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| ToDo | #acl BecariosGrupo:read,write,revert All:read #acl Default All:read == Fisher Vectors for PolSAR Image Classification == === Abstract === In this letter we study the application of the Fisher Vector (FV) to the problem of pixel-wise supervised classification of PolSAR images. This is a challenging problem since information in those images is encoded as complex-valued covariance matrices. We observe that the real part of these matrices preserve the positive semidefiniteness property of their complex counterpart. Based on this observation, we derive a FV from a mixture of real Wishart pdfs and integrate it with a Potts-like energy model in order to capture spatial dependencies between neighboring regions. Experimental results on two challenging datasets show the effectiveness of the approach. === Paper === Javier Redolfi, Jorge Sánchez, and Ana Georgina Flesia<<BR>> '''"Fisher Vectors for PolSAR Image Classification"'''<<BR>> IEEE Geoscience and Remote Sensing Letters (Accepted with Major Changes) === Code === === Dataset === For evaluation, we consider a subset of the images that were available trough [[https://earth.esa.int/web/polsarpro | PolSARpro]] by the European Space Agency (ESA). This subset consists of two fully polarimetric images over the San Francisco Bay (SFB) area, USA, and over an agricultural region in the Flevoland (FL) province in The Netherlands. ==== San Francisco Bay ==== Pseudocolor representation of the polarimetric data (left) and the ground truth labels (right). [[attachment:sfb.png]] ==== Flevoland ==== [[attachment:flev.png]] === Classification Examples === * As a second contribution, we have made available a set of ground truth annotations and a well defined training/testing procedure based on two popular datasets found in the literature. * To facilitate reproducibility, data and scripts are made available at the project website. * We also make classification results (qualitative) available trough the project website along with the scripts and a detailed explanation on how to generate them. |
Fisher Vectors for PolSAR Image Classification
Abstract
In this letter we study the application of the Fisher Vector (FV) to the problem of pixel-wise supervised classification of PolSAR images. This is a challenging problem since information in those images is encoded as complex-valued covariance matrices. We observe that the real part of these matrices preserve the positive semidefiniteness property of their complex counterpart. Based on this observation, we derive a FV from a mixture of real Wishart pdfs and integrate it with a Potts-like energy model in order to capture spatial dependencies between neighboring regions. Experimental results on two challenging datasets show the effectiveness of the approach.
Paper
Javier Redolfi, Jorge Sánchez, and Ana Georgina Flesia
"Fisher Vectors for PolSAR Image Classification"
IEEE Geoscience and Remote Sensing Letters (Accepted with Major Changes)
Code
Dataset
For evaluation, we consider a subset of the images that were available trough PolSARpro by the European Space Agency (ESA). This subset consists of two fully polarimetric images over the San Francisco Bay (SFB) area, USA, and over an agricultural region in the Flevoland (FL) province in The Netherlands.
San Francisco Bay
Pseudocolor representation of the polarimetric data (left) and the ground truth labels (right).
Flevoland
Classification Examples
- As a second contribution, we have made available a set of ground truth annotations and a well defined training/testing procedure based on two popular datasets found in the literature.
- To facilitate reproducibility, data and scripts are made available at the project website.
- We also make classification results (qualitative) available trough the project website along with the scripts and a detailed explanation on how to generate them.
