Classification of satellite images plays a vital role in remote sensing applications. Numerous algorithms have been developed and tested to classify a satellite image. The main purpose of these algorithms is to lessen the human efforts and errors in minimum time. Classification is performed on satellite images for various purposes. This paper presents a framework to classify a satellite image b...

We propose and demonstrate a fast, robust method for using satellite images to localize an Unmanned Aerial Vehicle (UAV). Previous work has large storage computation costs is unable run in real time. In this work, we collect Google Earth (GE) desired flight path offline autoencoder trained compress these low-dimensional vector representation while retaining the key features. This used UAV image...

Small satellites have limited payload and their attitudes are sometimes difficult to determine from the limited onboard sensors alone. Wrong attitudes lead to inaccurate map projections and measurements that require post-processing correction. In this study, we propose an automated and robust scheme that derives the satellite attitude from its observation images and known satellite position by ...

With the growing number of high-resolution satellite images, the traditional image retrieval method has become a bottleneck in the massive application of high-resolution satellite images because of the low degree of automation. However, there are few studies on the automation of satellite image retrieval. This paper presents an automatic high-resolution satellite image accurate retrieval method...