Infrared spectral imaging has been used in many fields, such as gas identification, environmental monitoring and target detection. In practical application, it is difficult to classify the spectrum between target and background due to cluster background and instrument noise. This article introduces the design of a modular FTIR imaging spectrometer based on interference optics and accurate control module. Based on this instrument, a spectral feature analysis and gas identification method is proposed and verified via experiment. The exact steps and algorithms include radiometric calibration, spectral pre-process, and spectral matching. First, multiple-points linear radiometric calibration is indicated to improve the calibration accuracy. Secondly, the spectral pre-processing methods are realized to decrease the noise and enhance the spectral difference between target and background. Thirdly, spectral matching based on similarity calculation is introduced to realize gas identification. Three methods, Euclidean distance (ED), spectral angle mapping (SAM) and spectral information divergence (SID), are derived. Finally, an experimental test is designed to verify the method proposed in this article, where SF6 is taken as the target. According to the results, various algorithms have different performance in time consumption and accuracy, and the proposed method is verified to be reliable and accurate in practical field test.
Single-pixel imaging was initially based on a statistical model, which uses a series of random patterns for illumination. This means that it requires a great number of measurements (much larger than pixel counts) and long data-acquisition time. Therefore it is necessary to enhance the effective of information collection and reconstruction by employing some priori information. In this paper, a single pixel imaging system is proposed based on APD and Spatial light modulator. And then an image reconstruction algorithm is proposed based on non-local means (NLM). Based on that there are a large number of similar patches within an infrared image, NLM method can abstract the non-local similarity information and then the value of image pixel can be estimated. The reconstructed image is obtained by minimizing a cost function.
A division of aperture infrared Stokes imaging polarimeter (ISIP) with optimal linear polarization measurements is presented. The focal plane array of the ISIP is divided into four independent imaging channels by a lens array, which is turned into four independent polarimetric analyzing channels by placing four linear polarizers of different orientation angles in front of each channel and a wave plate in one of the channels. An optimization method for the four polarization analyzing channels is proposed to improve measurement accuracy. For a high priority to linear polarization measurement, instead of optimization for full states of polarization components, we optimize the ISIP first for linear polarization components and then for circular polarization component. We demonstrate that the orientation angles of the polarizers are set at 0°, 60°, 90° and 120°, respectively. The optimal retardance of the wave plate is 90° with the orientation angle of 0°.
Hyperspectral imaging sensors can acquire images in hundreds of continuous narrow spectral bands. Therefore each object presented in the image can be identified from their spectral response. However, such kind of imaging brings a huge amount of data, which requires transmission, processing, and storage resources for both airborne and space borne imaging. Due to the high volume of hyperspectral image data, the exploration of compression strategies has received a lot of attention in recent years. Compression of hyperspectral data cubes is an effective solution for these problems. Lossless compression of the hyperspectral data usually results in low compression ratio, which may not meet the available resources; on the other hand, lossy compression may give the desired ratio, but with a significant degradation effect on object identification performance of the hyperspectral data. Moreover, most hyperspectral data compression techniques exploits the similarities in spectral dimensions; which requires bands reordering or regrouping, to make use of the spectral redundancy. In this paper, we explored the spectral cross correlation between different bands, and proposed an adaptive band selection method to obtain the spectral bands which contain most of the information of the acquired hyperspectral data cube. The proposed method mainly consist three steps: First, the algorithm decomposes the original hyperspectral imagery into a series of subspaces based on the hyper correlation matrix of the hyperspectral images between different bands. And then the Wavelet-based algorithm is applied to the each subspaces. At last the PCA method is applied to the wavelet coefficients to produce the chosen number of components. The performance of the proposed method was tested by using ISODATA classification method.
