Range anomaly suppression based on neighborhood pixels detection in ladar range images

Research on the range anomaly suppression algorithm in laser radar （ladar） range images is significant in the application and development of ladar. But most of existing algorithms cannot protect the edge and linear target well while suppressing the range anomaly. Aiming at this problem, the differences among the edge, linear target, and range anomaly are analyzed and a novel algo- rithm based on neighborhood pixels detection is proposed. Firstly, the range differences between current pixel and its neighborhood pixels are calculated. Then, the number of neighborhood pixels is detected by the range difference threshold. Finally, whether the current pixel is a range anomaly is distinguished by the neighbor- hood pixel number threshold. Experimental results show that the new algorithm not only has a better range anomaly suppression performance and higher efficiency, but also protects the edge and linear target preferably compared with other algorithms.

Discoloration Range and Shroud Image Depth Values Cannot Be Satisfied by the Same Proton Energy

The radiative hypothesis has been revisited showing other characteristics, produced by the protons used as dyes in total disagree with the ones of the Body Image that appears on the Shroud of Turin. Our investigations highlight that for the protons to reach 3.7 cm in air, the distance that measures the range of discoloration effects, must be emitted with an energy of about 1.5 MeV using Wilson and Brobeck’s empirical formula and 1.35 MeV using Bethe’s. This last formula provides a result closer to reality. Bethe shows that the penetration depth is greater than that calculated empirically. Such a value of proton energy (1.35 MeV) makes it possible to satisfy the discoloration effects range for the Shroud but it is incompatible with a depth of penetration in linen that is only 200 nm. Moreover, using the same subatomic particles, we obtained on the colored linen a distribution of energy represented by regression but not linear. Thus, also the possible I(z) correlation, between color intensity and body-sheet distance, which should be due to the oxidizing action of protons, does not agree with that extracted from the Shroud of Turin.

Comparison of Microwave Imaging Algorithms for Short-Range Scenarios

Three dimensional(3-D)imaging algorithms with irregular planar multiple-input-multiple-output(MIMO)arrays are discussed and compared with each other.Based on the same MIMO array,a modified back projection algorithm(MBPA)is accordingly proposed and four imaging algorithms are used for comparison,back-projection method(BP),back-projection one in time domain(BP-TD),modified back-projection one and fast Fourier transform(FFT)-based MIMO range migration algorithm(FFT-based MIMO RMA).All of the algorithms have been implemented in practical application scenarios by use of the proposed imaging system.Back to the practical applications,MIMO array-based imaging system with wide-bandwidth properties provides an efficient tool to detect objects hidden behind a wall.An MIMO imaging radar system,composed of a vector network analyzer(VNA),a set of switches,and an array of Vivaldi antennas,have been designed,fabricated,and tested.Then,these algorithms have been applied to measured data collected in different scenarios constituted by five metallic spheres in the absence and in the presence of a wall between the antennas and the targets in simulation and pliers in free space for experimental test.Finally,the focusing properties and time consumption of the above algorithms are compared.

Lookup Table Hough Transform for Real Time Range Image Segmentation and Featureless Co-Registration

The paper addresses range image segmentation, particularly of data recorded by range cameras, such as the Microsoft Kinect and the Mesa Swissranger SR4000. These devices record range images at video frame rates and allow for acquisition of 3-dimensional measurement sequences that can be used for 3D reconstruction of indoor environments from moving platforms. The role of segmentation is twofold. First the necessary image co-registration can be based on corresponding segments, instead of corresponding point features (which is common practice currently). Secondly, the segments can be used during subsequent object modelling. By realisising that planar regions in disparity images can be modelled as linear functions of the image coordinates, having integer values for both domain and range, the paper introduces a lookup table based implementation of local Hough transform, allowing to obtain good segmentation results at high speeds.

Fast recognition using convolutional neural network for the coal particle density range based on images captured under multiple light sources

A method based on multiple images captured under different light sources at different incident angles was developed to recognize the coal density range in this study.The innovation is that two new images were constructed based on images captured under four single light sources.Reconstruction image 1 was constructed by fusing greyscale versions of the original images into one image,and Reconstruction image2 was constructed based on the differences between the images captured under the different light sources.Subsequently,the four original images and two reconstructed images were input into the convolutional neural network AlexNet to recognize the density range in three cases:-1.5(clean coal) and+1.5 g/cm^(3)(non-clean coal);-1.8(non-gangue) and+1.8 g/cm^(3)(gangue);-1.5(clean coal),1.5-1.8(middlings),and+1.8 g/cm^(3)(gangue).The results show the following:(1) The reconstructed images,especially Reconstruction image 2,can effectively improve the recognition accuracy for the coal density range compared with images captured under single light source.(2) The recognition accuracies for gangue and non-gangue,clean coal and non-clean coal,and clean coal,middlings,and gangue reached88.44%,86.72% and 77.08%,respectively.(3) The recognition accuracy increases as the density moves further away from the boundary density.

