Band-Subset-Based Clustering and Fusion for Hyperspectral Imagery Classification

Zhao, Yongqiang; Zhang, Lei; Kong, Seong G.

doi:10.1109/tgrs.2010.2059707

Cited by 74 publications

(36 citation statements)

References 30 publications

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“…all the available pixels or just a cropped version of it. [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41] In our case, the whole data set of almost 400,000 pixels has been used to show the good scalability of the proposed concept.…”

Section: Image Data Setmentioning

confidence: 99%

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When remote sensing meets topological data analysis

Duponchel

2018

J. Spectral Imaging

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Hyperspectral remote sensing plays an increasingly important role in many scientific domains and everyday life problems. Indeed, this imaging concept ends up in applications as varied as catching tax-evaders red-handed by locating new construction and building alterations, searching for aircraft and saving lives after fatal crashes, detecting oil spills for marine life and environmental preservation, spying on enemies with reconnaissance satellites, watching algae grow as an indicator of environmental health, forecasting weather to warn about natural disasters and much more. From an instrumental point of view, we can say that the actual spectrometers have rather good characteristics, even if we can always increase spatial resolution and spectral range. In order to extract ever more information from such experiments and develop new applications, we must, therefore, propose multivariate data analysis tools able to capture the shape of data sets and their specific features. Nevertheless, actual methods often impose a data model which implicitly defines the geometry of the data set. The aim of the paper is thus to introduce the concept of topological data analysis in the framework of remote sensing, making no assumptions about the global shape of the data set, but also allowing the capture of its local features.

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Section: Image Data Setmentioning

confidence: 99%

“…In the literature, the data set is also used for purposes such as supervised classification, 18,19,21,25,27,29,30,33,34 …”

Section: Image Data Setmentioning

confidence: 99%

When remote sensing meets topological data analysis

Duponchel

2018

J. Spectral Imaging

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“…Changes in surface cover that occur between acquisitions can also have negative effects on the operation. Recent developments in image fusion have motivated new exploration of the spatial resolution enhancement of hyperspectral images [11,12]. Pansharpening, a traditional method applied to multispectral images, has received increasing attention for its use with hyperspectral images [13,14].…”

Section: Introductionmentioning

confidence: 99%

Potential of Resolution-Enhanced Hyperspectral Data for Mineral Mapping Using Simulated EnMAP and Sentinel-2 Images

2016

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Spaceborne hyperspectral images are useful for large scale mineral mapping. Acquired at a ground sampling distance (GSD) of 30 m, the Environmental Mapping and Analysis Program (EnMAP) will be capable of putting many issues related to environment monitoring and resource exploration in perspective with measurements in the spectral range between 420 and 2450 nm. However, a higher spatial resolution is preferable for many applications. This paper investigates the potential of fusion-based resolution enhancement of hyperspectral data for mineral mapping. A pair of EnMAP and Sentinel-2 images is generated from a HyMap scene over a mining area. The simulation is based on well-established sensor end-to-end simulation tools. The EnMAP image is fused with Sentinel-2 10-m-GSD bands using a matrix factorization method to obtain resolution-enhanced EnMAP data at a 10 m GSD. Quality assessments of the enhanced data are conducted using quantitative measures and continuum removal and both show that high spectral and spatial fidelity are maintained. Finally, the results of spectral unmixing are compared with those expected from high-resolution hyperspectral data at a 10 m GSD. The comparison demonstrates high resemblance and shows the great potential of the resolution enhancement method for EnMAP type data in mineral mapping.

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“…While band clustering merges only adjacent bands in one cluster, the groups as used by the proposed approach do not follow any predefined order. The idea to use correlation coefficients to group bands is also investigated in (Zhao et al, 2011). While the authors used the correlation between the data itself, the proposed methods computes the correlation of classification maps.…”

Section: Introductionmentioning

confidence: 99%

Task-Dependent Band-Selection of Hyperspectral Images by Projection-Based Random Forests

Hänsch¹,

Hellwich²

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The automatic classification of land cover types from hyperspectral images is a challenging problem due to (among others) the large amount of spectral bands and their high spatial and spectral correlation. The extraction of meaningful features, that enables a subsequent classifier to distinguish between different land cover classes, is often limited to a subset of all available data dimensions which is found by band selection techniques or other methods of dimensionality reduction. This work applies Projection-Based Random Forests to hyperspectral images, which not only overcome the need of an explicit feature extraction, but also provide mechanisms to automatically select spectral bands that contain original (i.e. non-redundant) as well as highly meaningful information for the given classification task. The proposed method is applied to four challenging hyperspectral datasets and it is shown that the effective number of spectral bands can be considerably limited without loosing too much of classification performance, e.g. a loss of 1% accuracy if roughly 13% of all available bands are used.

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Band-Subset-Based Clustering and Fusion for Hyperspectral Imagery Classification

Cited by 74 publications

References 30 publications

When remote sensing meets topological data analysis

When remote sensing meets topological data analysis

Potential of Resolution-Enhanced Hyperspectral Data for Mineral Mapping Using Simulated EnMAP and Sentinel-2 Images

Task-Dependent Band-Selection of Hyperspectral Images by Projection-Based Random Forests

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