Optimized Wishart Network for an Efficient Classification of Multifrequency PolSAR Data

Gadhiya, Tushar; Roy, Anil K.

doi:10.1109/lgrs.2018.2861081

Cited by 12 publications

(6 citation statements)

References 13 publications

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“…For parity in comparison with our results we have calculated the overall accuracy of the proposed method using ground truth of 7, 14 as well as 15 classes. We observe from Table 4 that the proposed method outperforms all the three methods [2,3,16].…”

Section: Methodsmentioning

confidence: 90%

“…Comparison of overall accuracies is reported in Table 4. ANN [2] and OWN [3] have used small subset of Flevoland dataset containing 7 classes. On the other hand Stein-SRC [16] used ground truth with 14 classes.…”

Section: Methodsmentioning

confidence: 99%

“…It uses Kronecker product of eigenvalues of coherency matrix to combine multiple bands information. Recently, an Optimized Wishart Network (OWN) for classification of multifrequency PolSAR data is reported [3].…”

Section: Introductionmentioning

confidence: 99%

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Stacked Autoencoder Based Feature Extraction and Superpixel Generation for Multifrequency PolSAR Image Classification

Gadhiya

Tangirala

Roy

2019

Lecture Notes in Computer Science

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In this paper we are proposing classification algorithm for multifrequency Polarimetric Synthetic Aperture Radar (PolSAR) image. Using PolSAR decomposition algorithms 33 features are extracted from each frequency band of the given image. Then, a two-layer autoencoder is used to reduce the dimensionality of input feature vector while retaining useful features of the input. This reduced dimensional feature vector is then applied to generate superpixels using simple linear iterative clustering (SLIC) algorithm. Next, a robust feature representation is constructed using both pixel as well as superpixel information. Finally, softmax classifier is used to perform classification task. The advantage of using superpixels is that it preserves spatial information between neighbouring PolSAR pixels and therefore minimizes the effect of speckle noise during classification. Experiments have been conducted on Flevoland dataset and the proposed method was found to be superior to other methods available in the literature.

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Section: Methodsmentioning

confidence: 90%

Section: Methodsmentioning

confidence: 99%

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Stacked Autoencoder Based Feature Extraction and Superpixel Generation for Multifrequency PolSAR Image Classification

Gadhiya

Tangirala

Roy

2019

Lecture Notes in Computer Science

Self Cite

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“…The result of these studies showed that by applying deep learning approaches it is possible to obtain high order features or more accurate results [29,30,59,76,[82][83][84][85][86][87]. However, there are some studies showing that the current methods are better than deep learning or give the same result, concluding that there is no value in applying complex structures [23,31,88,90,91]. Sometimes simple models that are formulated by carefully selecting the best estimators and then by examining a specific situation they give better results than complex models [70].…”

Section: Discussionmentioning

confidence: 99%

Smart Farming Becomes Even Smarter With Deep Learning—A Bibliographical Analysis

Ünal

2020

IEEE Access

100

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Smart farming is a new concept that makes agriculture more efficient and effective by using advanced information technologies. The latest advancements in connectivity, automation, and artificial intelligence enable farmers better to monitor all procedures and apply precise treatments determined by machines with superhuman accuracy. Farmers, data scientists and, engineers continue to work on techniques that allow optimizing the human labor required in farming. With valuable information resources improving day by day, smart farming turns into a learning system and becomes even smarter. Deep learning is a type of machine learning method, using artificial neural network principles. The main feature by which deep learning networks are distinguished from neural networks is their depth and that feature makes them capable of discovering latent structures within unlabeled, unstructured data. Deep learning networks that do not need human intervention while performing automatic feature extraction have a significant advantage over previous algorithms. The focus of this study is to explore the advantages of using deep learning in agricultural applications. This bibliography reviews the potential of using deep learning techniques in agricultural industries. The bibliography contains 120 papers from the database of the Science Citation Index on the subject that were published between 2016 and 2019. These studies have been retrieved from 39 scientific journals. The papers are classified into the following categories as disease detection, plant classification, land cover identification, precision livestock farming, pest recognition, object recognition, smart irrigation, phenotyping, and weed detection.

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“…In [354], the authors proposed an ensemble transfer learning framework to incorporate manifold polarimetric decompositions into a DCNN to jointly extract the spatial and polarimetric information of PolSAR image for classification. In order to effectively classify single-frequency and multifrequency PolSAR data, the authors proposed a single-hidden layer optimized Wishart network (OWN) and extended OWN, respectively in [356], which outperformed DL-based architecture involving multiple hidden layers. To exploit the spatial information between pixels on PolSAR images and preserve the local structure of data, a new DNN based on sparse filtering and manifold regularization (DSMR) was proposed for feature extraction and classification of PolSAR data in [364].…”

Section: A Sar Images Processingmentioning

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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Optimized Wishart Network for an Efficient Classification of Multifrequency PolSAR Data

Cited by 12 publications

References 13 publications

Stacked Autoencoder Based Feature Extraction and Superpixel Generation for Multifrequency PolSAR Image Classification

Stacked Autoencoder Based Feature Extraction and Superpixel Generation for Multifrequency PolSAR Image Classification

Smart Farming Becomes Even Smarter With Deep Learning—A Bibliographical Analysis

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

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