Feature extraction approach for quality assessment of remotely sensed hyperspectral images

Das, Samiran; Bhattacharya, Shubhobrata; Khatri, Pushkar Kumar

doi:10.1117/1.jrs.14.026514

Cited by 6 publications

(10 citation statements)

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“…rank(•) represents the rank function, S 1 = ∑ i,j |S i,j | represents the l 1 -norm and λ is a parameter that balances low-rank and sparse matrices. To solve problem (5), most existing hyperspectral anomaly detection based on LRSMD applies the nuclear norm to approximate the rank function. As such, the optimized LRSMD model can be written as: min…”

Section: Lrsmd-tnnmentioning

confidence: 99%

“…The size of window: (3, 5), (3,5), (13,31), (5, 7) The outputting sensitivity of spatial distance weight: 0.8, 5, 10, 0.5 The outputting sensitivity of spectral distance weight: 0.3, 1, 1, 0.3…”

Section: Bfadmentioning

confidence: 99%

“…The size of window: (3, 5), (5, 7), (13,21), (3,5) The fractional order: 0.8, 0.5, 0.8, 0.9 The Lagrange multiplier: 0.5, 1, 0.1, 0.1 The weighting coefficient: 0.5, 0.1, 0.1, 0.1…”

Section: Ssfscrdmentioning

confidence: 99%

“…The band number: 3, 3, 3, 3 The thresholding number: 5, 25, 125, 25 The domain transform recursive filtering: (1, 0.1). (5, 1), (5, 1), (5,1) The parameters of the proposed SCDF method were window size w, the standard deviation σ, parameters λ and β, number of singular values r, penalty coefficient µ 0 and µ max , parameter ε, reconstruction errors τ 1 and τ 2 , and the predefined logical predicate T ρ . Although there were many parameters in the proposed SCDF method, some parameters had little effect on the performance of the proposed method, such as penalty coefficient µ 0 and µ max , parameter ε, and reconstruction errors τ 1 and τ 2 .…”

Section: Aedmentioning

confidence: 99%

“…In the hyperspectral imaging process, the spectral information and spatial information of the ground targets can be obtained at the same time. The obtained tens or hundreds of spectral bands can be regarded as approximately continuous [1][2][3][4][5]. Hence, the obtained hyperspectral image (HSI) can more comprehensively reflect the subtle differences among different ground targets that cannot be detected by broadband remote sensing [6].…”

Section: Introductionmentioning

confidence: 99%

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Spectral–Spatial Complementary Decision Fusion for Hyperspectral Anomaly Detection

Xiang

Song

et al. 2022

Remote Sensing

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Hyperspectral anomaly detection has become an important branch of remote–sensing image processing due to its important theoretical value and wide practical application prospects. However, some anomaly detection methods mainly exploit the spectral feature and do not make full use of spatial features, thus limiting the performance improvement of anomaly detection methods. Here, a novel hyperspectral anomaly detection method, called spectral–spatial complementary decision fusion, is proposed, which combines the spectral and spatial features of a hyperspectral image (HSI). In the spectral dimension, the three–dimensional Hessian matrix was first utilized to obtain three–directional feature images, in which the background pixels of the HSI were suppressed. Then, to more accurately separate the sparse matrix containing the anomaly targets in the three–directional feature images, low–rank and sparse matrix decomposition (LRSMD) with truncated nuclear norm (TNN) was adopted to obtain the sparse matrix. After that, the rough detection map was obtained from the sparse matrix through finding the Mahalanobis distance. In the spatial dimension, two–dimensional attribute filtering was employed to extract the spatial feature of HSI with a smooth background. The spatial weight image was subsequently obtained by fusing the spatial feature image. Finally, to combine the complementary advantages of each dimension, the final detection result was obtained by fusing all rough detection maps and the spatial weighting map. In the experiments, one synthetic dataset and three real–world datasets were used. The visual detection results, the three–dimensional receiver operating characteristic (3D ROC) curve, the corresponding two–dimensional ROC (2D ROC) curves, and the area under the 2D ROC curve (AUC) were utilized as evaluation indicators. Compared with nine state–of–the–art alternative methods, the experimental results demonstrate that the proposed method can achieve effective and excellent anomaly detection results.

