{
  "id": "1712.09809",
  "version": "v1",
  "published": "2017-12-28T10:28:38.000Z",
  "updated": "2017-12-28T10:28:38.000Z",
  "title": "A Multi-Scale and Multi-Depth Convolutional Neural Network for Remote Sensing Imagery Pan-Sharpening",
  "authors": [
    "Qiangqiang Yuan",
    "Yancong Wei",
    "Xiangchao Meng",
    "Huanfeng Shen",
    "Liangpei Zhang"
  ],
  "categories": [
    "cs.CV"
  ],
  "abstract": "Pan-sharpening is a fundamental and significant task in the field of remote sensing imagery processing, in which high-resolution spatial details from panchromatic images are employed to enhance the spatial resolution of multi-spectral (MS) images. As the transformation from low spatial resolution MS image to high-resolution MS image is complex and highly non-linear, inspired by the powerful representation for non-linear relationships of deep neural networks, we introduce multi-scale feature extraction and residual learning into the basic convolutional neural network (CNN) architecture and propose the multi-scale and multi-depth convolutional neural network (MSDCNN) for the pan-sharpening of remote sensing imagery. Both the quantitative assessment results and the visual assessment confirm that the proposed network yields high-resolution MS images that are superior to the images produced by the compared state-of-the-art methods.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-12-28T10:28:38.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "multi-depth convolutional neural network",
      "remote sensing imagery pan-sharpening",
      "spatial resolution ms image",
      "yields high-resolution ms images",
      "multi-scale"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}