GAN to

achieve the segmentation task in an adversarial manner. Generating network ResUNet

uses the residual module in the encoder stage, which effectively solves the problem of

building extraction to a certain extent.