A Novel Ranking-Based Clustering Approach for Hyperspectral Band Selection

Jia, Sen; Tang, Guihua; Zhu, Jiasong; Li, Qingquan

doi:10.1109/tgrs.2015.2450759

Cited by 313 publications

(132 citation statements)

References 40 publications

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“…Guoying et al, [81] proposed a method for quick assessing large-scale DEM landslides from high resolution SPOT-5 image. The main application of proposed method is that, it is used for damage assessments in terms of depth and volume of larg-scale landslides.…”

Section: Object-oriented Classificationmentioning

confidence: 99%

Remote Sensing Satellite Image Processing Techniques for Image Classification: A Comprehensive Survey

Sowmya¹,

Shenoy²,

Venugopal³

2017

IJCA

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This paper is a brief survey of advance technological aspects of Digital Image Processing which are applied to remote sensing images obtained from various satellite sensors. In remote sensing, the image processing techniques can be categories in to four main processing stages: Image preprocessing, Enhancement, Transformation and Classification. Image pre-processing is the initial processing which deals with correcting radiometric distortions, atmospheric distortion and geometric distortions present in the raw image data. Enhancement techniques are applied to preprocessed data in order to effectively display the image for visual interpretation. It includes techniques to effectively distinguish surface features for visual interpretation. Transformation aims to identify particular feature of earth's surface and classification is a process of grouping the pixels, that produces effective thematic map of particular land use and land cover.

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Section: Object-oriented Classificationmentioning

confidence: 99%

Remote Sensing Satellite Image Processing Techniques for Image Classification: A Comprehensive Survey

Sowmya¹,

Shenoy²,

Venugopal³

2017

IJCA

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“…Affinity propagation and the k-means are also used in band selection [17,18]. Ranking-based methods are also widely applied, in which the importance of each band is first quantified according to a certain criterion, and then a given number of top-ranked bands in the sorted sequence is selected to form the subset [19]. Chang et al proposed a constrained band selection for hyperspectral images, in which the correlation or dependence between different bands is minimized [20].…”

Section: Introductionmentioning

confidence: 99%

Fast hyperspectral band selection based on spatial feature extraction

Cao

Wang

et al. 2018

J Real-Time Image Proc

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Hyperspectral images usually consist of hundreds of spectral bands, which can be used to precisely characterize different land cover types. However, the high dimensionality also has some disadvantages, such as the Hughes effect and a high storage demand. Band selection is an effective method to address these issues. However, most band selection algorithms are conducted with the high-dimensional band images, which will bring high computation complexity and may deteriorate the selection performance. In this paper, spatial feature extraction is used to reduce the dimensionality of band images and improve the band selection performance. The experiment results obtained on three real hyperspectral datasets confirmed that the spatial feature extraction-based approach exhibits more robust classification accuracy when compared with other methods. Besides, the proposed method can dramatically reduce the dimensionality of each band image, which makes it possible for band selection to be implemented in real time situations.

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“…Therefore, the HS images are more likely to be corrupted by noise [1,2]. An accurate approximation of the noise distribution and noise level in HS images is of benefit for improving the image quality, and is essential for subsequent HS image processing applications such as denoising [3,4], band selection [5], classification [6], target detection [7], and change detection [8].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Segmentation via Frequency-Based Similarity for Mixed Noise Estimation

Sun

2017

Remote Sensing

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Accurate approximation of the signal-independent (SI) and signal-dependent (SD) mixed noise from hyperspectral (HS) images is a critical task for many image processing applications where the detection of homogeneous regions plays a key role. Most of the conventional methods empirically divide images into rectangular blocks and then select the homogeneous ones, but it might result in erroneous homogeneity detection, especially for highly textured HS images. To address this challenge, a superpixel segmentation algorithm is proposed in this paper, which can decompose a noisy HS image into patches that adhere to the local structures and hence persist in homogeneous characteristic. A novel spectral similarity measure is defined in the frequency domain to make the superpixel segmentation algorithm more robust to the mixed noise. Combined with an improved scatter-plot-based homogeneous superpixel selection and a multiple linear regression-based noise parameter calculation, our method can accurately estimate SD and SI noise variances from HS images with different noise conditions and various image complexities. We evaluate the proposed method with both synthetic and real Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) HS images. Experimental results demonstrate that the proposed noise estimation method outperforms the state-of-the-art methods.

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A Novel Ranking-Based Clustering Approach for Hyperspectral Band Selection

Cited by 313 publications

References 40 publications

Remote Sensing Satellite Image Processing Techniques for Image Classification: A Comprehensive Survey

Remote Sensing Satellite Image Processing Techniques for Image Classification: A Comprehensive Survey

Fast hyperspectral band selection based on spatial feature extraction

Hyperspectral Image Segmentation via Frequency-Based Similarity for Mixed Noise Estimation

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