Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: PSGAN: A Generative Adversarial Network for Remote Sensing Image
  Pan-Sharpening   This paper addresses the problem of remote sensing image pan-sharpening from
the perspective of generative adversarial learning. We propose a novel deep
neural network based method named PSGAN. To the best of our knowledge, this is
one of the first attempts at producing high-quality pan-sharpened images with
GANs. The PSGAN consists of two components: a generative network (i.e.,
generator) and a discriminative network (i.e., discriminator). The generator is
designed to accept panchromatic (PAN) and multispectral (MS) images as inputs
and maps them to the desired high-resolution (HR) MS images and the
discriminator implements the adversarial training strategy for generating
higher fidelity pan-sharpened images. In this paper, we evaluate several
architectures and designs, namely two-stream input, stacking input, batch
normalization layer, and attention mechanism to find the optimal solution for
pan-sharpening. Extensive experiments on QuickBird, GaoFen-2, and WorldView-2
satellite images demonstrate that the proposed PSGANs not only are effective in
generating high-quality HR MS images and superior to state-of-the-art methods
and also generalize well to full-scale images.

3
The categories of this paper are: physics
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex