59 research outputs found
Band-wise Hyperspectral Image Pansharpening using CNN Model Propagation
Hyperspectral pansharpening is receiving a growing interest since the last
few years as testified by a large number of research papers and challenges. It
consists in a pixel-level fusion between a lower-resolution hyperspectral
datacube and a higher-resolution single-band image, the panchromatic image,
with the goal of providing a hyperspectral datacube at panchromatic resolution.
Thanks to their powerful representational capabilities, deep learning models
have succeeded to provide unprecedented results on many general purpose image
processing tasks. However, when moving to domain specific problems, as in this
case, the advantages with respect to traditional model-based approaches are
much lesser clear-cut due to several contextual reasons. Scarcity of training
data, lack of ground-truth, data shape variability, are some such factors that
limit the generalization capacity of the state-of-the-art deep learning
networks for hyperspectral pansharpening. To cope with these limitations, in
this work we propose a new deep learning method which inherits a simple
single-band unsupervised pansharpening model nested in a sequential band-wise
adaptive scheme, where each band is pansharpened refining the model tuned on
the preceding one. By doing so, a simple model is propagated along the
wavelength dimension, adaptively and flexibly, with no need to have a fixed
number of spectral bands, and, with no need to dispose of large, expensive and
labeled training datasets. The proposed method achieves very good results on
our datasets, outperforming both traditional and deep learning reference
methods. The implementation of the proposed method can be found on
https://github.com/giu-guarino/R-PN
Source-Aware Spatial-Spectral-Integrated Double U-Net for Image Fusion
In image fusion tasks, pictures from different sources possess distinctive
properties, therefore treating them equally will lead to inadequate feature
extracting. Besides, multi-scaled networks capture information more
sufficiently than single-scaled models in pixel-wised problems. In light of
these factors, we propose a source-aware spatial-spectral-integrated double
U-shaped network called Net. The network is mainly composed of a
spatial U-net and a spectral U-net, which learn spatial details and spectral
characteristics discriminately and hierarchically. In contrast with most
previous works that simply apply concatenation to integrate spatial and
spectral information, a novel structure named the spatial-spectral block
(called Block) is specially designed to merge feature maps from
different sources effectively. Experiment results show that our method
outperforms the representative state-of-the-art (SOTA) approaches in both
quantitative and qualitative evaluations for a variety of image fusion
missions, including remote sensing pansharpening and hyperspectral image
super-resolution (HISR)
Target-adaptive CNN-based pansharpening
We recently proposed a convolutional neural network (CNN) for remote sensing
image pansharpening obtaining a significant performance gain over the state of
the art. In this paper, we explore a number of architectural and training
variations to this baseline, achieving further performance gains with a
lightweight network which trains very fast. Leveraging on this latter property,
we propose a target-adaptive usage modality which ensures a very good
performance also in the presence of a mismatch w.r.t. the training set, and
even across different sensors. The proposed method, published online as an
off-the-shelf software tool, allows users to perform fast and high-quality
CNN-based pansharpening of their own target images on general-purpose hardware
DDRF: Denoising Diffusion Model for Remote Sensing Image Fusion
Denosing diffusion model, as a generative model, has received a lot of
attention in the field of image generation recently, thanks to its powerful
generation capability. However, diffusion models have not yet received
sufficient research in the field of image fusion. In this article, we introduce
diffusion model to the image fusion field, treating the image fusion task as
image-to-image translation and designing two different conditional injection
modulation modules (i.e., style transfer modulation and wavelet modulation) to
inject coarse-grained style information and fine-grained high-frequency and
low-frequency information into the diffusion UNet, thereby generating fused
images. In addition, we also discussed the residual learning and the selection
of training objectives of the diffusion model in the image fusion task.
Extensive experimental results based on quantitative and qualitative
assessments compared with benchmarks demonstrates state-of-the-art results and
good generalization performance in image fusion tasks. Finally, it is hoped
that our method can inspire other works and gain insight into this field to
better apply the diffusion model to image fusion tasks. Code shall be released
for better reproducibility
Deep Learning based data-fusion methods for remote sensing applications
In the last years, an increasing number of remote sensing sensors have been launched to orbit around the Earth, with a continuously growing production of massive data, that are useful for a large number of monitoring applications, especially for the monitoring task. Despite modern optical sensors provide rich spectral information about Earth's surface, at very high resolution, they are weather-sensitive. On the other hand, SAR images are always available also in presence of clouds and are almost weather-insensitive, as well as daynight available, but they do not provide a rich spectral information and are severely affected by speckle "noise" that make difficult the information extraction. For the above reasons it is worth and challenging to fuse data provided by different sources and/or acquired at different times, in order to leverage on their diversity and complementarity to retrieve the target information. Motivated by the success of the employment of Deep Learning methods in many image processing tasks, in this thesis it has been faced different typical remote sensing data-fusion problems by means of suitably designed Convolutional Neural Networks
Probability-based Global Cross-modal Upsampling for Pansharpening
Pansharpening is an essential preprocessing step for remote sensing image
processing. Although deep learning (DL) approaches performed well on this task,
current upsampling methods used in these approaches only utilize the local
information of each pixel in the low-resolution multispectral (LRMS) image
while neglecting to exploit its global information as well as the cross-modal
information of the guiding panchromatic (PAN) image, which limits their
performance improvement. To address this issue, this paper develops a novel
probability-based global cross-modal upsampling (PGCU) method for
pan-sharpening. Precisely, we first formulate the PGCU method from a
probabilistic perspective and then design an efficient network module to
implement it by fully utilizing the information mentioned above while
simultaneously considering the channel specificity. The PGCU module consists of
three blocks, i.e., information extraction (IE), distribution and expectation
estimation (DEE), and fine adjustment (FA). Extensive experiments verify the
superiority of the PGCU method compared with other popular upsampling methods.
Additionally, experiments also show that the PGCU module can help improve the
performance of existing SOTA deep learning pansharpening methods. The codes are
available at https://github.com/Zeyu-Zhu/PGCU.Comment: 10 pages, 5 figure
Single-image super-resolution of sentinel-2 low resolution bands with residual dense convolutional neural networks
Sentinel-2 satellites have become one of the main resources for Earth observation images because they are free of charge, have a great spatial coverage and high temporal revisit. Sentinel-2 senses the same location providing different spatial resolutions as well as generating a multi-spectral image with 13 bands of 10, 20, and 60 m/pixel. In this work, we propose a single-image super-resolution model based on convolutional neural networks that enhances the low-resolution bands (20 m and 60 m) to reach the maximal resolution sensed (10 m) at the same time, whereas other approaches provide two independent models for each group of LR bands. Our proposed model, named Sen2-RDSR, is made up of Residual in Residual blocks that produce two final outputs at maximal resolution, one for 20 m/pixel bands and the other for 60 m/pixel bands. The training is done in two stages, first focusing on 20 m bands and then on the 60 m bands. Experimental results using six quality metrics (RMSE, SRE, SAM, PSNR, SSIM, ERGAS) show that our model has superior performance compared to other state-of-the-art approaches, and it is very effective and suitable as a preliminary step for land and coastal applications, as studies involving pixel-based classification for Land-Use-Land-Cover or the generation of vegetation indices.This work was funded by the Spanish Agencia Estatal de Investigación (AEI) under projects ARTEMISAT-2 (CTM2016-77733-R) and PID2020-117142GB-I00 of the call MCIN/AEI/10.13039/501100011033).Peer ReviewedPostprint (published version
- …