Gas detection and identification is based on the spectral absorption peak feature, which is acquired by the spectrometer. FTIR imaging spectrometer has the advantages of high spectral resolution and good sensitivity, which are both suitable for the unknown or mixture gas identification applications, such as plume pollution monitoring, chemical agents detection and leakage detection. According to the application requirement, a dual band FTIR imaging spectrometer has been developed and verified. This FTIR imaging spectrometer combines the infrared thermal imaging sensor and Michelson interferometer to form the three dimensional data cube. Based on this instrument, the theoretical analysis and algorithm is introduced, and the numerical method is explained to illuminate the basic idea in gas identification based on spectral features. After that, the field verification test is setup and completed. Firstly, the FTIR imaging spectrometer is used to detect SF6, NH3 and the mixture gas, while the gas is exhausted out from the storage vase with a specific speed. Secondly, the instrument is delivered to the industrial area to monitor the plume emission, and analyze the components in plume. Finally, the instrument is utilized to monitoring the oil spill in ocean, and the practical maritime trial is realized. Further, the gas concentration evaluation method is discussed. Quantitative issue in gas identification is an important topic. The test results show that, based on the gas identification method introduced in this paper, FTIR imaging spectrometer can be utilized to identify the unknown gas or mixture gas in real time. The instrument will play a key role in environmental emergency and monitoring application.
Oil spill pollution is a severe environmental problem that persists in the marine environment and in inland water systems around the world. Remote sensing is an important part of oil spill response. The hyperspectral images can not only provide the space information but also the spectral information. Pixels of interests generally incorporate information from disparate component that requires quantitative decomposition of these pixels to extract desired information. Oil spill detection can be implemented by applying hyperspectral camera which can collect the hyperspectral data of the oil. By extracting desired spectral signature from hundreds of band information, one can detect and identify oil spill area in vast geographical regions. There are now numerous hyperspectral image processing algorithms developed for target detection. In this paper, we investigate several most widely used target detection algorithm for the identification of surface oil spills in ocean environment. In the experiments, we applied a hyperspectral camera to collect the real life oil spill. The experimental results shows the feasibility of oil spill detection using hyperspectral imaging and the performance of hyperspectral image processing algorithms were also validated.
FTIR imaging spectrometer has significant meaning in the fields like industrial plume emission monitoring and public security monitoring. In this paper, a LWIR FTIR imaging spectrometer is applied to realize the field gas identification experiment. First, the structure and design of this spectrometer is indicated and discussed. Based on the algorithms research, the related gas identification software is developed. To verify this design, both lab and field experiments are realized. The lab experiment is applied to verify the spectral identification algorithm. The field trial is applied to analyze the gas components, and the results show that this spectrometer can realize the gas elements identification in real time.
The spectral characteristics of infrared radiation from target provide significant characteristics information for target's detection and track including radiance brightness, radiance intensity and spectrum characteristics of target. And the same time, the spectral characteristics provide the basis of target detection and recognize equipment's waveband optimization design and detection capability analysis. This paper using the passive imaging Fourier transformation infrared spectrometer measure the infrared spectral characteristic of target. The spectral range cover the medium wave and long wave infrared. And the instrument can interference imaging in 320×256 spatial resolution or other window size. This paper designs a set of calibration and test processes to realize the infrared spectral radiance measurement of target. Using this method, this paper test some typical infrared target. After the radiance calibration, the calibrated result is verified by standard radiance source. Thereby, the remote measurement of infrared background is taken as the comparison test. Finally, the typical infrared target spectral features are extracted and measured. The test results show that the method mentioned in this paper is practical.
Hyper-spectral images can not only provide spatial information but also a wealth of spectral information. A short list of applications includes environmental mapping, global change research, geological research, wetlands mapping, assessment of trafficability, plant and mineral identification and abundance estimation, crop analysis, and bathymetry. A crucial aspect of hyperspectral image analysis is the identification of materials present in an object or scene being imaged.
Classification of a hyperspectral image sequence amounts to identifying which pixels contain various spectrally distinct materials that have been specified by the user. Several techniques for classification of multi-hyperspectral pixels have been used from minimum distance and maximum likelihood classifiers to correlation matched filter-based approaches such as spectral signature matching and the spectral angle mapper.
In this paper, an improved hyperspectral images classification algorithm is proposed. In the proposed method, an improved similarity measurement method is applied, in which both the spectrum similarity and space similarity are considered. We use two different weighted matrix to estimate the spectrum similarity and space similarity between two pixels, respectively. And then whether these two pixels represent the same material can be determined. In order to reduce the computational cost the wavelet transform is also applied prior to extract the spectral and space features.
