An inverse synthetic aperture radar (ISAR) high-precision compensation method is proposed based on coherent processing of intermediate frequency direct sampling data. First, the compensation of high-speed movement is performed by a modified linear frequency modulation matched filter during the pulse compression. The motion trajectory in the down-range direction is then reconstructed by compensation of window sampling difference of each pulse. Modified envelope correlation is applied to calculate the range profile shift between each pulse and the first one. Polynomial fitting is adopted to accurately estimate the motion characteristics. Subsequently, coherent processing is applied by combining range alignment and initial phase compensation. The migration through range cells correction can be then realized by keystone transform to the highly coherent data. Consequently, ISAR images with high quality are achieved. Experimental results on simulated and real data have demonstrated the validity of the proposed method.

The most challenging aspect of through-wall imaging is the presence of the wall, which suppresses the detection and localization to the obscured scatterers. Moreover, wall parameters are not known in practice; how to detect the target without prior knowledge of the wall is becoming important. Therefore, an approach is proposed to solve this problem in this paper. First, an effective clutter mitigation method based on singular value decomposition is proposed to achieve the target scattering fields. After a number of data pairs that consist of the target position and its scattering fields have been collected, the through-wall detection problem can be resolved by extracting a nonlinear relationship between them. In this way, the presence of the wall is automatically included in the nonlinear relationship, which is obtained through a training phase using a support vector machine. For a detection task, the position of the target can be estimated from this nonlinear relationship. The whole detection procedure does not require the prior knowledge of the wall. Also, it is shown that the proposed method is effective. Moreover, the impacts of training samples and signal-to-noise ratio on detection accuracy are analyzed.

By exploiting the sparsity of radar target image, it is hopeful to obtain a high-resolution target image in multiple-input-multiple-output (MIMO) radar via a sparse representation (SR) method. However, for the three-dimensional (3-D) imaging, the conventional SR method has to convert the 3-D problem into the one-dimensional (1-D) problem. Thus, it will inevitably impose a heavy burden on the storage and computation. A multidimensional smoothed L0 (MD-SL0) algorithm is proposed based on the conventional smoothed L0 algorithm. The proposed MD-SL0 can directly apply to the multidimensional SR problem without transforming to the 1-D case. As a result, a MIMO radar 3-D imaging method via MD-SL0 is achieved with high computation efficiency and low storage burden. Finally, the effectiveness of the method is validated by the results of comparative experiments.

This paper aims to implement an iterative fuzzy edge detection (IFED) method on blurred satellite images. Some degradation effects such as atmospheric effects, clouds and their shadows, atmospheric aerosols, and fog remarkably decline the quality satellite images. Hence, some processes such as enhancement and edge detection in satellite images are challenging. One group of methods that can deal with these effects is fuzzy logic methods. Therefore, IFED method was applied in this work on the subimages of the Ikonos, Landsat 7, and SPOT 5 satellite images, contaminated by aforementioned effects. Such as most FED methods, IFED has two components: enhancement and edge detection. In this context, a six-step iterative method, using the if-then-else mechanism, was implemented on the images to perform fuzzy enhancement, and subsequently, edge detection was done. To evaluate the merit of the enhancement and select the best number of iterations, edge gray-value rate criterion was applied. The peak signal-to-noise ratio (PSNR) is applied for the quantitative evaluation of the IFED method. The results of IFED, in comparison with some prior edge detection methods, showed higher PSNR values and a high performance in the edge detection of the earth features in the blurred satellite images.

Image smear, produced by the shutter-less operation of full-frame charge-coupled device (CCD) sensors, greatly affects the performance of target detection, the centering accuracy, and visual magnitude estimation. We study the operation principle of full-frame CCDs, analyze the cause and properties of smear effect, and propose a smear removal algorithm for star images of full-frame CCDs. The proposed method locates the smears and extracts the rough profiles of the smeared stars by finding the conditional extrema. Then Gaussian fitting is applied to accurately extract the stars, in order to maintain the integrity of star images while minimizing the smear effect. The extraction of smears and stars requires parameters such as the size of the CCD, the integration time and the readout time, as well as the estimation of background noise. We assess the performance of our scheme with real observed data. The experimental results show that the proposed scheme improves the average signal-to-noise ratio of the images by about 22%, presenting better smear removal performance compared with several published methods. The limitation of the proposed algorithm includes the difficulty of distinguishing between two very close stars displaying the gray level of a single peak and overestimation of the background noise may also influence the performance of the algorithm.

Doppler radar is a cost-effective tool for moving target tracking, which can support a large range of civilian and military applications. A modified linear predictive coding (LPC) approach is proposed to increase the target localization accuracy of the Doppler radar. Based on the time-frequency analysis of the received echo, the proposed approach first real-time estimates the noise statistical parameters and constructs an adaptive filter to intelligently suppress the noise interference. Then, a linear predictive model is applied to extend the available data, which can help improve the resolution of the target localization result. Compared with the traditional LPC method, which empirically decides the extension data length, the proposed approach develops an error array to evaluate the prediction accuracy and thus, adjust the optimum extension data length intelligently. Finally, the prediction error array is superimposed with the predictor output to correct the prediction error. A series of experiments are conducted to illustrate the validity and performance of the proposed techniques.

