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.
Small target is also weak target, which is likely to be a threat to the observation platform. So small target detection is an important task for many automatic object detection system. Otherwise, small target detection is a challenge for many complex scenes because of the low SNR and sophisticated background. This paper introduced a fast and effective method for small target detection in infrared scene with complex background, which is suitable for missile guidance and menace warning. Firstly, a template is created to detect the local maxima in the image. Secondly, a constrained double criteria region growth algorithm is performed to form separate regions. Finally, extracted regions are selected by a small round target filter, after which, the remaining connected regions are considered to be detected small targets. The proposed algorithm was applied on videos captured by cooled infrared imagers. Experimental results show the method introduced in this paper is efficient and effective, which is suitable for time sensitive automatic target detection.
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.
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.
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.
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.
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.