… Satellite or manned aerial remote sensing have the advantages of region-… image resolution,
poor human safety, and prohibitive cost. Unmanned Aerial Vehicle (UAV) remote sensing is …

… resolution remote sensing … remote sensing image processing software Matlab, and then
combined with Back Propagation neural network classifier for the high resolution remote sensing …

… , which can detect changes for bitemporal remote sensing images. Different from the … 1)
In the application of deep learning for remote sensing change detection, we define a new …

… increase with increasing image resolution, and … remote sensing image classification.
SO-DNN adopts the simple linear iterative clustering (SLIC) algorithm to segment remote sensing …

… of remote sensing … in Remote sensing image depends on Satellite altitude, focal length
and the size of detector. From this aspect, the ground target that is smaller than image resolution …

… In this paper, we present a three-step super-resolution method for remote sensing images
via the WT combined with the recursive Res-Net (WTCRR). First, the LR image is decomposed …

… tions are generally preferred in remote sensing, actual observations are often not evenly
sampled and have lower-than-desired resolution. Hence, there is an interest in resolution …

… addition, the scale selection algorithm is essentially equivalent to setting the image pixel
number as the optimal scale according to the size of the object shape and the image resolution. …

… Deep convolutional networks perform well in remote sensing (RS) image classification. … in
remote sensing classification tasks. Traditionally, the acquisition of remote sensing images is …

… on the manual digitization of high spatial resolution remote sensing (HSRRS) images, which
… The rapid development of remote sensing technology makes it possible to use the road …