A PolSAR Scattering Power Factorization Framework and Novel Roll-Invariant Parameter-Based Unsupervised Classification Scheme Using a Geodesic Distance

Total Power ◽

Invariant Parameter ◽

Decomposition Schemes ◽

Type Parameter ◽

Power Component

© 1980-2012 IEEE. We propose a generic scattering power factorization framework (SPFF) for polarimetric synthetic aperture radar (PolSAR) data to directly obtain N scattering power components along with a residue power component for each pixel. Each scattering power component is factorized into similarity (or dissimilarity) using elementary targets and a generalized volume model. The similarity measure is derived using a geodesic distance between pairs of 4× 4 real Kennaugh matrices. In standard model-based decomposition schemes, the 3× 3 Hermitian-positive semi-definite covariance (or coherency) matrix is expressed as a weighted linear combination of scattering targets following a fixed hierarchical process. In contrast, under the proposed framework, a convex splitting of unity is performed to obtain the weights while preserving the dominance of the scattering components. The product of the total power (Span) with these weights provides the nonnegative scattering power components. Furthermore, the framework, along with the geodesic distance (GD) is effectively used to obtain specific roll-invariant parameters such as scattering-type parameter (αGD), helicity parameter (τ GD), and purity parameter (PGD). A PGD/αGD unsupervised classification scheme is also proposed for PolSAR images. The SPFF, the roll invariant parameters, and the classification results are assessed using C-band RADARSAT-2 and L-band ALOS-2 images of San Francisco.

A PolSAR Scattering Power Factorization Framework and Novel Roll-Invariant Parameter-Based Unsupervised Classification Scheme Using a Geodesic Distance

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2019.2957514 ◽

2020 ◽

Vol 58 (5) ◽

pp. 3509-3525 ◽

Cited By ~ 1

Author(s):

Debanshu Ratha ◽

Eric Pottier ◽

Avik Bhattacharya ◽

Alejandro C. Frery

Keyword(s):

Classification Scheme ◽

Geodesic Distance ◽

Invariant Parameter

Unsupervised Classification of Polarimetric SAR Image Based on Geodesic Distance and Non-Gaussian Distribution Feature

Sensors ◽

10.3390/s21041317 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1317

Author(s):

Junrong Qu ◽

Xiaolan Qiu ◽

Chibiao Ding ◽

Bin Lei

Keyword(s):

Geodesic Distance ◽

Image Interpretation ◽

Wishart Distribution ◽

Surface Scattering ◽

Initial Segmentation ◽

L Band ◽

Non Gaussian

Polarimetric synthetic aperture radar (PolSAR) image classification plays a significant role in PolSAR image interpretation. This letter presents a novel unsupervised classification method for PolSAR images based on the geodesic distance and K-Wishart distribution. The geodesic distance is obtained between the Kennaugh matrices of the observed target and canonical targets, and it is further utilized to define scattering similarity. According to the maximum scattering similarity, initial segmentation is produced, and the image is divided into three main categories: surface scattering, double-bounce scattering, and random volume scattering. Then, using the shape parameter α of K-distribution, each scattering category is further divided into three sub-categories with different degrees of heterogeneity. Finally, the K-Wishart maximum likelihood classifier is applied iteratively to update the results and improve the classification accuracy. Experiments are carried out on three real PolSAR images, including L-band AIRSAR, L-band ESAR, and C-band GaoFen-3 datasets, containing different resolutions and various terrain types. Compared with four other classic and recently developed methods, the final classification results demonstrate the effectiveness and superiority of the proposed method.

A Classification Scheme for Sediments and Habitats on Exposed Intertidal Flats with Multi-Frequency Polarimetric SAR

Remote Sensing ◽

10.3390/rs13030360 ◽

2021 ◽

Vol 13 (3) ◽

pp. 360

Author(s):

Wensheng Wang ◽

Martin Gade ◽

Kerstin Stelzer ◽

Jörn Kohlus ◽

Xinyu Zhao ◽

...

Keyword(s):

Classification Scheme ◽

Wadden Sea ◽

Relative Difference ◽

Intertidal Flats ◽

Sediment Types ◽

X Band ◽

Good Potential ◽

L Band ◽

Sar Data

We developed an extension of a previously proposed classification scheme that is based upon Freeman–Durden and Cloude–Pottier decompositions of polarimetric Synthetic Aperture Radar (SAR) data, along with a Double-Bounce Eigenvalue Relative Difference (DERD) parameter, and a Random Forest (RF) classifier. The extension was done, firstly, by using dual-copolarization SAR data acquired at shorter wavelengths (C- and X-band, in addition to the previously used L-band) and, secondly, by adding indicators derived from the (polarimetric) Kennaugh elements. The performance of the newly developed classification scheme, herein abbreviated as FCDK-RF, was tested using SAR data of exposed intertidal flats. We demonstrate that the FCDK-RF scheme is capable of distinguishing between different sediment types, namely mud and sand, at high spatial accuracies. Moreover, the classification scheme shows good potential in the detection of bivalve beds on the exposed flats. Our results show that the developed FCDK-RF scheme can be applied for the mapping of sediments and habitats in the Wadden Sea on the German North Sea coast using multi-frequency and multi-polarization SAR from ALOS-2 (L-band), Radarsat-2 (C-band) and TerraSAR-X (X-band).

