… Challenges of remote sensing in general and of satellite images in particular vary. First, …
Then, we discuss the topic of deep learning methods for remote mapping. Finally, we compare …

… sensing data to train more effective mapping relationship between MODIS and Landsat
images by deep learning … fusion models based on learning have outstanding performance, but …

… Deep Learning in recent years has been in the field of image processing. Research on Deep
Learning … neuron network (CNN) can learn solutions with human-level capability for certain …

… sensors. To extract the water bodies from the high-resolution remote sensing images, we
proposed a novel deep learning … and diverse information in the remote sensing images. The …

… used deep learning based supervised image classification algorithm and images collected
… Therefore, we have applied deep learning approach for the image classification of forested …

… To this end, this article proposes a new RSSCoriented error-tolerant deep learning (RSSC-…
sensing image scene dataset. In our proposed RSSC-ETDL method, learning multiview …

… on deep learning and big data. In this paper, we reviewed the latest deep learning approaches
pertinent to the image … We compiled the current sensor tools and deep learning principles …

… -based) VHR remote sensing image mapping assisted by HBD using … to crop the remote
sensing image and acquire objects/… module is called deep learning in which a remote sensing …

… With the development of compressed-sensing (CS) theory 13,14 , compressive spectral
imaging has received growing attention from researchers because of its elegant combination of …

… deep learning models applied to remote sensing datasets to show which method performs
well in extracting road parts from high-resolution remote sensing images… sensing images from …