no code implementations • 16 Jan 2023 • Pia Addabbo, Nicomino Fiscante, Gaetano Giunta, Danilo Orlando, Giuseppe Ricci, Silvia Liberata Ullo
Hyperspectral target detection is a task of primary importance in remote sensing since it allows identification, location, and discrimination of target features.
no code implementations • 8 Jul 2022 • Linjie Yan, Sudan Han, Chengpeng Hao, Danilo Orlando, Giuseppe Ricci
The joint adaptive detection of multiple point-like targets in scenarios characterized by different clutter types is still an open problem in the radar community.
no code implementations • 11 May 2022 • Pia Addabbo, Danilo Orlando, Giuseppe Ricci, Louis L. Scharf
If, instead, the signal subspace is known only by its dimension, the performance of GLR and EP detectors is very similar.
no code implementations • 23 Mar 2022 • Angelo Coluccia, Alessio Fascista, Giuseppe Ricci
The paper addresses the design of adaptive radar detectors having desired behavior, in Gaussian disturbance with unknown statistics.
no code implementations • 18 Aug 2021 • Angelo Coluccia, Danilo Orlando, Giuseppe Ricci
In this paper, we propose a new solution for the detection problem of a coherent target in heterogeneous environments.
no code implementations • 5 Jul 2021 • Danilo Orlando, Giuseppe Ricci, Louis L. Scharf
The noises in these two channels share a common covariance matrix, up to a scale, which may be known or unknown.
no code implementations • 21 Jun 2021 • Pia Addabbo, Filippo Biondi, Carmine Clemente, Sudan Han, Danilo Orlando, Giuseppe Ricci
This paper addresses the challenge of classifying polarimetric SAR images by leveraging the peculiar characteristics of the polarimetric covariance matrix (PCM).
no code implementations • 17 Sep 2020 • Pia Addabbo, Jun Liu, Danilo Orlando, Giuseppe Ricci
In this work, we develop and compare two innovative strategies for parameter estimation and radar detection of multiple point-like targets.
no code implementations • 17 Apr 2020 • Pia Addabbo, Sudan Han, Danilo Orlando, Giuseppe Ricci
The classification procedure relies on a model for the observables including latent variables that is solved by the expectation-maximization algorithm.