Unsupervised classification of snowflake images using a generative adversarial network and <i>K</i>-medoids classification

Atmospheric Measurement Techniques ◽

10.5194/amt-13-2949-2020 ◽

2020 ◽

Vol 13 (6) ◽

pp. 2949-2964

Author(s):

Jussi Leinonen ◽

Alexis Berne

Keyword(s):

Classification Scheme ◽

Feature Space ◽

Automated Classification ◽

Generative Adversarial Network ◽

Microphysical Properties ◽

Adversarial Network ◽

Computationally Intensive ◽

Comparison Of The Results ◽

Supervised Classification Methods

Abstract. The increasing availability of sensors imaging cloud and precipitation particles, like the Multi-Angle Snowflake Camera (MASC), has resulted in datasets comprising millions of images of falling snowflakes. Automated classification is required for effective analysis of such large datasets. While supervised classification methods have been developed for this purpose in recent years, their ability to generalize is limited by the representativeness of their labeled training datasets, which are affected by the subjective judgment of the expert and require significant manual effort to derive. An alternative is unsupervised classification, which seeks to divide a dataset into distinct classes without expert-provided labels. In this paper, we introduce an unsupervised classification scheme based on a generative adversarial network (GAN) that learns to extract the key features from the snowflake images. Each image is then associated with a distribution of points in the feature space, and these distributions are used as the basis of K-medoids classification and hierarchical clustering. We found that the classification scheme is able to separate the dataset into distinct classes, each characterized by a particular size, shape and texture of the snowflake image, providing signatures of the microphysical properties of the snowflakes. This finding is supported by a comparison of the results to an existing supervised scheme. Although training the GAN is computationally intensive, the classification process proceeds directly from images to classes with minimal human intervention and therefore can be repeated for other MASC datasets with minor manual effort. As the algorithm is not specific to snowflakes, we also expect this approach to be relevant to other applications.

Modified Hybrid Freeman/Eigenvalue Decomposition for Polarimetric SAR Data

Journal of Sensors ◽

10.1155/2015/168720 ◽

2015 ◽

Vol 2015 ◽

pp. 1-7 ◽

Cited By ~ 1

Author(s):

Shuang Zhang ◽

Shuang Wang ◽

Bo Chen

Keyword(s):

Synthetic Aperture Radar ◽

San Francisco ◽

Decomposition Method ◽

Unitary Transformation ◽

Synthetic Aperture ◽

Eigenvalue Decomposition ◽

Reflection Symmetry ◽

Polarimetric Sar ◽

Airborne Sensors

Because of the rapid advancement of the airborne sensors and spaceborne sensors, large volumes of fully polarimetric synthetic aperture radar (PolSAR) data are available, but they are too complex to interpret difficultly. In this paper, a modified hybrid Freeman/eigenvalue decomposition method for the coherency matrix derived from the fully PolSAR sensors is proposed. The proposed modified hybrid Freeman/eigenvalue decomposition uses a real unitary transformation on the coherency matrix to release correlations between the copolarized term and cross polarized term, and the scattering models are derived from eigenvectors of the coherency matrix with reflection symmetry condition. The anisotropy and entropy are used to determine whether the volume scattering component is derived from the man-made structures or not. Moreover, the scattering powers from the proposed hybrid Freeman/eigenvalue decomposition are all nonnegative values. Fully PolSAR data on San Francisco acquired by AIRSAR sensor are used in the experiments to prove the efficacy of the proposed decomposition.

Fuzzy Support Vector Machine for PolSAR Image Classification

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.639-640.1162 ◽

2013 ◽

Vol 639-640 ◽

pp. 1162-1167 ◽

Cited By ~ 1

Author(s):

Hong Xia Ke ◽

Guo Dong Liu ◽

Guo Bing Pan

Keyword(s):

Support Vector Machine ◽

Image Classification ◽

Image Data ◽

Original Data ◽

Total Power ◽

Support Vector ◽

Fuzzy Support Vector Machine ◽

Automatic Categorization ◽

Svm Algorithm

Fully Polarimetric Synthetic Aperture Radar (PolSAR) image classification, with the complexity for its data’s scattering mechanism and statistical property, has expected to be performed by an automatic categorization. This paper presents a supervised method called Fuzzy support vector machine (FSVM), which is a variant of the SVM algorithm to classify the PolSAR image data. In order to take advantages of PolSAR data, five scattering features (entropy, total power, three Eigenvalues of Coherent Matrix: λ1,λ2,λ3) are input as original data space of the FSVM algorithm. The feasibility of this approach is examined by the JPL/AIRSAR PolSAR data. The classification results show that the proposed FSVM method has out-performed the SVM method.