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Section: Lrsmd-tnnmentioning

confidence: 99%

Section: Bfadmentioning

confidence: 99%

“…The size of window: (3, 5), (5, 7), (13,21), (3,5) The fractional order: 0.8, 0.5, 0.8, 0.9 The Lagrange multiplier: 0.5, 1, 0.1, 0.1 The weighting coefficient: 0.5, 0.1, 0.1, 0.1…”

Section: Ssfscrdmentioning

confidence: 99%

Section: Aedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Spectral–Spatial Complementary Decision Fusion for Hyperspectral Anomaly Detection

Xiang

Song

et al. 2022

Remote Sensing

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Advanced Wearable Sensing Technologies for Sustainable Precision Agriculture – a Review on Chemical Sensors

Muthumalai,

Gokila,

Haldorai

et al. 2023

Advanced Sensor Research

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Crop production is impacted by increased plant diseases and shifting environmental circumstances. Monitoring plant health is necessary to raise crop quality and productivity to meet population growth demands. Nanotechnology‐based sensor platforms provide real‐time plant monitoring capabilities, going beyond the constraints of conventional sensor technologies. Wearables are an evolving area of health monitoring and have been modified for agricultural purposes. Wearable sensors are placed on various plant organs in the agricultural industry to check the crops’ health continuously. The varieties of wearable sensor materials and their fabrications, followed by their sensing mechanisms, are highlighted in this review. Furthermore, monitoring plant micro‐environmental factors, including salinity, hazardous gases, and pesticides, are discussed. This text covers various internal plant growth factors monitoring, such as sap flow, transpiration, and signal monitoring. The challenges of wearable sensors in agriculture are mentioned toward the end.

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Plant‐Wearable Sensors for Intelligent Forestry Monitoring

Han

et al. 2022

Advanced Sustainable Systems

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Traditional monitoring technologies, such as infrared thermography, [7] hyperspectral techniques, [8] wireless sensor networks, [9] satellite imaging and remote sensing techniques, [10] etc. have limited their use in monitoring the growth of forest seedlings due to their high cost, difficult installation, complicated signal conversion, and difficulty in achieving high temporal resolution for real-time monitoring. [11] Moreover, the operational complexity, nonportability, and poor biocompatibility of traditional rigid sensors used for forestry monitoring hinder the healthy growth of seedlings dealing with developing and fragile tree seedlings. [12,13] On the other hand, the growth and development of seedlings directly affect the quality of forestry trees and changes in the forest ecological environment. [14] Plant physiological metabolism, plant growth in seedling cultivation and growing environment monitoring of young trees need to be monitored to evaluate plant growth characteristics, and then integrated with molecular theories such as genetic modification in forest genetics and breeding. [15,16] In this process, plant-wearable sensors play a very important role, and the lack of intelligent forestry monitoring technology brings problems such as high cost, long time, and inaccurate data. Hence, it is essential to develop intelligent strategies that monitor the growth of young forests and the harmful stresses during the growth of young forests, which induces unique requirement of flexible plant-wearable sensors for forestry monitoring.Meanwhile, plant-wearable sensors can be closely fitted to the surface of tree seedlings for real-time continuous monitoring without causing damage to the seedlings themselves by using their own characteristics such as flexibility and stretchability, [17,18] thus appealing to this wearables requirement for intelligent forestry monitoring. Therefore, flexible, lightweight, and wearable properties of plant-wearable sensors are urgently needed for this application of intelligent forestry monitoring.Herein, this paper introduces the recent development status of plant-wearable sensors in detail from the perspective of early intelligent monitoring in forestry and enumerates their structural compositions and working mechanisms. Application scenarios of plant wearables, including for the detection of plant physiological metabolism and plant growth, signal molecule detection, forest fire prevention, growth environment monitoring, and development of self-powered wireless monitoring system, are systematically summarized (Figure 1). Flexible plant-wearable sensors show great potential for precise and intelligent monitoring of real-time physical and chemical signals in forestry seedlings to optimize their growth environment, increase carbon sequestration, and promote underforest ecosystems. Furthermore, plant-wearable sensors can help prevent threats such as forest ecological deterioration, forest diseases, and insect pests. Herein, recent advances in emerging plantwearable sensors used in forest...

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Feature extraction approach for quality assessment of remotely sensed hyperspectral images

Cited by 6 publications

References 0 publications

Spectral–Spatial Complementary Decision Fusion for Hyperspectral Anomaly Detection

Spectral–Spatial Complementary Decision Fusion for Hyperspectral Anomaly Detection

Advanced Wearable Sensing Technologies for Sustainable Precision Agriculture – a Review on Chemical Sensors

Plant‐Wearable Sensors for Intelligent Forestry Monitoring

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