PanNet#

class deepinv.models.PanNet(backbone_net=None, hrms_shape=(4, 900, 900), scale_factor=4, highpass_kernel_size=5, device='cpu', **kwargs)[source]#

Bases: Module

PanNet architecture for pan-sharpening.

PanNet neural network from Yang et al. PanNet: A Deep Network Architecture for Pan-Sharpening, ICCV 2017.

Takes input measurements as a deepinv.utils.TensorList with elements (MS, PAN), where MS is the low-resolution multispectral image of shape (B, C, H, W) and PAN is the high-resolution panchromatic image of shape (B, 1, H*r, W*r) where r is the pan-sharpening factor.

Parameters:

  • backbone_net (torch.nn.Module) – Backbone neural network, e.g. ResNet. If None, defaults to a simple ResNet.

  • hrms_shape (tuple[int]) – shape of high-resolution multispectral images (C,H,W), defaults to (4,900,900)

  • scale_factor (int) – pansharpening downsampling ratio HR/LR, defaults to 4

  • highpass_kernel_size (int) – square kernel size for extracting high-frequency features, defaults to 5

  • device (str) – torch device, defaults to “cpu”

create_sampler(direction, hr_shape, noise_gain=0.0)[source]#

Helper function for downsampling/upsampling images (useful for reduced-resolution training with Wald’s protocol).

Parameters:

  • direction (str) – down or up

  • hr_shape (tuple) – HRMS input shape (C,H,W)

  • noise_gain (float) – noise applied to downsampling ONLY, defaults to 0.

Return dinv.physics.Physics:

deepinv sampler

Return type:

Physics

forward(y, physics, *args, **kwargs)[source]#

Evaluate the pansharpening model

Parameters:

Examples using PanNet:#

Remote sensing with satellite images

Remote sensing with satellite images