The proposed method is tested using hyperspectral imagery collected by the National Aeronautics and Space Administration Jet Propulsion Laboratory. Experimental results the efficiency of this new method on hyperspectral images associated with space object material identification.
Hyper-spectral remote sensing data can be acquired by imaging the same area with multiple wavelengths,
and it normally consists of hundreds of band-images. Hyper-spectral images can not only provide spatial
information but also high resolution spectral information, and it has been widely used in environment
monitoring, mineral investigation and military reconnaissance. However, because of the corresponding large
data volume, it is very difficult to transmit and store Hyper-spectral images. Hyper-spectral image dimensional
reduction technique is desired to resolve this problem. Because of the High relation and high redundancy of
the hyper-spectral bands, it is very feasible that applying the dimensional reduction method to compress the
data volume. This paper proposed a novel band selection-based dimension reduction method which can
adaptively select the bands which contain more information and details. The proposed method is based on the
principal component analysis (PCA), and then computes the index corresponding to every band. The indexes
obtained are then ranked in order of magnitude from large to small. Based on the threshold, system can
adaptively and reasonably select the bands. The proposed method can overcome the shortcomings induced
by transform-based dimension reduction method and prevent the original spectral information from being lost.
The performance of the proposed method has been validated by implementing several experiments. The
experimental results show that the proposed algorithm can reduce the dimensions of hyper-spectral image with
little information loss by adaptively selecting the band images.
Infrared imaging system has been applied widely in both military and civilian fields. Since the infrared imager has
various types and different parameters, for system manufacturers and customers, there is great demand for evaluating the
performance of IR imaging systems with a standard tool or platform. Since the first generation IR imager was developed,
the standard method to assess the performance has been the MRTD or related improved methods which are not perfect
adaptable for current linear scanning imager or 2D staring imager based on FPA detector.
For this problem, this paper describes an evaluation method based on the triangular orientation discrimination metric
which is considered as the effective and emerging method to evaluate the synthesis performance of EO system. To realize
the evaluation in field test, an experiment instrument is developed. And considering the importance of operational
environment, the field test is carried in practical atmospheric environment. The test imagers include panoramic imaging
system and staring imaging systems with different optics and detectors parameters (both cooled and uncooled). After
showing the instrument and experiment setup, the experiment results are shown. The target range performance is
analyzed and discussed. In data analysis part, the article gives the range prediction values obtained from TOD method,
MRTD method and practical experiment, and shows the analysis and results discussion. The experimental results prove
the effectiveness of this evaluation tool, and it can be taken as a platform to give the uniform performance prediction
Hyperspectral image analysis method is widely used in all kinds of application including agriculture identification and
forest investigation and atmospheric pollution monitoring. In order to accurately and steadily analyze hyperspectral
image, considering the spectrum and spatial information which is provided by hyperspectral data together is necessary.
The hyperspectral image has the characteristics of large amount of wave bands and information. Corresponding to the
characteristics of hyperspectral image, a fast image fusion method that can fuse the hyperspectral image with high
fidelity is studied and proposed in this paper. First of all, hyperspectral image is preprocessed before the morphological
close operation. The close operation is used to extract wave band characteristic to reduce dimensionality of hyperspectral
image. The spectral data is smoothed at the same time to avoid the discontinuity of the data by combination of spatial
information and spectral information. On this basis, Mean-shift method is adopted to register key frames. Finally, the
selected key frames by fused into one fusing image by the pyramid fusion method. The experiment results show that this
method can fuse hyper spectral image in high quality. The fused image’s attributes is better than the original spectral
images comparing to the spectral images and reach the objective of fusion.