Land cover classification based on remote sensing imagery is an important means to monitor, evaluate, and manage land resources. However, it requires robust classification methods that allow accurate mapping of complex land cover categories. Random forest (RF) is a powerful machine-learning classifier that can be used in land remote sensing. However, two important parameters of RF classification, namely, the number of trees and the number of variables tried at each split, affect classification accuracy. Thus, optimal parameter selection is an inevitable problem in RF-based image classification. This study uses the genetic algorithm (GA) to optimize the two parameters of RF to produce optimal land cover classification accuracy. HJ-1B CCD2 image data are used to classify six different land cover categories in Changping, Beijing, China. Experimental results show that GA-RF can avoid arbitrariness in the selection of parameters. The experiments also compare land cover classification results by using GA-RF method, traditional RF method (with default parameters), and support vector machine method. When the GA-RF method is used, classification accuracies, respectively, improved by 1.02% and 6.64%. The comparison results show that GA-RF is a feasible solution for land cover classification without compromising accuracy or incurring excessive time.

A spectrum reconstruction algorithm based on space–time adaptive processing (STAP) can effectively suppress azimuth ambiguity for multichannel synthetic aperture radar (SAR) systems in azimuth. However, the traditional STAP-based reconstruction approach has to estimate the covariance matrix and calculate matrix inversion (MI) for each Doppler frequency bin, which will result in a very large computational load. In addition, the traditional STAP-based approach has to know the exact platform velocity, pulse repetition frequency, and array configuration. Errors involving these parameters will significantly degrade the performance of ambiguity suppression. A modified STAP-based approach to solve these problems is presented. The traditional array steering vectors and corresponding covariance matrices are Doppler-variant in the range-Doppler domain. After preprocessing by a proposed phase compensation method, they would be independent of Doppler bins. Therefore, the modified STAP-based approach needs to estimate the covariance matrix and calculate MI only once. The computation load could be greatly reduced. Moreover, by combining the reconstruction method and a proposed adaptive parameter estimation method, the modified method is able to successfully achieve multichannel SAR signal reconstruction and suppress azimuth ambiguity without knowing the above parameters. Theoretical analysis and experiments showed the simplicity and efficiency of the proposed methods.

As far back as early 15th century during the reign of the Ming Dynasty (1368 to 1634 AD), Gomantong cave in Sabah (Malaysia) has been known as one of the largest roosting sites for wrinkle-lipped bats (Chaerephon plicata) and swiftlet birds (Aerodramus maximus and Aerodramus fuciphagus) in very large colonies. Until recently, no study has been done to quantify or estimate the colony sizes of these inhabitants in spite of the grave danger posed to this avifauna by human activities and potential habitat loss to postspeleogenetic processes. This paper evaluates the transferability of a hybrid optimization image analysis-based method developed to detect and count cave roosting birds. The method utilizes high-resolution terrestrial laser scanning intensity image. First, segmentation parameters were optimized by integrating objective function and the statistical Taguchi methods. Thereafter, the optimized parameters were used as input into the segmentation and classification processes using two images selected from Simud Hitam (lower cave) and Simud Putih (upper cave) of the Gomantong cave. The result shows that the method is capable of detecting birds (and bats) from the image for accurate population censusing. A total number of 9998 swiftlet birds were counted from the first image while 1132 comprising of both bats and birds were obtained from the second image. Furthermore, the transferability evaluation yielded overall accuracies of 0.93 and 0.94 (area under receiver operating characteristic curve) for the first and second image, respectively, with p value of <0.0001 at 95% confidence level. The findings indicate that the method is not only efficient for the detection and counting cave birds for which it was developed for but also useful for counting bats; thus, it can be adopted in any cave.

NASA has been planning a hyperspectral infrared imager mission which will provide global coverage using a hyperspectral imager with 60-m resolution. In some practical applications, such as special crop monitoring or mineral mapping, 60-m resolution may still be too coarse. There have been many pansharpening algorithms for hyperspectral images by fusing high-resolution (HR) panchromatic or multispectral images with low-resolution (LR) hyperspectral images. We propose an approach to generating HR hyperspectral images by fusing high spatial resolution color images with low spatial resolution hyperspectral images. The idea is called hybrid color mapping (HCM) and involves a mapping between a high spatial resolution color image and a low spatial resolution hyperspectral image. Several variants of the color mapping idea, including global, local, and hybrid, are proposed and investigated. It was found that the local HCM yielded the best performance. Comparison of the local HCM with >10 state-of-the-art algorithms using five performance metrics has been carried out using actual images from the air force and NASA. Although our HCM method does not require a point spread function (PSF), our results are comparable to or better than those methods that do require PSF. More importantly, our performance is better than most if not all methods that do not require PSF. After applying our HCM algorithm, not only the visual performance of the hyperspectral image has been significantly improved, but the target classification performance has also been improved. Another advantage of our technique is that it is very efficient and can be easily parallelized. Hence, our algorithm is very suitable for real-time applications.

For better using of inverse synthetic aperture radar (ISAR) images of ship targets, it is more desirable to select a proper imaging time to obtain high quality top-view or side-view images. However, optimum imaging time selection is not robust enough for the restriction of traditional geometric feature extraction methods. In our study, we propose a method based on the geometric features and gradient maximization. First, we select the imaging instant from radar echoes by the centerline and mainmast of the ship. In this part, we propose a geometric features extraction method to improve the robustness of instant selection in different scenarios. Then, an image gradient maximization is employed to estimate the period for ISAR imaging. Finally, experimental results of both simulated and real signals are provided to demonstrate the effectiveness and practicability of the algorithm.