How Hyperspectral Image Unmixing and Denoising Can Boost Each Other

Rasti, Behnood; Koirala, Bikram; Scheunders, Paul; Ghamisi, Pedram

doi:10.3390/rs12111728

Cited by 14 publications

(4 citation statements)

References 44 publications

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“…Spectral unmixing has become an important tool in hyperspectral imagery, prompting its usage in a number of applications (e.g., image reconstruction [73][74][75], noise reduction [76][77][78], spatial resolution enhancement [79][80][81], supervised material classification [32,82,83], change detection [84][85][86][87], and anomaly detection [31,88,89]). The importance of spectral unmixing in remote sensing has motivated the development of many algorithms for this task, which we broadly summarize here; see surveys [12,[90][91][92][93][94] for a more thorough overview.…”

Section: Background On Spectral Unmixingmentioning

confidence: 99%

Unsupervised Diffusion and Volume Maximization-Based Clustering of Hyperspectral Images

et al. 2023

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Hyperspectral images taken from aircraft or satellites contain information from hundreds of spectral bands, within which lie latent lower-dimensional structures that can be exploited for classifying vegetation and other materials. A disadvantage of working with hyperspectral images is that, due to an inherent trade-off between spectral and spatial resolution, they have a relatively coarse spatial scale, meaning that single pixels may correspond to spatial regions containing multiple materials. This article introduces the Diffusion and Volume maximization-based Image Clustering (D-VIC) algorithm for unsupervised material clustering to address this problem. By directly incorporating pixel purity into its labeling procedure, D-VIC gives greater weight to pixels corresponding to a spatial region containing just a single material. D-VIC is shown to outperform comparable state-of-the-art methods in extensive experiments on a range of hyperspectral images, including land-use maps and highly mixed forest health surveys (in the context of ash dieback disease), implying that it is well-equipped for unsupervised material clustering of spectrally-mixed hyperspectral datasets.

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Section: Background On Spectral Unmixingmentioning

confidence: 99%

Unsupervised Diffusion and Volume Maximization-Based Clustering of Hyperspectral Images

et al. 2023

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“…At the same time, there is a certain correlation between the reflectance data of various bands. The accuracy of diagnostic models based on raw data is low [46,47]. Extracting spectral characteristic variables to establish a diagnostic model of nutrient element content can reduce the computational cost.…”

Section: Extraction Of Hyperspectral Characteristic Variablesmentioning

confidence: 99%

Spectral Diagnostic Model for Agricultural Robot System Based on Binary Wavelet Algorithm

Tang

Gao

et al. 2022

Sensors

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The application of agricultural robots can liberate labor. The improvement of robot sensing systems is the premise of making it work. At present, more research is being conducted on weeding and harvesting systems of field robot, but less research is being conducted on crop disease and insect pest perception, nutritional element diagnosis and precision fertilizer spraying systems. In this study, the effects of the nitrogen application rate on the absorption and accumulation of nitrogen, phosphorus and potassium in sweet maize were determined. Firstly, linear, parabolic, exponential and logarithmic diagnostic models of nitrogen, phosphorus and potassium contents were constructed by spectral characteristic variables. Secondly, the partial least squares regression and neural network nonlinear diagnosis model of nitrogen, phosphorus and potassium contents were constructed by the high-frequency wavelet sensitivity coefficient of binary wavelet decomposition. The results show that the neural network nonlinear diagnosis model of nitrogen, phosphorus and potassium content based on the high-frequency wavelet sensitivity coefficient of binary wavelet decomposition is better. The R2, MRE and NRMSE of nn of nitrogen, phosphorus and potassium were 0.974, 1.65% and 0.0198; 0.969, 9.02% and 0.1041; and 0.821, 2.16% and 0.0301, respectively. The model can provide growth monitoring for sweet corn and a perception model for the nutrient element perception system of an agricultural robot, while making preliminary preparations for the realization of intelligent and accurate field fertilization.

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“…In [81], a local low-rank and sparse representation (Local LRSR) was suggested based on a weighted nuclear norm for HSI denoising in the presence of Gaussian noise. Spectral linear unmixing techniques are also considered as a low-rank HSI denoiser [82]. However, denoisers based on unmixing are often vulnerable to endmember estimations.…”

Section: A Conventional Techniquesmentioning

confidence: 99%

Image Restoration for Remote Sensing: Overview and Toolbox

Rasti¹,

Chen²,

Dalsasso³

et al. 2021

Preprint

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This paper is under review in GRSM. Remote sensing provides valuable information about objects or areas from a distance in either active (e.g., RADAR and LiDAR) or passive (e.g., multispectral and hyperspectral) modes. The quality of data acquired by remotely sensed imaging sensors (both active and passive) is often degraded by a variety of noise types and artifacts. Image restoration, which is a vibrant field of research in the remote sensing community, is the task of recovering the true unknown image from the degraded observed image. Each imaging sensor induces unique noise types and artifacts into the observed image. This fact has led to the expansion of restoration techniques in different paths according to each sensor type. This review paper brings together the advances of image restoration techniques with particular focuses on synthetic aperture radar and hyperspectral images as the most active sub-fields of image restoration in the remote sensing community. We, therefore, provide a comprehensive, discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to investigate the vibrant topic of data restoration by supplying sufficient detail and references. Additionally, this review paper accompanies a toolbox to provide a platform to encourage interested students and researchers in the field to further explore the restoration techniques and fast-forward the community. The toolboxes are provided in https://github.com/ImageRestorationToolbox.

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How Hyperspectral Image Unmixing and Denoising Can Boost Each Other

Cited by 14 publications

References 44 publications

Unsupervised Diffusion and Volume Maximization-Based Clustering of Hyperspectral Images

Unsupervised Diffusion and Volume Maximization-Based Clustering of Hyperspectral Images

Spectral Diagnostic Model for Agricultural Robot System Based on Binary Wavelet Algorithm

Image Restoration for Remote Sensing: Overview and Toolbox

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