136 research outputs found
A New Ratio Image Based CNN Algorithm For SAR Despeckling
In SAR domain many application like classification, detection and
segmentation are impaired by speckle. Hence, despeckling of SAR images is the
key for scene understanding. Usually despeckling filters face the trade-off of
speckle suppression and information preservation. In the last years deep
learning solutions for speckle reduction have been proposed. One the biggest
issue for these methods is how to train a network given the lack of a
reference. In this work we proposed a convolutional neural network based
solution trained on simulated data. We propose the use of a cost function
taking into account both spatial and statistical properties. The aim is two
fold: overcome the trade-off between speckle suppression and details
suppression; find a suitable cost function for despeckling in unsupervised
learning. The algorithm is validated on both real and simulated data, showing
interesting performances
Edge Preserving CNN SAR Despeckling Algorithm
SAR despeckling is a key tool for Earth Observation. Interpretation of SAR
images are impaired by speckle, a multiplicative noise related to interference
of backscattering from the illuminated scene towards the sensor. Reducing the
noise is a crucial task for the understanding of the scene. Based on the
results of our previous solution KL-DNN, in this work we define a new cost
function for training a convolutional neural network for despeckling. The aim
is to control the edge preservation and to better filter manmade structures and
urban areas that are very challenging for KL-DNN. The results show a very good
improvement on the not homogeneous areas keeping the good results in the
homogeneous ones. Result on both simulated and real data are shown in the
paper.Comment: Accepted to LAGIRS 202
Multi-Objective CNN Based Algorithm for SAR Despeckling
Deep learning (DL) in remote sensing has nowadays become an effective
operative tool: it is largely used in applications such as change detection,
image restoration, segmentation, detection and classification. With reference
to synthetic aperture radar (SAR) domain the application of DL techniques is
not straightforward due to non trivial interpretation of SAR images, specially
caused by the presence of speckle. Several deep learning solutions for SAR
despeckling have been proposed in the last few years. Most of these solutions
focus on the definition of different network architectures with similar cost
functions not involving SAR image properties. In this paper, a convolutional
neural network (CNN) with a multi-objective cost function taking care of
spatial and statistical properties of the SAR image is proposed. This is
achieved by the definition of a peculiar loss function obtained by the weighted
combination of three different terms. Each of this term is dedicated mainly to
one of the following SAR image characteristics: spatial details, speckle
statistical properties and strong scatterers identification. Their combination
allows to balance these effects. Moreover, a specifically designed architecture
is proposed for effectively extract distinctive features within the considered
framework. Experiments on simulated and real SAR images show the accuracy of
the proposed method compared to the State-of-Art despeckling algorithms, both
from quantitative and qualitative point of view. The importance of considering
such SAR properties in the cost function is crucial for a correct noise
rejection and details preservation in different underlined scenarios, such as
homogeneous, heterogeneous and extremely heterogeneous
A model-free ratio based nonlocal framework for denoising of SAR and TomoSAR data
This paper introduces a general patch-based model-free framework for despeckling of single and multi-baseline synthetic aperture radar (SAR) image. Particularly, the method is based on the empirical distribution similarity between the patch containing the pixel to be restored and the patch containing a candidate similar pixel. In order to decide whether the patches follow a similar distribution, the Kolmogorov-Smirnov test is adapted. Finally, within the restoration process, the selected similar pixels are aggregated based on their relative importance obtained according to their distribution similarities. Experimental validation of the proposed methodology is provided using different real data sets and compared with existing NLSAR approach in relation to single SAR image despeckling and tomographic application for the 3D reflectivity reconstruction of volumetric media as well as permanent scatterer detection in urban environments
A deep learning solution for height estimation on a forested area based on Pol-TomoSAR data
Forest height and underlying terrain reconstruction is one of the main aims in dealing with forested areas. Theoretically, synthetic aperture radar tomography (TomoSAR) offers the possibility to solve the layover problem, making it possible to estimate the elevation of scatters located in the same resolution cell. This article describes a deep learning approach, named tomographic SAR neural network (TSNN), which aims at reconstructing forest and ground height using multipolarimetric multibaseline (MPMB) SAR data and light detection and ranging (LiDAR)-based data. The reconstruction of the forest and ground height is formulated as a classification problem, in which TSNN, a feedforward network, is trained using covariance matrix elements as input vectors and quantized LiDAR-based data as the reference. In our work, TSNN is trained and tested with P-band MPMB data acquired by ONERA over Paracou region of French Guiana in the frame of the European Space Agency's campaign TROPISAR and LiDAR-based data provided by the French Agricultural Research Center. The novelty of the proposed TSNN is related to its ability to estimate the height with a high agreement with LiDAR-based measurement and actual height with no requirement for phase calibration. Experimental results of different covariance window sizes are included to demonstrate that TSNN conducts height measurement with high spatial resolution and vertical accuracy outperforming the other two TomoSAR methods. Moreover, the conducted experiments on the effects of phase errors in different ranges show that TSNN has a good tolerance for small errors and is still able to precisely reconstruct forest heights
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