An Eigenvalue-Based Approach for Structure Classification in Polarimetric SAR Images

Abstract

In this paper, we design a novel unsupervised architecture for automatic classification of the dominant polarization in polarimetric SAR images. To this end, we leverage the ideas developed in [1] and suitably exploit them to build a decision logic capable of recognizing the dominant scattering mechanism which characterizes the pixel under test. Specifically, we combine the original data to generate three different sets of reducedsize vectors, which feed a dominant eigenvalues classifier based upon the Model Order Selection rules. Then, the outputs of the latter classification schemes are exploited to infer, according to a specific criterion, the dominant polarization. The performance analysis is conducted on measured data and points out the effectiveness of the newly proposed classification architecture also showing that information about the dominant polarization can be representative of the type of structure which gives raise to the dominant backscattering mechanism.

Angle Estimation for MIMO Radar in the Presence of Gain-Phase Errors with One Instrumental Tx/Rx Sensor: A Theoretical and Numerical Study

“…Unlike the traditional matrix decomposition, PARAFAC decomposition is usually unique. For the model in (5), it was pointed out that if the Kruskal ranks of A r , A t and B (denoted by kr A r , kr A t and kr(B), respectively) fulfill [10] kr A r + kr A t + kr(B) ≥ 2K + 2 (13) then the estimations of A r , A t , and B are unique with permutations and scaling effect, i.e.,…”

“…Array radar is one of the most important techniques in remote sensing [1][2][3][4][5]. As a branch of active array radar, multiple-input multiple-output (MIMO) radar has gained extensive interest owing to the capability of waveform diversity (it emits mutual orthogonal waveforms using sensor array and receives the echoes using sensor array).…”