Spectral–Spatial Classification for Hyperspectral Data Using Rotation Forests With Local Feature Extraction and Markov Random Fields

This paper presents a methodology of road feature extraction from the different resolutions of Remote Sensing images of Landsat-8 Operational Lander Image (OLI) and ResourceSat-2 of Linear Imaging Self Sensor-3 (LISS-3) and LISS-4 sensors with the spatial resolutions of 15 m, 24 m, and 5 m. In the methodology of road extraction, an index is proposed based on the spectral profile of Roads, also involving Morphological transform (Top-Hat or Bot-Hat) and Markov Random Fields (MRF). In the proposed index, Short Wave Infrared (SWIR) band has a significant role in the detection of roads from sensors, and it is named Normalized Difference Road Index (NDRI). To enhancement of features from the index, Bot-Hat transforms used. To segment the road features from this image, MRF used. The methodology is performed on the OLI, LISS-3 and LISS-4 images, and presented with results.

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Rotation invariant iris feature extraction using Gaussian Markov random fields with non-separable wavelet

Neurocomputing ◽

10.1016/j.neucom.2009.09.016 ◽

2010 ◽

Vol 73 (4-6) ◽

pp. 883-894 ◽

Cited By ~ 32

Author(s):

Jing Huang ◽

Xinge You ◽

Yuan Yuan ◽

Feng Yang ◽

Lin Lin

Keyword(s):

Feature Extraction ◽

Random Fields ◽

Markov Random Fields ◽

Rotation Invariant ◽

Gaussian Markov Random Fields ◽

Markov Random ◽

Iris Feature Extraction

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Model Selection in Markov Random Fields for High Spatial Resolution Hyperspectral Data

Handbook of Applied Spatial Analysis ◽

10.1007/978-3-642-03647-7_26 ◽

2009 ◽

pp. 545-563

Author(s):

Francesco Lagona

Keyword(s):

Model Selection ◽

Spatial Resolution ◽

Random Fields ◽

Markov Random Fields ◽

High Spatial Resolution ◽

Hyperspectral Data ◽

Markov Random

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Spectral-spatial destriping of hyperspectral image via correntropy based sparse representation and unidirectional Huber–Markov random fields

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691317500564 ◽

2017 ◽

Vol 15 (06) ◽

pp. 1750056 ◽

Cited By ~ 1

Author(s):

Yulong Wang ◽

Yuan Yan Tang ◽

Luoqing Li

Keyword(s):

Sparse Representation ◽

Random Fields ◽

Markov Random Fields ◽

Spatial Information ◽

Hyperspectral Image ◽

Hyperspectral Data ◽

Data Sets ◽

Spectral Correlation ◽

Markov Random ◽

Augmented Lagrange Multiplier

This paper presents a novel destriping method for hyperspectral images. Most of the previous destriping methods regard only the corrupted subimage as an isolated image and fail to consider the high spectral correlation between the subimages in different bands. This may impede their performance of removing striping noises. The proposed method takes advantage of both spectral and spatial information to contribute to the process of striping noise reduction. Firstly, a correntropy-based sparse representation (CSR) model is utilized to learn the high spectral correlation between the subimages in different bands. Then the spatial information of the target subimage with striping noise is incorporated into the CSR model with a unidirectional Huber–Markov random field prior. We devise an Augmented Lagrange Multiplier type of algorithm to efficiently compute the destriped results. The experimental results on two real-world hyperspectral data sets demonstrate the effectiveness of the proposed method.

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