Urban Area Extraction Using X-Band Fully Polarimetric SAR Imagery

Susaki, Junichi; Kishimoto, Masaaki

doi:10.1109/jstars.2016.2526626

Cited by 7 publications

(7 citation statements)

References 17 publications

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“…Among these data, Rs2-Sanf, Rs2-Flev and AS-Sanf data are commonly used datasets. The results of comparison methods [46][47] on these typical data sets are comparable with the results reported in the corresponding papers [47] [48]. This shows that the comparisons are objective.…”

Section: B Esc-pu-learning Ba Extraction Performancessupporting

confidence: 77%

Eigenvalue Statistical Components-Based PU-Learning for PolSAR Built-Up Areas Extraction and Cross-Domain Analysis

Gui

Wang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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PolSAR provides important support for built-up areas (BA) information analysis, due to the ability of weather independent imaging and sensitivity to targets scattering and geometric characteristics. However, PolSAR BA with large orientation angles are usually misdetected as vegetation, and labeled BA samples with special orientations are difficult to obtain. Furthermore, the labeled BA samples and trained models can hardly work well in cross-domain PolSAR imagery BA analysis. This paper presents a PolSAR BA extraction method based on eigenvalue statistical components (ESC) and PU-Learning (PUL), and it helps to realize cross-domain BA extraction by combining subspace alignment (SA). Firstly, the roll invariance of coherency-matrix eigenvalues and building orientation effects are analyzed. Then, by adopting eigenvalue-Wishart unsupervised classification, regional statistical information and rotation invariant property are comprehensively utilized in ESC. Finally, the BA can be extracted by combining PUL classifier with only positive samples at same distinguishable orientation. Combined with SA, the novel ESC-PUL-SA domain adaptation facilitates robust unsupervised cross-domain PolSAR BA analysis, reducing the differences caused by sensors and imaging scenes. The ESC-PUL BA extraction on seven PolSAR imageries showed that the accuracies reach 92-98% with only a few positive samples (less than 0.65%). The ESC-PUL-SA performance was further validated by 14 unsupervised cross-domain BA analysis units among ten datasets, including Radarsat-2, Gaofen-3, AirSAR and UAVSAR images. With randomly selected positive samples from source domain, the proposed ESC-PUL-SA achieved accuracies of all cross-domain BA extraction range from 89.64-95.53%.

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Section: B Esc-pu-learning Ba Extraction Performancessupporting

confidence: 77%

Eigenvalue Statistical Components-Based PU-Learning for PolSAR Built-Up Areas Extraction and Cross-Domain Analysis

Gui

Wang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…In comparison to the single-polarimetric SAR system, a fully polarimetric SAR system can capture more scattering information from ground objects through four polarimetric modes, which can produce a 2 × 2 scattering matrix instead of complex-valued data. The advantages of PolSAR systems have led to their widespread application in various fields, such as military monitoring [2], object detection [3], crop growth prediction [4], and terrain classification [5]. One particular task related to PolSAR is image classification by assigning a class label to each pixel.…”

Section: Introductionmentioning

confidence: 99%

Polarimetric Synthetic Aperture Radar Image Classification Based on Double-Channel Convolution Network and Edge-Preserving Markov Random Field

Shi,

Nie,

et al. 2023

Remote Sensing

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Deep learning methods have gained significant popularity in the field of polarimetric synthetic aperture radar (PolSAR) image classification. These methods aim to extract high-level semantic features from the original PolSAR data to learn the polarimetric information. However, using only original data, these methods cannot learn multiple scattering features and complex structures for extremely heterogeneous terrain objects. In addition, deep learning methods always cause edge confusion due to the high-level features. To overcome these limitations, we propose a novel approach that combines a new double-channel convolutional neural network (CNN) with an edge-preserving Markov random field (MRF) model for PolSAR image classification, abbreviated to “DCCNN-MRF”. Firstly, a double-channel convolution network (DCCNN) is developed to combine complex matrix data and multiple scattering features. The DCCNN consists of two subnetworks: a Wishart-based complex matrix network and a multi-feature network. The Wishart-based complex matrix network focuses on learning the statistical characteristics and channel correlation, and the multi-feature network is designed to learn high-level semantic features well. Then, a unified network framework is designed to fuse two kinds of weighted features in order to enhance advantageous features and reduce redundant ones. Finally, an edge-preserving MRF model is integrated with the DCCNN network. In the MRF model, a sketch map-based edge energy function is designed by defining an adaptive weighted neighborhood for edge pixels. Experiments were conducted on four real PolSAR datasets with different sensors and bands. The experimental results demonstrate the effectiveness of the proposed DCCNN-MRF method.

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“…Building extraction has attracted continuous attention since it plays an important role in damage assessment, population estimation, city expansion, and other remote sensing applications in the military and civilian fields [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. Polarimetric synthetic aperture radar (PolSAR) is an active, high-resolution and multimode operating radar system, which provides abundant information for quick remote sensing in almost all weather and solar illumination conditions.…”

Section: Introductionmentioning

confidence: 99%

“…Azmedroub et al [4] applied the Yamaguchi four-component decomposition and circular polarimetric covariance matrix to detect buildings. Susaki et al [5] utilized the volume scattering power, the total power, and co-polarization coherence to discriminate buildings. On this basis, Kajimoto et al [6] replaced the co-polarization coherence with the POA randomness to further discriminate buildings.…”

Section: Introductionmentioning

confidence: 99%

Refined Model-Based and Feature-Driven Extraction of Buildings from PolSAR Images

et al. 2019

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Polarimetric synthetic aperture radar (PolSAR) building extraction plays an important role in urban planning, disaster management, etc. In this paper, a building extraction method using refined model-based decomposition and robust scattering feature is proposed. On the one hand, the newly proposed refined five-component decomposition and its derived scattering powers are applied to detect the buildings. On the other hand, by combining the matrix elements and co-polarization correlation coefficient, a robust feature is proposed to discriminate buildings and non-buildings. Both these two preliminary extraction results are obtained through thresholding segmentation. Finally, they are fused via the HX Markov random fields so as to further improve the extraction accuracy. The performance of the proposed method is demonstrated and evaluated with Gaofen-3 and uninhabited aerial vehicle SAR full PolSAR data over different test sites. Outputs show that the proposed method outperforms other state-of-the-art methods and provides an overall accuracy of over 90%.

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Urban Area Extraction Using X-Band Fully Polarimetric SAR Imagery

Cited by 7 publications

References 17 publications

Eigenvalue Statistical Components-Based PU-Learning for PolSAR Built-Up Areas Extraction and Cross-Domain Analysis

Eigenvalue Statistical Components-Based PU-Learning for PolSAR Built-Up Areas Extraction and Cross-Domain Analysis

Polarimetric Synthetic Aperture Radar Image Classification Based on Double-Channel Convolution Network and Edge-Preserving Markov Random Field

Refined Model-Based and Feature-Driven Extraction of Buildings from PolSAR Images

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