Low Resolution Recognition of Aerial Images Remote sensing for classification has been widely studied and is useful for a lot of applications like precision agriculture, surveillance, and military applications. Recently, due to tremendous results achieved by deep learning using Convolutional Neural Networks (CNN) for Imagenet dataset, there have been a large number of works which use deep learning for aerial image classification. Most of the works concentrate on original resolution and there are no works on lowresolution recognition of aerial images. This work is critical because aerial images are taken from a very high distance from the ground and the cost of installing high definition cameras is high, so it is hard to get a high resolution of the image. In this paper, we explore how we can do the better classification of aerial images for original spatial resolution and low spatial resolution in deep learning by using texture information. In our framework, we use YUV color space which is generally used for video coding and we also use Laplacian of Gaussian (LOG) information to exploit the texture information. We decouple RGB information into luminance information (Y channel), color information (UV) and texture information (LOG) and we train a separate CNN for each feature and combine them using autoencoder and with our results, we show that we do better than RGB images in original resolution and low resolution.

Deep Learning for Remote Sensing Image SuperResolution The aim of image superResolution (SR) is to enhance image resolution while still retain the integrity of the original image. There are many ongoing types of research on image superresolution for natural images, but any a few on remote sensing images. In this paper, we proposed deep learningbased image superresolution techniques, including convolutional neural network (CNN) and generative adversarial network (GAN) to enhance the resolution of remote sensing images by a factor 4. In CNN, it learns an end to end mapping from lowresolution image to highresolution image whereas, in GAN, the model learns the mapping guided by the GAN loss and gives the sharper appearance in highresolution images. Our experimental results show that visually GAN models perform well but are inferior to other models in terms of image quality metrics, whereas quantitatively CNN models outperform other superresolution models.