Since infrared image quality depends on many factors such as optical performance and electrical noise of thermal imager, image quality evaluation becomes an important issue which can conduce to both image processing afterward and capability improving of thermal imager. There are two ways of infrared image quality evaluation, with or without reference image. For real-time thermal image, the method without reference image is preferred because it is difficult to get a standard image. Although there are various kinds of methods for evaluation, there is no general metric for image quality evaluation. This paper introduces a novel method to evaluate infrared image without reference image from five aspects: noise, clarity, information volume and levels, information in frequency domain and the capability of automatic target recognition. Generally, the basic image quality is obtained from the first four aspects, and the quality of target is acquired from the last aspect. The proposed method is tested on several infrared images captured by different thermal imagers. Calculate the indicators and compare with human vision results. The evaluation shows that this method successfully describes the characteristics of infrared image and the result is consistent with human vision system.
Since the infrared imaging system has played a significant role in the military self-defense system and fire control system, the radiation signature of IR target becomes an important topic in IR imaging application technology. IR target signature can be applied in target identification, especially for small and dim targets, as well as the target IR thermal design. To research and analyze the targets IR signature systematically, a practical and experimental project is processed under different backgrounds and conditions. An infrared radiation acquisition system based on a MWIR cooled thermal imager and a LWIR cooled thermal imager is developed to capture the digital infrared images. Furthermore, some instruments are introduced to provide other parameters. According to the original image data and the related parameters in a certain scene, the IR signature of interested target scene can be calculated. Different background and targets are measured with this approach, and a comparison experiment analysis shall be presented in this paper as an example. This practical experiment has proved the validation of this research work, and it is useful in detection performance evaluation and further target identification research.
The existence of non-uniformities in the responsitivity of the element array is a severe problem typical to common infrared detector. These non-uniformities result in a “curtain’’ like fixed pattern noises (FPN) that appear in the image. Some random noise can be restrained by the method kind of equalization method. But the fixed pattern noise can only be removed by .non uniformity correction method. The produce of non uniformities of detector array is the combined action of infrared detector array, readout circuit, semiconductor device performance, the amplifier circuit and optical system. Conventional linear correction techniques require costly recalibration due to the drift of the detector or changes in temperature. Therefore, an adaptive non-uniformity method is needed to solve this problem. A lot factors including detectors and environment conditions variety are considered to analyze and conduct the cause of detector drift. Several experiments are designed to verify the guess. Based on the experiments, an adaptive non-uniformity correction method is put forward in this paper. The strength of this method lies in its simplicity and low computational complexity. Extensive experimental results demonstrate the disadvantage of traditional non-uniformity correct method is conquered by the proposed scheme.
Using super-resolution (SR) technology is a good approach to obtain high-resolution infrared image. However, Image super-resolution reconstruction is essentially an ill-posed problem, it is important to design an effective regularization term (image prior). Gaussian prior is widely used in the regularization term, but the reconstructed SR image becomes over-smoothness. Here, a novel regularization term called non-local means (NLM) term is derived based on the assumption that the natural image content is likely to repeat itself within some neighborhood. In the proposed framework, the estimated high image is obtained by minimizing a cost function. The iteration method is applied to solve the optimum problem. With the progress of iteration, the regularization term is adaptively updated. The proposed algorithm has been tested in several experiments. The experimental results show that the proposed approach is robust and can reconstruct higher quality images both in quantitative term and perceptual effect.
This paper proposed a smoke detection method suitable for constant speed rotating platform. The movements of long
range scenes in images acquired by camera on rotating surveillance platform is approximate translation, the proposed
method uses a grid point based image patch matching to obtain the parameters of the translation, and compensates the
movements of scenes to generate short-time stable scenes for smoke detection. After the movement compensation of the
video, the smoke candidate regions are selected by two-stage background difference, color judgment and shadow
judgment. The two-stage background difference which takes advantage of fast background updating and slow
background updating is performed to detect slowly changing regions. The color judgment is applied to filter out
non-smoke color regions, for smoke always takes on gray colors. And the shadow judgment is to carry out by the light
color feature and the no texture changing feature of shadows. An image buffer pool is established for smoke diffusion
analysis where forward region trajectories and corresponding backward region trajectories are obtained. Those regions’
moving trajectories are obtained by a consecutive frame region matching method based on their corresponding position
distance and are filtered by a consistency constraint, after which, the translation, expanding, shrinking features of
remaining smoke candidate regions are attained, which are used for movements and diffusion judgments. The smoke
candidate regions passing all of above judgments are considered smoke regions. Experimental results show the proposed
method is prominent for forest smoke detection.
