Semisupervised Classification of PolSAR Image Incorporating Labels’ Semantic Priors

Hou, Biao; Guan, Jiaojiao; Wu, Qian; Jiao, Licheng

doi:10.1109/lgrs.2019.2953203

Cited by 11 publications

(11 citation statements)

References 16 publications

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“…Following the idea of increasing the diversity of classification, Wang et al [43] proposed a tri-training-based algorithm where three groups of QP-derived features were used to train three different classifiers. In recent semi-supervised classification studies, CNNs were employed to model the spatial dependency effect in the classification framework [44], [45]. A CNN classification network incorporates two semantic priors to preserve the spatial consistency and boundaries [44].…”

Section: Introductionmentioning

confidence: 99%

Local and Global Spatial Information for Land Cover Semisupervised Classification of Complex Polarimetric SAR Data

Ghanbari

Clausi

2023

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

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Each of the three satellites constituting the RADARSAT Constellation Mission (RCM) provide compact polarimetric synthetic aperture radar (CP SAR) data. The complex CP data have similar properties to the complex quad polarimetric (QP) data provided by prior RADARSAT missions. In this paper, a land cover classification method using spatial information is designed based on the statistical characteristics of the complex CP and QP SAR data. First, the local spatial dependency among pixels is captured by superpixels. Second, a graph is constructed on the superpixels to model the global spatial dependency among superpixels. The land cover classification image with land cover type labels is then estimated by propagating labels from the few labeled superpixels to the unlabeled superpixels. Classification of two RCM complex CP and QP scenes demonstrates that the proposed method, with few labeled pixels, provides much higher classification accuracy than methods which do not exploit global spatial dependency.

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Section: Introductionmentioning

confidence: 99%

Local and Global Spatial Information for Land Cover Semisupervised Classification of Complex Polarimetric SAR Data

Ghanbari

Clausi

2023

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

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“…This working mechanism brings polarization diversity, which can reveal different electromagnetic scattering characteristics of the earth terrain. Due to the superior quality in the acquisition of full polarization information [7], PolSAR images collected by airborne and satellite sensors have been widely used in many geo-science and remote sensing applications, such as disaster prediction, environmental monitoring, and other related fields [5], [8]- [12]. Hence, it is of great significance to interpret PolSAR images [13]- [16].…”

Section: Introductionmentioning

confidence: 99%

AIR-PolSAR-Seg: A Large-Scale Data Set for Terrain Segmentation in Complex-Scene PolSAR Images

Wang

Zeng

Yan

et al. 2022

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

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Polarimetric synthetic aperture radar (PolSAR) terrain segmentation is a fundamental research topic in PolSAR image interpretation. Recently, many studies have been investigated to handle this task. However, the existing data for PolSAR terrain segmentation have relatively limited scale and their scene complexity is relatively simple. These issues greatly restrict the development of algorithms. Therefore, there is a strong requirement for establishing a large-scale data set for terrain segmentation in complex-scene PolSAR images. In this paper, we present a benchmark data set containing a PolSAR amplitude image with a 9082×9805-pixel region and 2000 image patches with a size of 512×512 for PolSAR terrain segmentation, which is called AIR-PolSAR-Seg. We collect the PolSAR image with a resolution of 8 m from the GaoFen-3 satellite, and it is equipped with pixel-wise annotation which covers six categories. Compared with the previous data resources, AIR-PolSAR-Seg preserves some specific properties. First, AIR-PolSAR-Seg owns a large-size PolSAR image and provides a large quantity of image patches. It offers the research community a complete data resource with adequate training examples and reliable validation results. Second, AIR-PolSAR-Seg is established upon a PolSAR image with high scene complexity. This characteristic motivates robust and advanced segmentation approaches to facilitate complex-scene PolSAR image analysis. Based on AIR-PolSAR-Seg, three tasks are introduced: multi-category segmentation, water body segmentation, and building segmentation. Moreover, a performance analysis of traditional approaches and deep learning-based approaches are conducted, which can be regarded as baselines and provide references for future research.

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“…Pixels-based semi-supervised deep learning methods [9,[11][12][13] use individual pixels as the input. In [9], a graph-based model is proposed for semi-supervised deep learning.…”

Section: Introductionmentioning

confidence: 99%

“…A complex-valued GAN is proposed in [12] to deal with the problem of only a few labeled data are available. In [13], a semi-supervised classification method considers the semantic priors of labeled data. Besides, the algorithm considers both consistent regions and aligned boundaries.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Superpixels Based Semi-Supervised Similarity-Constrained CNN for PolSAR Image Classification

Guo

Sun

et al. 2020

Remote Sensing

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Recently, deep learning has been highly successful in image classification. Labeling the PolSAR data, however, is time-consuming and laborious and in response semi-supervised deep learning has been increasingly investigated in PolSAR image classification. Semi-supervised deep learning methods for PolSAR image classification can be broadly divided into two categories, namely pixels-based methods and superpixels-based methods. Pixels-based semi-supervised methods are liable to be affected by speckle noises and have a relatively high computational complexity. Superpixels-based methods focus on the superpixels and ignore tiny detail-preserving represented by pixels. In this paper, a Fuzzy superpixels based Semi-supervised Similarity-constrained CNN (FS-SCNN) is proposed. To reduce the effect of speckle noises and preserve the details, FS-SCNN uses a fuzzy superpixels algorithm to segment an image into two parts, superpixels and undetermined pixels. Moreover, the fuzzy superpixels algorithm can also reduce the number of mixed superpixels and improve classification performance. To exploit unlabeled data effectively, we also propose a Similarity-constrained Convolutional Neural Network (SCNN) model to assign pseudo labels to unlabeled data. The final training set consists of the initial labeled data and these pseudo labeled data. Three PolSAR images are used to demonstrate the excellent classification performance of the FS-SCNN method with data of limited labels.

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Semisupervised Classification of PolSAR Image Incorporating Labels’ Semantic Priors

Cited by 11 publications

References 16 publications

Local and Global Spatial Information for Land Cover Semisupervised Classification of Complex Polarimetric SAR Data

Local and Global Spatial Information for Land Cover Semisupervised Classification of Complex Polarimetric SAR Data

AIR-PolSAR-Seg: A Large-Scale Data Set for Terrain Segmentation in Complex-Scene PolSAR Images

Fuzzy Superpixels Based Semi-Supervised Similarity-Constrained CNN for PolSAR Image Classification

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