Target Recognition Rate Research Articles

基于纹理特征的“猫眼”效应目标识别方法

In order to overcome the low recognition rate of cat-eyeeffect target and ineffective distinction of car lights and telescopic sights,a recognition method ofcat-eyeeffect target based on texture character was proposed.Firstly,car lights were removed by filtering the difference images with the column-mean-subtraction,EHPF and median filter.Secondly,the discrimination was carried out in terms of the features of the shape of thecat-eyeeffect target.Finally,according to the texture features of the cat-eyeeffect target,membership vectors were constructed,and the fuzzy comprehensive evaluation was used to recognize thecat-eyeeffect target under the maximum membership degree principle.Experimental results indicate that the proposed method reduces false alarm rate and improves recognition rate compared with the shape-frequency dual criterions(SFDC)method.

SAR Images Recognition Combined Bidirectional 2DPCA with KPCA

The bidirectional 2DPCA (two-dimensional principal component analysis) method for SAR images recognition, can compress the columns and rows of images matrix and reduce the number of feature dimensions. However, it fails to use high order statistics information of image data, neglects the nonlinearity correlation between pixels. Therefore, this paper presents the method combined bidirectional 2DPCA with KPCA (Kernel Principal Component Analysis). This method not only compresses the dimensions of images data, but develops the superiority of KPCA in describing correlation between many pixels. Experimental results show that: this method can decrease calculated amount and raise recognition rate of SAR target effectively.

다중센서 영상 기반의 지상 표적 분류 알고리즘

본 논문은 다중센서 영상을 이용한 결정 융합 기반의 지상 표적 분류 알고리즘 및 특징 추출 기법을 제안한다. 표적의 인식률 향상을 위하여 가중 투표 방법을 적용함으로써 개별 분류기로부터 획득된 결과를 융합하였다. 또한 개별 센서 영상 내에 속한 표적을 분류하기 위해 CCD 영상으로부터 획득한 CM 영상의 밝기 차이와 FLIR 영상 내 표적의 윤곽선 정보 및 차량과 포탑의 너비 비율을 이용하여 스케일과 회전변화에 강인한 특징들을 추출하였다. 마지막으로 실험을 통하여 본 논문에서 제안한 지상 표적 분류 알고리즘과 특징 추출 기법에 대한 성능을 검증한다. This paper proposes ground target classification algorithm based on decision fusion and feature extraction method using multi-sensor images. The decisions obtained from the individual classifiers are fused by applying a weighted voting method to improve target recognition rate. For classifying the targets belong to the individual sensors images, features robust to scale and rotation are extracted using the difference of brightness of CM images obtained from CCD image and the boundary similarity and the width ratio between the vehicle body and turret of target in FLIR image. Finally, we verity the performance of proposed ground target classification algorithm and feature extraction method by the experimentation.

Classifying ships by their acoustic signals with a cross-bispectrum algorithm and a radial basis function neural network

An algorithm for estimating the cross-bispectrum of an acoustic vector signal was formulated. Composed features of sound pressure and acoustic vector signals are extracted by the proposed algorithm and other estimating algorithms for secondary and higher order spectra. Its effectiveness was tested with lake and sea trial data. These features can be used to construct an input vector set for a radial basis function neural network. The classification of vessels can then be made based on the extracted features. It was shown that the composed features of acoustic vector signals are more easily divided into categories than those of pressure signals. When using the composed features of acoustic vector signals, the recognition rate of underwater acoustic targets improves.