Aiming at the problems existing in the effect evaluation of pattern painting camouflage, such as evaluating indicate simplification and nonobjectivity, this paper proposes a new method for effect evaluation of pattern painting camouflage based on entropy weighted similarity. According to the object and purpose of pattern painting camouflage assessment, five characteristic evaluating indicators of target image and its background, namely, hue, brightness, shape, texture and speckle, are selected synthetically, and the weight of each evaluating indicator affecting the whole evaluation result is determined by using entropy weight method. The different patterns of painting camouflage with some specific backgrounds are selected for comprehensive evaluation, meanwhile, these camouflage painting patterns are evaluated by the classic grey clustering decision algorithm, and finally the evaluating results of two methods are compared. The results show that the conclusions obtained by the two different evaluation methods are consistent and support mutual verification, which indicates that the proposed method in this paper is feasible and effective for the effect evaluation of pattern painting camouflage.
In remote sensing images, the common existence of mixed pixels affects the classification of remote sensing images and the targets detection. The N-FINDR algorithm is a widely used endmember extraction algorithm. The algorithm is full- automatic. it has no parameters and has a good selection effect. However, in the volume calculation, the random selection of the initial value of the spectral endmember limits the efficiency of the method. The dimensionality reduction of the data loses some details; The automatic target retrieval algorithm uses the method of orthogonal subspace projection to determine the feature pixels. The algorithm runs faster and is more efficient, but it does not consider the angle between the pixels. Therefore, an improved N-FINDR algorithm based on object retrieval initialization is proposed in this paper. The improved algorithm can select the initial endmember, and then use the new volume calculation formula to solve the maximum volume of the simplex, so as to obtain the endmember, which makes up for the shortcomings of the two methods. Finally, simulation data and real airport diagram are used to verify the algorithm. The improved N-FINDR algorithm is superior to the traditional internal maximum volume algorithm in terms of running time and extraction accuracy.
Hyperspectral remote sensing images contain not only spatial information, but also abundant spectral information, which are widely used in the field of space-spectrum joint target detection. Unlike other target detection algorithms, the anomaly detection doesn’t require any prior knowledge, and can effectively identify the pixels that stand out from the cluttered backgrounds in high spectral images. At the same time, compared with the background objects, the abnormal target is composed of sub-pixels and has distinctive spectral characteristics. In this paper, a new anomaly target detection algorithm based on Laplace of Gaussian (LoG) operator is proposed to solve the problem that spatial information is not fully utilized and the real-time detection capability is not strong. Firstly, the algorithm uses the LoG operator to obtain the target detection results under different bands with analyzing the spatial characteristics of the anomaly, combined with the blob detection theory which is widely used in the field of the image recognition field. The results are finished by the spatial filtering, which highlights the anomaly and effectively suppress the background. Then, a Boolean map-based fusion approach and morphological expansion theory is used to synthesize the detection results of different bands. In the end, the real AVIRIS Imagery and HYDICE Imagery are used for simulation, and the results show that the algorithm is with strong robustness, high detection probability and low false alarm rate.
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