Driving Realistic Texture in Simulated Long-Wave Infrared Imagery

Ward, Jason T.; Lach, S.R.; Schott, John R.; Sanders, Niek J.; Brown, Scott

doi:10.1109/igarss.2008.4779451

Cited by 4 publications

(7 citation statements)

References 2 publications

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“…where ε is the emissivity of surface material, 2 1~λ λ is the detected waveband, 1 c and 2 c are the radiation constants:…”

Section: Thermal Radiation Modelmentioning

confidence: 99%

“…Texture is a major factor that impacts both the visual appearance and the overall scene statistics of imagery throughout the electromagnetic spectrum [18] . In this paper, we propose a texture structure model to add realism to infrared images.…”

Section: Texture Structure Modelmentioning

confidence: 99%

“…IR texture in this paper refers to the image's spatial and spectral in-class variability [1] . Generally, infrared texture can be generated in two ways: conversion from images of the visible (VIS) band, and model-based IR texture synthesis.…”

Section: Introductionmentioning

confidence: 99%

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Simulation of realistic infrared texture of aeolian sand ripples

Liu

Long²,

Hao

et al. 2011

SPIE Proceedings

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Texture is of vital importance when rendering realistic infrared images. Traditional attempts to simulate infrared textures often suffer from insufficient realism seen in actual infrared images. This paper presents a synthesis model for generating infrared texture of aeolian sand ripples. By integration of physical radiation model and texture structure model, sources of variability that causes IR texture are modeled. The physical radiation model takes into account geometrical, thermodynamic and meteorological parameters and calculates the thermal radiation distribution of different surface slopes. The texture structure model is introduced based on natural landscape features to simulate spatial distribution patterns of aeolian sand ripples. Simulation results are indicative of good performance of the proposed method to simulate radiometrically-correct yet visually-appealing infrared textures.

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“…where ε is the emissivity of surface material, 2 1~λ λ is the detected waveband, 1 c and 2 c are the radiation constants:…”

Section: Thermal Radiation Modelmentioning

confidence: 99%

Section: Texture Structure Modelmentioning

confidence: 99%

See 1 more Smart Citation

Simulation of realistic infrared texture of aeolian sand ripples

Liu

Long²,

Hao

et al. 2011

SPIE Proceedings

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“…The dictionary set D u learns from the scene, and the abundance set "a" over the dictionary exhibits characteristic high sparsity over the D u ; γ is the parameter to adjust the balance between the two terms in Equation (2). To learn the D u for a specific data set, the normal approach is the selection of spectra from a comprehensive dictionary (also known as completed or overcompleted dictionary) [10], which consists of vast number of spectral database, then each of it is tested as according to Equation (2) to justify if it fits into the criteria. The process is repeated over the comprehensive dictionary until all the elements of D u is found or until all spectral database in the dictionary is exhausted.…”

Section: Introductionmentioning

confidence: 99%

Endmember Learning with K-Means through SCD Model in Hyperspectral Scene Reconstructions

Chatterjee

Yuen

2019

J. Imaging

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This paper proposes a simple yet effective method for improving the efficiency of sparse coding dictionary learning (DL) with an implication of enhancing the ultimate usefulness of compressive sensing (CS) technology for practical applications, such as in hyperspectral imaging (HSI) scene reconstruction. CS is the technique which allows sparse signals to be decomposed into a sparse representation “a” of a dictionary D u . The goodness of the learnt dictionary has direct impacts on the quality of the end results, e.g., in the HSI scene reconstructions. This paper proposes the construction of a concise and comprehensive dictionary by using the cluster centres of the input dataset, and then a greedy approach is adopted to learn all elements within this dictionary. The proposed method consists of an unsupervised clustering algorithm (K-Means), and it is then coupled with an advanced sparse coding dictionary (SCD) method such as the basis pursuit algorithm (orthogonal matching pursuit, OMP) for the dictionary learning. The effectiveness of the proposed K-Means Sparse Coding Dictionary (KMSCD) is illustrated through the reconstructions of several publicly available HSI scenes. The results have shown that the proposed KMSCD achieves ~40% greater accuracy, 5 times faster convergence and is twice as robust as that of the classic Spare Coding Dictionary (C-SCD) method that adopts random sampling of data for the dictionary learning. Over the five data sets that have been employed in this study, it is seen that the proposed KMSCD is capable of reconstructing these scenes with mean accuracies of approximately 20–500% better than all competing algorithms adopted in this work. Furthermore, the reconstruction efficiency of trace materials in the scene has been assessed: it is shown that the KMSCD is capable of recovering ~12% better than that of the C-SCD. These results suggest that the proposed DL using a simple clustering method for the construction of the dictionary has been shown to enhance the scene reconstruction substantially. When the proposed KMSCD is incorporated with the Fast non-negative orthogonal matching pursuit (FNNOMP) to constrain the maximum number of materials to coexist in a pixel to four, experiments have shown that it achieves approximately ten times better than that constrained by using the widely employed TMM algorithm. This may suggest that the proposed DL method using KMSCD and together with the FNNOMP will be more suitable to be the material allocation module of HSI scene simulators like the CameoSim package.

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“…needed to drive the DIRSIG thermal model. Note that the terms the model is most sensitive to (i.e., solar absortivity, emissivity, slope, and azimuth) are computed on a sub-pixel, per sample basis using spectral library data and the DEM [10]. Figure 4 is an example data set showing a simulated TIRS band of a small subsection of the image where two arrays meet.…”

Section: Simulated Scenementioning

confidence: 99%

Simulation of Image Performance Characteristics of the Landsat Data Continuity Mission (LDCM) Thermal Infrared Sensor (TIRS)

et al. 2012

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Abstract:The next Landsat satellite, which is scheduled for launch in early 2013, will carry two instruments: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). Significant design changes over previous Landsat instruments have been made to these sensors to potentially enhance the quality of Landsat image data. TIRS, which is the focus of this study, is a dual-band instrument that uses a push-broom style architecture to collect data. To help understand the impact of design trades during instrument build, an effort was initiated to model TIRS imagery. The Digital Imaging and Remote Sensing Image Generation (DIRSIG) tool was used to produce synthetic "on-orbit" TIRS data with detailed radiometric, geometric, and digital image characteristics. This work presents several studies that used DIRSIG simulated TIRS data to test the impact of engineering performance data on image quality in an effort to determine if the image data meet specifications or, in the event that they do not, to determine if the resulting image data are still acceptable. OPEN ACCESSRemote Sens. 2012, 4 2478

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Driving Realistic Texture in Simulated Long-Wave Infrared Imagery

Cited by 4 publications

References 2 publications

Simulation of realistic infrared texture of aeolian sand ripples

Simulation of realistic infrared texture of aeolian sand ripples

Endmember Learning with K-Means through SCD Model in Hyperspectral Scene Reconstructions

Simulation of Image Performance Characteristics of the Landsat Data Continuity Mission (LDCM) Thermal Infrared Sensor (TIRS)

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