With constraints to the performance of the IR detector, IR image usually has lower visual effect with low contrast and less detailed information. In this paper, a new dynamic range infrared image details enhancement algorithm is studied, using a bilateral filter to extract a base component and a detail component. Then these two components are compressed to fit the display dynamic range and then recombined to obtain the output-enhancement image. This algorithm has solved the problem of ripple phenomenon which exists in the traditional infrared image digital detail enhancement. Finally, the algorithm described in this paper is proved experimentally that can provide better DDE effect.
The operation and observation experience of users is affected by the quality of infrared images which are collected by infrared imager. And image quality is a significant indicator for the performance of image processing algorithm and the optimization of system parameters as well. An image quality reduced reference assessment model is put forward to evaluate the degree of infrared image quality reduction. The detail characteristic of infrared image texture is extracted by the fractal dimension analysis method proposed in this paper as the representation of image quality. The method computes the fractal dimension of every pixel one by one with a multi-scale window over the entire image to get the information of corresponding image block. A quality information image is mapped from the fractal dimension of all pixels to describe the infrared image quality. The parameters of the quality information image combined with the peak SNR of original infrared image are adopted as the metric of infrared image quality. The method can be embedded into image processing system to optimize image processing algorithms and parameters settings, and provide reference for fault diagnosis.
Fire detection based on video surveillance is a very effective method for large area outdoor fire prevention, but the
unpredictable place and time makes automatic fire detection a difficult problem. This paper adopts a loose color
selection and frame differential to narrow down possible fire regions, where every pixel’s temporal color variations
are analyzed by 3-state Markov modals. One of the Markov modal is used for brightness variation examination and
the other one is used for fire color likeness that is measured by color difference. In order to eliminate false
detections, the fractal dimension calculation and texture match are performed. Experimental results prove the
proposed method is feasible and suitable for outdoor or indoor fire detection in surveillance videos.
This paper introduces a novel vehicle detection method combined with probability voting based hypothesis generation
(HG) and SVM based hypothesis verification (HV) specialized for the complex background airborne traffic video. In HG
stage, a statistic based road area extraction method is applied and the lane marks are eliminated. Remained areas are
clustered, and then the canny algorithm is performed to detect edges in clustered areas. A voting strategy is designed to
detect rectangle objects in the scene. In HV stage, every possible vehicle area is rotated to align the vehicle along the
vertical direction, and the vertical and horizontal gradients of them are calculated. SVM is adopted to classify vehicle
and non-vehicle. The proposed method has been applied to several traffic scenes, and the experiment results show it’s
effective and veracious for the vehicle detection.
Image registration is crucial in various image fusion tasks, like super-resolution. For the success of super-resolution
reconstruction, it is essential to find out high accurate subpixel motion estimation between the input images sequence. This paper proposes a frequency domain-based motion estimation algorithm for the under-sampled infrared images. The proposed algorithm which only considers pure translational motion is based on the phase-only correlation. Due to the discrete Fourier transform and subpixel displacement, the signal peak is not always concentrate in the integer coordinates. Thus, the signals adjacent to peak are utilized to estimate aliasing influence. Excellent results are obtained for subpixel translation estimation. The algorithm is also compared to other algorithms, and the analyses show that this algorithm is more robust and accurate.
This research indicated the experiment method to analyze and predict the effect of turbulence on the performance of IR
thermal imagers. First, the values of structure constant of refractive index, C<sub>n</sub> <sup>2</sup>, were measured. The C<sub>n</sub> <sup>2</sup> model used in
engineering applications is also introduced. And the calculated values were compared to the experiment data, so that the
model can be modified. Meanwhile, two IR thermal imagers in MWIR and LWIR bands were installed to provide the
data on the range performance. After that, the range values as a function of varying turbulence were calculated utilizing
the simulation tool. Finally, this paper analyzed the range values in different groups.