7 research outputs found
Isotropization of Quaternion-Neural-Network-Based PolSAR Adaptive Land Classification in Poincare-Sphere Parameter Space
Quaternion neural networks (QNNs) achieve high accuracy in polarimetric synthetic aperture radar classification for various observation data by working in Poincare-sphere-parameter space. The high performance arises from the good generalization characteristics realized by a QNN as 3-D rotation as well as amplification/attenuation, which is in good consistency with the isotropy in the polarization-state representation it deals with. However, there are still two anisotropic factors so far which lead to a classification capability degraded from its ideal performance. In this letter, we propose an isotropic variation vector and an isotropic activation function to improve the classification ability. Experiments demonstrate the enhancement of the QNN ability
Averaged Stokes Vector Based Polarimetric SAR Data Interpretation
In this paper, we propose a new polarimetric synthetic aperture radar (SAR) data interpretation method based on a locally averaged Stokes vector. We first propose a method to extract discriminators from all three components of the averaged Stokes vector. Based on the extracted discriminators, we build four physical interpretation layers with ascending priorities, i.e., the basic structure layer, the low-coherence targets layer, the man-made targets layer, and the low-backscattering targets layer. An intuitive final image can be generated by simply stacking the four layers in the priority order. We test the performance of the proposed method over Advanced Land Observing Satellite Phased Array type L-band SAR (ALOS-PALSAR) data. Experimental results show that the proposed method has high interpretation performance, particularly for skew-aligned or randomly distributed buildings and isolated man-made targets such as bridges
Study of the speckle noise effects over the eigen decomposition of polarimetric SAR data: a review
This paper is focused on considering the effects of
speckle noise on the eigen decomposition of the co-
herency matrix. Based on a perturbation analysis of the
matrix, it is possible to obtain an analytical expression for
the mean value of the eigenvalues and the eigenvectors,
as well as for the Entropy, the Anisotroopy and the dif-
ferent a angles. The analytical expressions are compared
against simulated polarimetric SAR data, demonstrating
the correctness of the different expressions.Peer ReviewedPostprint (published version
New target detector based on geometrical perturbation filters for polarimetric Synthetic Aperture Radar (POL-SAR)
Synthetic Aperture Radar (SAR) is an active microwave remote sensing system able to
acquire high resolution images of the scattering behaviour of an observed scene. The
contribution of SAR polarimetry (POLSAR) in detection and classification of objects is
described and found to add valuable information compared to previous approaches. In
this thesis, a new target detection/classification methodology is developed that makes
novel use of the polarimetric information of the backscattered field from a target. The
detector is based on a geometrical perturbation filter which correlates the target of
interest with its perturbed version. Specifically, the operation is accomplished with a
polarimetric coherence representing a weighted and normalised inner product between
the target and its perturbed version, where the weights are extracted from the
observables. The mathematical formulation is general and can be applied to any
deterministic (point) target. However, in this thesis the detection is primarily focused on
multiple reflections and oriented dipoles due to their extensive availability in common
scenarios. An extensive validation against real data is provided exploiting different
datasets. They include one airborne system: E-SAR L-band (DLR, German Aerospace
Centre); and three satellite systems: ALOS-PALSAR L-band (JAXA, Japanese
Aerospace Exploration Agency), RADARSAT-2 C-band (Canadian Space Agency) and
TerraSAR-X X-band (DLR). The attained detection masks reveal significant agreement
with the expected results based on the theoretical description. Additionally, a
comparison with another widely used detector, the Polarimetric Whitening Filter (PWF)
is presented. The methodology proposed in this thesis appears to outperform the PWF in
two significant ways: 1) the detector is based on the polarimetric information rather than the amplitude of the return, hence the detection is not restricted to bright targets; 2) the
algorithm is able to discriminate among the detected targets (i.e. target recognition)
 Ocean Remote Sensing with Synthetic Aperture Radar
The ocean covers approximately 71% of the Earth’s surface, 90% of the biosphere and contains 97% of Earth’s water. The Synthetic Aperture Radar (SAR) can image the ocean surface in all weather conditions and day or night. SAR remote sensing on ocean and coastal monitoring has become a research hotspot in geoscience and remote sensing. This book—Progress in SAR Oceanography—provides an update of the current state of the science on ocean remote sensing with SAR. Overall, the book presents a variety of marine applications, such as, oceanic surface and internal waves, wind, bathymetry, oil spill, coastline and intertidal zone classification, ship and other man-made objects’ detection, as well as remotely sensed data assimilation. The book is aimed at a wide audience, ranging from graduate students, university teachers and working scientists to policy makers and managers. Efforts have been made to highlight general principles as well as the state-of-the-art technologies in the field of SAR Oceanography