Optimal use of polarimetric signature on PALSAR-2 data for land cover classification

Phartiyal, Gopal Singh; Kumar, Krishan; Singh, Dharmendra; Singh, Kumar Hemant

doi:10.1109/igarss.2017.8128016

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…1. Construct two-dimensional feature space: 16: Construct three-dimensional feature space: 26: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…V. Thakur et al developed a decision tree based on separability index to classify ALOS-PALSAR data [25]. And G. S. Phartiyal et al attempt to analyze the polarimetric signature to decide the individual class boundary values which will help in building a decision tree based classification technique [26]. Those above methods focus on the feature selection or optimization before decision tree algorithm, not on the improvement on the tree nodes.…”

Section: Introductionmentioning

confidence: 99%

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Interpretable POLSAR Image Classification Based on Adaptive-Dimension Feature Space Decision Tree

et al. 2020

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Decision tree method has been applied to POLSAR image classification, due to its capability to interpret the scattering characteristics as well as good classification accuracy. Compared with popular machine learning classifiers, decision tree approach can explain the scattering process of certain type of targets by use of the polarimetric features at the tree nodes. Except the interpretability, decision tree approach could be transplanted to other data set without training process for the same terrain types, since the polarimetric features are inherently connected to the physical scattering properties. Currently, decision tree based classifiers, typically employ one single polarimetric feature at the nodes of the tree. The idea to increase the number of the polarization features at the decision tree node is expected to improve the classification result, which combine two or more polarimetric features to form a two or high dimension feature space. In this way, the classes which cannot be discriminated with one feature could possibly be separated with the space constructed by several features. However, it also inevitably leads to an increase in the computational burden. In fact, not all nodes require very high-dimensional feature space to achieve high classification precision. Therefore, in this paper we proposed that the dimension of the feature space used in the decision tree nodes is adaptively changed from one to three, due to the separability of the classes under this node. The developed classification method is examined by the classical AIRSAR data in Flevoland area of the Netherlands, as well as GaoFen-3 data in Hulunbuir of China. The experiments show that the classification performance is superior to the fixed dimension feature decision tree methods, with less and reasonable computation time. Besides, the transferability of polarimetric features obtained by decision tree is preliminarily demonstrated in the application to another AIRSAR data.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Interpretable POLSAR Image Classification Based on Adaptive-Dimension Feature Space Decision Tree

et al. 2020

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“…Derived PolSAR features are also used in this study. Polarization signatures (PSs) are computed from the single look complex PolSAR data with procedure similar to Phartiyal et al (2017). Further, polarization signatures correlation features (PSCFs) are computed from the PSs using procedure similar to as explained in Phartiyal, Kumar, and Singh (2020).…”

Section: Datasetmentioning

confidence: 99%

Permuted Spectral and Permuted Spectral-Spatial CNN Models for PolSAR-Multispectral Data based Land Cover Classification

Singh

Bordu

Singh

et al. 2020

International Journal of Remote Sensing

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It is a challenge to develop methods which can process the PolSAR and multispectral (MS) data modalities together without losing information from either for remote sensing applications. This paper presents a study which attempts to introduce novel deep learning based remote sensing data processing frameworks that utilizes convolutional neural networks (CNNs) in both spatial and spectral domains to perform land cover (LC) classification with PolSAR-MS data. Also since earth observation remotely sensed data have usually larger spectral depth than normal camera image data, exploiting the spectral information in remote sensing (RS) data is crucial as well. In fact, convolutions in the subspectral space are intuitive and alternative to the process of feature selection. Recently, researchers have gained success in exploiting the spectral information of RS data, especially the hyperspectral data with CNNs. In this paper, exploitation of the spectral information in the PolSAR-MS data via a permuted localized spectral convolution along with localized spatial convolution is proposed. Further, the study in this paper also establishes the significance of performing permuted localized spectral convolutions over non-localized or localized spectral convolutions. Two models are proposed, namely a permuted local spectral convolutional network (Perm-LS-CNN) and a permuted local spectral-spatial convolutional network (Perm-LSS-CNN). These models are trained on ground truth class data points measured directly on the terrain. The evaluation of the generalization performance is done using ground truth knowledge on selected well known regions in the study areas. Comparison with other popular machine learning classifiers shows that the Perm-LSS-CNN model provides better classification results in terms of both accuracy and generalization.

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Improved mapping of flood affected villages in India: a novel three-stage approach using PolSAR polarization signatures and ensembled dilated CNNs

Phartiyal,

Singh,

Yahia

2023

International Journal of Remote Sensing

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Optimal use of polarimetric signature on PALSAR-2 data for land cover classification

Cited by 4 publications

References 14 publications

Interpretable POLSAR Image Classification Based on Adaptive-Dimension Feature Space Decision Tree

Interpretable POLSAR Image Classification Based on Adaptive-Dimension Feature Space Decision Tree

Permuted Spectral and Permuted Spectral-Spatial CNN Models for PolSAR-Multispectral Data based Land Cover Classification

Improved mapping of flood affected villages in India: a novel three-stage approach using PolSAR polarization signatures and ensembled dilated CNNs

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