Classification of Sea Ice Based on Fuzzy Clustering with Spatial Information

<p><strong>Abstract.</strong> This paper describes a new framework for classification of hyperspectral images, based on both spectral and spatial information. The spatial information is obtained by an enhanced Marker-based Hierarchical Segmentation (MHS) algorithm. The hyperspectral data is first fed into the Multi-Layer Perceptron (MLP) neural network classification algorithm. Then, the MHS algorithm is applied in order to increase the accuracy of less-accurately classified land-cover types. In the proposed approach, the markers are extracted from the classification maps obtained by MLP and Support Vector Machines (SVM) classifiers. Experimental results on Washington DC Mall hyperspectral dataset, demonstrate the superiority of proposed approach compared to the MLP and the original MHS algorithms.</p>

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Sparse Subspace Clustering in Hyperspectral Images using Incomplete Pixels

TecnoLógicas ◽

10.22430/22565337.1205 ◽

2019 ◽

Vol 22 (46) ◽

pp. 1-14 ◽

Cited By ~ 1

Author(s):

Jorge Luis Bacca ◽

Henry Arguello

Keyword(s):

Spatial Information ◽

Hyperspectral Image ◽

Subspace Clustering ◽

Unsupervised Classification ◽

Hyperspectral Images ◽

Image Clustering ◽

Spectral Signature ◽

Spectral Image ◽

Sparse Subspace Clustering

Spectral image clustering is an unsupervised classification method which identifies distributions of pixels using spectral information without requiring a previous training stage. The sparse subspace clustering-based methods (SSC) assume that hyperspectral images lie in the union of multiple low-dimensional subspaces. Using this, SSC groups spectral signatures in different subspaces, expressing each spectral signature as a sparse linear combination of all pixels, ensuring that the non-zero elements belong to the same class. Although these methods have shown good accuracy for unsupervised classification of hyperspectral images, the computational complexity becomes intractable as the number of pixels increases, i.e. when the spatial dimension of the image is large. For this reason, this paper proposes to reduce the number of pixels to be classified in the hyperspectral image, and later, the clustering results for the missing pixels are obtained by exploiting the spatial information. Specifically, this work proposes two methodologies to remove the pixels, the first one is based on spatial blue noise distribution which reduces the probability to remove cluster of neighboring pixels, and the second is a sub-sampling procedure that eliminates every two contiguous pixels, preserving the spatial structure of the scene. The performance of the proposed spectral image clustering framework is evaluated in three datasets showing that a similar accuracy is obtained when up to 50% of the pixels are removed, in addition, it is up to 7.9 times faster compared to the classification of the data sets without incomplete pixels.

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Fusion of Spectral and Spatial Information for Classification of Hyperspectral Remote-Sensed Imagery by Local Graph

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2015.2498664 ◽

2016 ◽

Vol 9 (2) ◽

pp. 583-594 ◽

Cited By ~ 21

Author(s):

Wenzhi Liao ◽

Mauro Dalla Mura ◽

Jocelyn Chanussot ◽

Aleksandra Pizurica

Keyword(s):

Spatial Information ◽

Local Graph

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On the utilization of novel spectral laser scanning for three-dimensional classification of vegetation elements

Interface Focus ◽

10.1098/rsfs.2017.0039 ◽

2018 ◽

Vol 8 (2) ◽

pp. 20170039 ◽

Cited By ~ 14

Author(s):

Zhan Li ◽

Michael Schaefer ◽

Alan Strahler ◽

Crystal Schaaf ◽

David Jupp

Keyword(s):

Laser Scanning ◽

Spatial Information ◽

Information Source ◽

Three Dimensional ◽

Laser Scanner ◽

Biochemical Properties ◽

Dual Wavelength ◽

Wavelength Scanning ◽

Spatial Attributes

The Dual-Wavelength Echidna Lidar (DWEL), a full waveform terrestrial laser scanner (TLS), has been used to scan a variety of forested and agricultural environments. From these scanning campaigns, we summarize the benefits and challenges given by DWEL's novel coaxial dual-wavelength scanning technology, particularly for the three-dimensional (3D) classification of vegetation elements. Simultaneous scanning at both 1064 nm and 1548 nm by DWEL instruments provides a new spectral dimension to TLS data that joins the 3D spatial dimension of lidar as an information source. Our point cloud classification algorithm explores the utilization of both spectral and spatial attributes of individual points from DWEL scans and highlights the strengths and weaknesses of each attribute domain. The spectral and spatial attributes for vegetation element classification each perform better in different parts of vegetation (canopy interior, fine branches, coarse trunks, etc.) and under different vegetation conditions (dead or live, leaf-on or leaf-off, water content, etc.). These environmental characteristics of vegetation, convolved with the lidar instrument specifications and lidar data quality, result in the actual capabilities of spectral and spatial attributes to classify vegetation elements in 3D space. The spectral and spatial information domains thus complement each other in the classification process. The joint use of both not only enhances the classification accuracy but also reduces its variance across the multiple vegetation types we have examined, highlighting the value of the DWEL as a new source of 3D spectral information. Wider deployment of the DWEL instruments is in practice currently held back by challenges in instrument development and the demands of data processing required by coaxial dual- or multi-wavelength scanning. But the simultaneous 3D acquisition of both spectral and spatial features, offered by new multispectral scanning instruments such as the DWEL, opens doors to study biophysical and biochemical properties of forested and agricultural ecosystems at more detailed scales.

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