Unsupervised classification based on non‐negative eigenvalue decomposition and Wishart classifier

Wang, Chunle; Yu, Weidong; Wang, Robert; Deng, Yunkai; Zhao, Fengjun; Lu, Youchun

doi:10.1049/iet-rsn.2014.0076

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…Microwave radars are among the most important monitoring systems due to their all‐weather and all‐time observation characteristics and also due to acquiring high resolution images [1]. A new type of radar system is the polarimetric synthetic aperture radar (POLSAR), which is introduced as an advanced imaging instrument for remote sensing [2]. A POLSAR system acquires the backscattered energy of the land covers in the scene.…”

Section: Introductionmentioning

confidence: 99%

Spatial and polarimetric information fusion using residual network for polarimetric synthetic aperture radar image classification

Imani

2022

IET Radar Sonar & Navi

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A polarimetric synthetic aperture radar (POLSAR) system provides an image that can be considered as a data cube containing spatial information in two spatial dimensions and polarimetric information in the scattering dimension. A spatial and polarimetric residual network (SPRN) is proposed for POLSAR image classification. At first, polarimetric features are extracted from the scattering dimension through two designed polarimetric residual blocks. Then, the processed POLSAR cube is fed to two consecutive spatial residual blocks for contextual feature extraction. Three dimensional convolutional layers are used as basic layers for simultaneous extraction and fusion of polarimetric information and correlation among neighbouring pixels in local regions. The shortcut connections are utilised to overcome the degradation problem due to increasing network depth. In addition, batch normalisation is applied to regularise the learning process. The experimental results on four real POLSAR images show the superior performance of SPRN compared to several state‐of‐the‐art classifiers in terms of various assessment measures.

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Section: Introductionmentioning

confidence: 99%

Spatial and polarimetric information fusion using residual network for polarimetric synthetic aperture radar image classification

Imani

2022

IET Radar Sonar & Navi

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“…Lee et al applied Freeman-Durden three-component decomposition with the Wishart classifier to classify PolSAR data [13]. Wang et al adopted the non-negative eigenvalue decomposition for terrain and land-use classification [14]. Hong et al proposed a four-component decomposition and applied it to classify wetland vegetation types [15].…”

Section: Introductionmentioning

confidence: 99%

PolSAR Land Cover Classification Based on Roll-Invariant and Selected Hidden Polarimetric Features in the Rotation Domain

Tao

Chen

et al. 2017

Remote Sensing

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Abstract:Land cover classification is an important application for polarimetric synthetic aperture radar (PolSAR). Target polarimetric response is strongly dependent on its orientation. Backscattering responses of the same target with different orientations to the SAR flight path may be quite different. This target orientation diversity effect hinders PolSAR image understanding and interpretation. Roll-invariant polarimetric features such as entropy, anisotropy, mean alpha angle, and total scattering power are independent of the target orientation and are commonly adopted for PolSAR image classification. On the other aspect, target orientation diversity also contains rich information which may not be sensed by roll-invariant polarimetric features. In this vein, only using the roll-invariant polarimetric features may limit the final classification accuracy. To address this problem, this work uses the recently reported uniform polarimetric matrix rotation theory and a visualization and characterization tool of polarimetric coherence pattern to investigate hidden polarimetric features in the rotation domain along the radar line of sight. Then, a feature selection scheme is established and a set of hidden polarimetric features are selected in the rotation domain. Finally, a classification method is developed using the complementary information between roll-invariant and selected hidden polarimetric features with a support vector machine (SVM)/decision tree (DT) classifier. Comparison experiments are carried out with NASA/JPL AIRSAR and multi-temporal UAVSAR data. For AIRSAR data, the overall classification accuracy of the proposed classification method is 95.37% (with SVM)/96.38% (with DT), while that of the conventional classification method is 93.87% (with SVM)/94.12% (with DT), respectively. Meanwhile, for multi-temporal UAVSAR data, the mean overall classification accuracy of the proposed method is up to 97.47% (with SVM)/99.39% (with DT), which is also higher than the mean accuracy of 89.59% (with SVM)/97.55% (with DT) from the conventional method. The comparison studies clearly demonstrate the efficiency and advantage of the proposed classification methodology. In addition, the proposed classification method achieves better robustness for the multi-temporal PolSAR data. This work also further validates that added benefits can be gained for PolSAR data investigation by mining and utilization of hidden polarimetric information in the rotation domain.

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“…In particular, radar imaging systems such as high‐resolution radar and synthetic aperture radar (SAR) have been used in many automated applications as complements to optical imagery [1, 2]. For example, air and space‐based SAR algorithms have characterised agricultural land use [3], recognised types of urban structures/buildings [4], detected oil spills in large ocean area [5], constructed 3D models of the earth [6], enabled autonomous navigation in commercial automobiles [7], and distinguished types of aircraft, ships and ground vehicles [8–10] in automatic target recognition (ATR).…”

Section: Introductionmentioning

confidence: 99%

Commercial vehicle classification from spectrum parted linked image test‐attributed synthetic aperture radar imagery

Saville

Saini

Smith

2016

IET Radar, Sonar & Navigation

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Unsupervised classification based on non‐negative eigenvalue decomposition and Wishart classifier

Cited by 4 publications

References 14 publications

Spatial and polarimetric information fusion using residual network for polarimetric synthetic aperture radar image classification

Spatial and polarimetric information fusion using residual network for polarimetric synthetic aperture radar image classification

PolSAR Land Cover Classification Based on Roll-Invariant and Selected Hidden Polarimetric Features in the Rotation Domain

Commercial vehicle classification from spectrum parted linked image test‐attributed synthetic aperture radar imagery

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