Classification of ISAR Images Using Variable Cross-Range Resolutions

Lee, Seungjae; Lee, Myung-Jun; Kim, Kyung‐Tae; Bae, Ji‐Hoon

doi:10.1109/taes.2018.2814211

Cited by 20 publications

(19 citation statements)

References 16 publications

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“…Let the scattered field signal at a specific slant-range bin (in the case of the azimuth frequency spectrum) or azimuth bin (in the case of the slant-range frequency spectrum) be noted by

;

, where

is either

. The AR model assumes that

is a sum of undamped exponentials [ 11 , 16 , 17 ]. In the AR model,

should satisfy the following forward and backward linear prediction conditions along the slant-range frequency or azimuth frequency directions:

where

denotes the complex conjugate,

can be either

(in the case of the AR model in the slant-range frequency direction) or

(in the case of the AR model in the azimuth frequency direction),

denotes the coefficients of the AR model,

is the AR model order, and

is the estimated data using forward or backward prediction.…”

Section: Generation Of Super-resolved Target Image From Large-scale K...mentioning

confidence: 99%

“…The SR algorithms in [ 9 , 10 , 11 , 12 , 13 , 14 , 15 ] are based on high-resolution spectral estimation (SE) techniques, such as autoregressive (AR) model-based linear prediction (LP), multiple signal classification (MUSIC), estimation of signal parameters via rotational invariance techniques (ESPRIT), and relaxation (RELAX), which successfully generate super-resolved radar images for various targets, solving the limitations of predetermined spatial resolutions. In addition, some studies have demonstrated that super-resolved target images can enhance target recognition capabilities [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

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Super-Resolution Procedure for Target Responses in KOMPSAT-5 Images

Lee

2022

Sensors

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Recently, target analysis using satellite SAR images has received much attention in the area of satellite SAR remote sensing. Because the spatial resolution of the target response in the satellite SAR image is a main factor that has a large effect on target analysis performances, the improvement of the spatial resolution of target response is required to enhance the target analysis capability. However, the spatial resolution is already determined in the satellite SAR system design process. To solve the above problem, the super-resolution techniques that have been applied to radar images can be utilized. However, the application of the super-resolution techniques to the target response in the satellite SAR image is not simple due to the following reasons. First, the target’s motion induces severe blurring of the target response, which impedes the successful improvement of spatial resolution. Next, the zero-region in the frequency spectrum of the target image containing the target response also hinders the generation of the super-resolved image. To successfully improve spatial resolution of the satellite SAR image, the super-resolution techniques should be combined with proper preprocessing steps that can cope with the above two issues. In this paper, the whole super-resolution procedure for target responses in KOMPSAT-5 images is described. To the best of the authors’ knowledge, the description of the whole super-resolution procedure for target responses is the first ever attempt in the area of satellite SAR. First, a target image containing the target response is extracted from a large-scale KOMPSAT-5 image. Subsequently, the target image is transformed to be appropriate for the utilization of super-resolution techniques by proper preprocessing steps, considering the direction of super resolution and the motion of the target. Then, some super-resolution techniques are utilized to improve the spatial resolutions and qualities of the target images. The super-resolution performances of the proposed scheme are validated using various target images for point static, extended static, and extended moving targets. The novelties of this paper can be summarized as follows: (1) the practical design of whole super-resolution processing for real satellite SAR images; (2) the performance evaluation of super-resolution techniques on real satellite SAR images. The results show that the proposed scheme can led to noticeable improvements of spatial resolution of the target images for various types of targets with reliable computation times. In addition, the proposed scheme also enhanced PSLR, ISLR, and IC, leading to clearer scattering information of the principal scatterers. Consequently, the proposed method can assist in extracting more precise and meaningful information for targets represented in KOMPSAT-5 images, which means great potential for target recognition.

show abstract

“…Let the scattered field signal at a specific slant-range bin (in the case of the azimuth frequency spectrum) or azimuth bin (in the case of the slant-range frequency spectrum) be noted by

;

, where

is either

. The AR model assumes that

is a sum of undamped exponentials [ 11 , 16 , 17 ]. In the AR model,

should satisfy the following forward and backward linear prediction conditions along the slant-range frequency or azimuth frequency directions:

where

denotes the complex conjugate,

can be either

(in the case of the AR model in the slant-range frequency direction) or

(in the case of the AR model in the azimuth frequency direction),

denotes the coefficients of the AR model,

is the AR model order, and

is the estimated data using forward or backward prediction.…”

Section: Generation Of Super-resolved Target Image From Large-scale K...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Super-Resolution Procedure for Target Responses in KOMPSAT-5 Images

Lee

2022

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Compared with the traditional Radar Cross-Section (RCS), High-Resolution Range Profile (HRRP), and Micro-Doppler Signature (MDS), the ISAR image can provide more abundant information of the target. Therefore, the use of ISAR images for space target recognition has always been a research hotspot in the field of space situational awareness [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

“…It is mainly reflected in the following six aspects: (1) The ISAR image is different from a traditional optical image and usually more difficult to understand. (2) Due to factors such as speckle noise and interference fringes, the quality of the ISAR image will decrease to varying degrees. 3The ISAR image usually appears as a sparse or isolated scattering center distribution.…”

Section: Introductionmentioning

confidence: 99%

A Fast Recognition Method for Space Targets in ISAR Images Based on Local and Global Structural Fusion Features with Lower Dimensions

Yang

Zhang

Ding

2020

International Journal of Aerospace Engineering

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Feature extraction is the key step of Inverse Synthetic Aperture Radar (ISAR) image recognition. However, limited by the cost and conditions of ISAR image acquisition, it is relatively difficult to obtain large-scale sample data, which makes it difficult to obtain target deep features with good discriminability by using the currently popular deep learning method. In this paper, a new method for low-dimensional, strongly robust, and fast space target ISAR image recognition based on local and global structural feature fusion is proposed. This method performs the trace transformation along the longest axis of the ISAR image to generate the global trace feature of the space target ISAR image. By introducing the local structural feature, Local Binary Pattern (LBP), the complementary fusion of the global and local features is achieved, which makes up for the missing structural information of the trace feature and ensures the integrity of the ISAR image feature information. The representation of trace and LBP features in a low-dimensional mapping feature space is found by using the manifold learning method. Under the condition of maintaining the local neighborhood relationship in the original feature space, the effective fusion of trace and LBP features is achieved. So, in the practical application process, the target recognition accuracy is no longer affected by trace function, LBP feature block number selection, and other factors, realizing the high robustness of the algorithm. To verify the effectiveness of the proposed algorithm, an ISAR image database containing 1325 samples of 5 types of space targets is used for experiments. The results show that the classification accuracy of the 5 types of space targets can reach more than 99%, and the recognition accuracy is no longer affected by the trace feature and LBP feature selection, which has strong robustness. The proposed method provides a fast and effective high-precision model for space target feature extraction, which can give some references for solving the problem of space object efficient identification under the condition of small sample data.

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“…The ISAR imaging plays an important role in both military and civilian applications. Especially, it is normally used for target recognition and classification, since the ISAR image is the essential prerequisite for the subsequent feature extraction and recognition of the target [5–7]. Therefore, for many application scenarios, the high‐quality and real‐time performance are two crucial indicators for ISAR imaging.…”

Section: Introductionmentioning

confidence: 99%