A Low-Complexity Approach for the Color Display of Hyperspectral Remote-Sensing Images Using One-Bit-Transform-Based Band Selection

Demir, Begüm; Çelebi, Anıl; Ertürk, Sarp

doi:10.1109/tgrs.2008.2001553

Cited by 58 publications

(24 citation statements)

References 16 publications

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“…(3) The first three well-structured bands are selected as principal bands instead of the full bands by comparing the value of A(l) [32][33][34].…”

Section: Obtaining Spectral-spatial Vectormentioning

confidence: 99%

An Efficient Hyperspectral Image Retrieval Method: Deep Spectral-Spatial Feature Extraction with DCGAN and Dimensionality Reduction Using t-SNE-Based NM Hashing

Zhang

et al. 2018

Remote Sensing

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Abstract:Hyperspectral images are one of the most important fundamental and strategic information resources, imaging the same ground object with hundreds of spectral bands varying from the ultraviolet to the microwave. With the emergence of huge volumes of high-resolution hyperspectral images produced by all sorts of imaging sensors, processing and analysis of these images requires effective retrieval techniques. How to ensure retrieval accuracy and efficiency is a challenging task in the field of hyperspectral image retrieval. In this paper, an efficient hyperspectral image retrieval method is proposed. In principle, our method includes the following steps: (1) in order to make powerful representations for hyperspectral images, deep spectral-spatial features are extracted with the Deep Convolutional Generative Adversarial Networks (DCGAN) model; (2) considering the higher dimensionality of deep spectral-spatial features, t-Distributed Stochastic Neighbor Embedding-based Nonlinear Manifold (t-SNE-based NM) hashing is utilized to make dimensionality reduction by learning compact binary codes embedded on the intrinsic manifolds of deep spectral-spatial features for balancing between learning efficiency and retrieval accuracy; and (3) multi-index hashing in Hamming space is measured to find similar hyperspectral images. Five comparative experiments are conducted to verify the effectiveness of deep spectral-spatial features, dimensionality reduction of t-SNE-based NM hashing, and similarity measurement of multi-index hashing. The experimental results using NASA datasets show that our hyperspectral image retrieval method can achieve comparable and superior performance with less computational time.

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“…(3) The first three well-structured bands are selected as principal bands instead of the full bands by comparing the value of A(l) [32][33][34].…”

Section: Obtaining Spectral-spatial Vectormentioning

confidence: 99%

An Efficient Hyperspectral Image Retrieval Method: Deep Spectral-Spatial Feature Extraction with DCGAN and Dimensionality Reduction Using t-SNE-Based NM Hashing

Zhang

et al. 2018

Remote Sensing

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“…For the interpolation method summarized in Section III-A, we use h = 12 and N I = 5. For the final visualization, we use a final linear stretching (also called "linear fusion of images" 2 Let us note that a similar automatic bad band removal strategy but based on the inter-correlation measure (using the fact that the noisy hyperspectral bands lack structure) has also been proposed in [16] and [17]. Let us note that such a strategy can be also done manually (by visual inspection) or can be determined [10] by the publicly available signal-to-noise curve (due to the measurement equipment) from the 1997 AVIRIS calibration given in [37, Fig.…”

Section: A Setupmentioning

confidence: 99%

“…Let us also mention the low-complexity color display approach (suitable for hardware implementation) proposed in [16], which uses the 1-b transform of hyperspectral image bands for selecting three suitable bands for the RGB display, or the multivariate visualization technique proposed in [17], which uses a double color layers displaying simultaneously on the first layer (referred to as the background layer) the distribution of materials existing in the image scene and, in the second layer (detail layer), their respective composition (i.e., the socalled end-member materials) at the subpixel level. A recent paper in which different color display techniques are reviewed, compared, and evaluated is proposed in [18].…”

Section: Introductionmentioning

confidence: 99%

A Multiresolution Markovian Fusion Model for the Color Visualization of Hyperspectral Images

Mignotte

2010

IEEE Trans. Geosci. Remote Sensing

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Abstract-In this paper, we present a nonstationary Markov random field (MRF) fusion model for the color display of hyperspectral images. The proposed fusion or dimensionality reduction model is derived from the preservation of spectral distance criterion. This quantitative metric of good dimensionality reduction and meaningful visualization allows us to derive an appealing fusion model of high-dimensional spectral data, expressed as a Gibbs distribution or a nonstationary MRF model defined on a complete graph. In this framework, we propose a computationally efficient coarse-to-fine conjugate-gradient optimization method to minimize the cost function related to this energy-based fusion model. The experiments reported in this paper demonstrate that the proposed visualization method is efficient (in terms of preservation of spectral distances and discriminality of pixels with different spectral signatures) and performs well compared to the best existing state-of-the-art multidimensional imagery color display methods recently proposed in the literature.Index Terms-Color display, complete graph, conjugategradient method, multidimensional imagery, multiresolution optimization, nonlocal Markov model, nonstationary Markov random field (MRF), visualization of hyperspectral images.

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“…Some softwares provide interactive tools for users to manually pick three bands to display [1], [2]. More sophisticated band selection methods [3], [4], [5] aim to highlight expected features so that human perceptual bands or the most informative bands are selected for visualization.…”

Section: Introductionmentioning

confidence: 99%

Constrained Manifold Learning for Hyperspectral Imagery Visualization

Liao

Qian

Tang

2018

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Abstract-Displaying the large number of bands in a hyperspectral image (HSI) on a trichromatic monitor is important for HSI processing and analysis system. The visualized image shall convey as much information as possible from the original HSI and meanwhile facilitate image interpretation. However, most existing methods display HSIs in false color, which contradicts with user experience and expectation. In this paper, we propose a visualization approach based on constrained manifold learning, whose goal is to learn a visualized image that not only preserves the manifold structure of the HSI but also has natural colors. Manifold learning preserves the image structure by forcing pixels with similar signatures to be displayed with similar colors. A composite kernel is applied in manifold learning to incorporate both the spatial and spectral information of HSI in the embedded space. The colors of the output image are constrained by a corresponding natural-looking RGB image, which can either be generated from the HSI itself (e.g., band selection from the visible wavelength) or be captured by a separate device. Our method can be done at instance-level and feature-level. Instance-level learning directly obtains the RGB coordinates for the pixels in the HSI while feature-level learning learns an explicit mapping function from the high dimensional spectral space to the RGB space. Experimental results demonstrate the advantage of the proposed method in information preservation and natural color visualization.

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A Low-Complexity Approach for the Color Display of Hyperspectral Remote-Sensing Images Using One-Bit-Transform-Based Band Selection

Cited by 58 publications

References 16 publications

An Efficient Hyperspectral Image Retrieval Method: Deep Spectral-Spatial Feature Extraction with DCGAN and Dimensionality Reduction Using t-SNE-Based NM Hashing

An Efficient Hyperspectral Image Retrieval Method: Deep Spectral-Spatial Feature Extraction with DCGAN and Dimensionality Reduction Using t-SNE-Based NM Hashing

A Multiresolution Markovian Fusion Model for the Color Visualization of Hyperspectral Images

Constrained Manifold Learning for Hyperspectral Imagery Visualization

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