Superpixel-Driven Optimized Wishart Network for Fast PolSAR Image Classification Using Global ${k}$ -Means Algorithm

Gadhiya, Tushar; Roy, Anil K.

doi:10.1109/tgrs.2019.2933483

Cited by 27 publications

(7 citation statements)

References 53 publications

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“…The method proposed by the team consists of a set of fully polarized SAR image preprocessing methods and a multi-scale deep network collaboration with superpixel constraints. This method uses Deeplab V3+ for pixel-level classification and simultaneously extracts local gradient ratio patterns from the original fully polarimetric SAR image, then performs weighted K-means [76] clustering to generate superpixels. Under the constraints of superpixels, the classification loss function is further optimized to improve the segmentation performance.…”

Section: First Place In the Semantic Segmentation In Fully Polarimetric Sarmentioning

confidence: 99%

Automated High-Resolution Earth Observation Image Interpretation: Outcome of the 2020 Gaofen Challenge

Sun

Wang

Yan

et al. 2021

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

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In this paper, we introduce the 2020 Gaofen Challenge and relevant scientific outcomes. The 2020 Gaofen Challenge is an international competition, which is organized by the China High-Resolution Earth Observation Conference Committee and the Aerospace Information Research Institute, Chinese Academy of Sciences and technically co-sponsored by the IEEE Geoscience and Remote Sensing Society (IEEE-GRSS) and the International Society for Photogrammetry and Remote Sensing (ISPRS). It aims at promoting the academic development of automated high-resolution earth observation image interpretation. Six independent tracks have been organized in this challenge, which cover the challenging problems in the field of object detection and semantic segmentation. With the development of convolutional neural networks, deep learning-based methods have achieved good performance on image interpretation. In this paper, we report the details and the best-performing methods presented so far in the scope of this challenge.

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Section: First Place In the Semantic Segmentation In Fully Polarimetric Sarmentioning

confidence: 99%

Automated High-Resolution Earth Observation Image Interpretation: Outcome of the 2020 Gaofen Challenge

Sun

Wang

Yan

et al. 2021

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

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“…Therefore, the training consumption could be decreased significantly. Similarly, a superpixel-driven optimized Wishart network was introduced in [433] for fast PolSAR image classification. In [434], the authors applied some tricks (such as BN, drop-out strategy) and concatenated ReLU to reduce computation cost of DL algorithm.…”

Section: Iv) Promoting the Real-time Or Reducing Computation Costsmentioning

confidence: 99%

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

Lang,

Fu,

Martorella

et al. 2020

Preprint

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Modern radar systems have high requirements in terms of accuracy, robustness and real-time capability when operating on increasingly complex electromagnetic environments. Traditional radar signal processing (RSP) methods have shown some limitations when meeting such requirements, particularly in matters of target classification. With the rapid development of machine learning (ML), especially deep learning, radar researchers have started integrating these new methods when solving RSP-related problems. This paper aims at helping researchers and practitioners to better understand the application of ML techniques to RSP-related problems by providing a comprehensive, structured and reasoned literature overview of ML-based RSP techniques. This work is amply introduced by providing general elements of ML-based RSP and by stating the motivations behind them. The main applications of ML-based RSP are then analysed and structured based on the application field. This paper then concludes with a series of open questions and proposed research directions, in order to indicate current gaps and potential future solutions and trends.

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“…To date, polarimetric SAR (PolSAR), as an advanced form of SAR, has provided abundant scattering information on observed land cover and targets. For civilian purposes, PolSAR can be used to monitor the growth of crops [1] and for the study of the Earth's resources [2], urban planning [3], mineral resource exploration [4], and disaster monitoring [5], while from a military perspective, PolSAR can be used to identify, detect and evaluate important strategic military targets [6]. Currently, several airborne and spaceborne platforms continuously provide an enormous amount of PolSAR data.…”

Section: Introductionmentioning

confidence: 99%

Efficient Superpixel Generation for Polarimetric SAR Images with Cross-Iteration and Hexagonal Initialization

Zou

Qin

et al. 2022

Remote Sensing

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Clustering-based methods of polarimetric synthetic aperture radar (PolSAR) image superpixel generation are popular due to their feasibility and parameter controllability. However, these methods pay more attention to improving boundary adherence and are usually time-consuming to generate satisfactory superpixels. To address this issue, a novel cross-iteration strategy is proposed to integrate various advantages of different distances with higher computational efficiency for the first time. Therefore, the revised Wishart distance (RWD), which has better boundary adherence but is time-consuming, is first integrated with the geodesic distance (GD), which has higher efficiency and more regular shape, to form a comprehensive similarity measure via the cross-iteration strategy. This similarity measure is then utilized alternately in the local clustering process according to the difference between two consecutive ratios of the current number of unstable pixels to the total number of unstable pixels, to achieve a lower computational burden and competitive accuracy for superpixel generation. Furthermore, hexagonal initialization is adopted to further reduce the complexity of searching pixels for relabelling in the local regions. Extensive experiments conducted on the AIRSAR, RADARSAT-2 and simulated data sets demonstrate that the proposed method exhibits higher computational efficiency and a more regular shape, resulting in a smooth representation of land cover in homogeneous regions and better-preserved details in heterogeneous regions.

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Superpixel-Driven Optimized Wishart Network for Fast PolSAR Image Classification Using Global ${k}$ -Means Algorithm

Cited by 27 publications

References 53 publications

Automated High-Resolution Earth Observation Image Interpretation: Outcome of the 2020 Gaofen Challenge

Automated High-Resolution Earth Observation Image Interpretation: Outcome of the 2020 Gaofen Challenge

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

Efficient Superpixel Generation for Polarimetric SAR Images with Cross-Iteration and Hexagonal Initialization

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