Author(s)

Jingya Huang

Corresponding Author

Jingya Huang

ABSTRACT

High-resolution remote sensing images, as a special type of image taken by satellites and other aircraft, have extremely important valuesand status in both military and civilian applications. With the continuous development of remote sensing technology and computer vision technology, remote sensing image target detection plays an important role in many fields such as military and civilian. In the existing high-resolution image change detection research methods, image registration and feature extraction are the key factors affecting the change detection results. Traditional target detection and recognition methods are difficult to adapt to massive high-resolution remote sensing image data. It is necessary to find a way to automatically learn the most effective features from massive image data, and fully re-examine the association between data. In this paper, the deep learning deconvolution neural network is used to detect the targets in remote sensing images. According to the related research of media neural cognitive computing, the development trend and research direction of target classification and recognition of remote sensing image big data are discussed.

KEYWORDS

High resolution, Remote sensing image, Target detection, Deep learning