Pre-processing for time domain image formation in SS-BSAR system

A pre-processing procedure is designed for a space-surface bistatic synthetic aperture radar （SS-BSAR） system when a time domain image formation algorithm is employed. Three crucial technical issues relating to the procedure are fully discussed. Firstly, unlike image formation algorithms operating in the frequency domain, a time domain algorithm requires the accurate global navigation satellite system （GNSS） time and position. This paper proposes acquisition of this information using a time-and-spatial transfer with precise ephemeris and interpolation. Secondly, synchronization errors and compensation methods in SS-BSAR are analyzed. Finally, taking the non-ideal factors in the echo and the compatibility of image formation algorithms into account, a matched filter based on the minimum delay is constructed. Experimental result using real data suggest the pre-processing is functioning properly.

High Resolution SAR Image Algorithm with Sample Length Constraints for the Range Direction

The traditional Range Doppler(RD)algorithm is unable to meet practical needs owing to the limit of resolution.The order of fractional Fourier Transform(FrFT)and the length of sampling signals affect SAR imaging performance when FrFT is applied to RD algorithm.To overcome the above shortcomings,the purpose of this paper is to propose a high-resolution SAR image algorithm by using the optimal order of FrFT and the sample length constraints for the range direction.The expression of the optimal order of SAR range signals via FrFT is deduced in detail.The initial sample length and its constraints are proposed to obtain the best sample length of SAR range signals.Experimental results demonstrate that,when the range sampling-length changes in a certain interval,the best sampling-length will be obtained,which the best values of the range resolution,PSLR and ISLR,will be derived respectively.Compared with traditional RD algorithm,the main-lobe width of the peak-point target of the proposed algorithm is narrow in the range direction.While the peak amplitude of the first side-lobe is reduced significantly,those of other side-lobes also drop in various degrees.

SEGMENTATION OF RANGE IMAGE BASED ON KOHONEN NEURAL NETWORK

This paper presents an unsupervised range image segmentation based on Kohonen neural network. At first, the derivative and partial derivative of each point are calculated and the normal in each points is gotten. With the character vectors including normal and range value, self-organization map is introduced to cluster. The normal analysis is used to eliminate over-segmentation and the last result is gotten. This method avoid selecting original seeds and uses fewer samples, moreover computes rapidly. The experiment shows the better performance.

A Method for Registering Multiple Range Images

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Distortion-Free Data Embedding Scheme for High Dynamic Range Images

Distortion-free data embedding is a technique which can assure that not only the secret data is correctly extracted but also the cover media is recovered without any distortion after secret data is extracted completely. Because of these advantages, this technique attracts the attention of many researchers. In this paper, a new distortion-free data embedding scheme for high dynamic range （HDR） images is proposed. By depending on Cartesian product, this scheme can obtain higher embedding capacity while maintaining the exactly identical cover image and stego image when using the tone mapping algorithms. In experimental results, the proposed scheme is superior to Yu et aL＇s scheme in regard to the embedding rate——an average embedding rate of 0.1355 bpp compared with Yn et aL＇s scheme （0.1270 bpp）.

Fast Scheme for Projective Geometric Correction and Edge Blending Based on High Dynamic Range Images

With the development of graphic processing unit(GPU)power,it is now possible to implement geometric correction and edge blending functions on a single computer.However,the processing resources consumed by the geometric correction and edge blending phases still burden the system and slow down the main application considerably.A new platform independent scheme is proposed,minimizing the negative influence on performance.In this scheme,parameters for geometric correction and edge blending are firstly defined in an interactive way and recorded as a 32-bit high dynamic range(HDR) image,which is then used by high level shading language(HLSL) codes embedded in the main application as a lookup table,greatly reducing the computational complexity and enhancing flexibility.

Edge detection of range images using genetic neural networks

Due to the complexity and asymmetrical illumination, the images of object are difficult to be effectively segmented by some routine method. In this paper, a kind of edge detection method based on image features and genetic algorithms neural network for range images was proposed. Fully considering the essential difference between an edge point and a noise point, some characteristic parameters were extracted from range maps as the input nodes of the network in the algorithm. Firstly, a genetic neural network was designed and implemented. The neural network is trained by genetic algorithm, and then genetic neural network algorithm is combined with the virtue of global optimization of genetic algorithm and the virtue of parallel computation of neural network, so that this algorithm is of good global property. The experimental results show that this method can get much faster and more accurate detection results than the classical differential algorithm, and has better anti-noise performance.

Multi-Scale Feature Extraction for Joint Classification of Hyperspectral and LiDAR Data

With the development of sensors,the application of multi-source remote sensing data has been widely concerned.Since hyperspectral image(HSI)contains rich spectral information while light detection and ranging(LiDAR)data contains elevation information,joint use of them for ground object classification can yield positive results,especially by building deep networks.Fortu-nately,multi-scale deep networks allow to expand the receptive fields of convolution without causing the computational and training problems associated with simply adding more network layers.In this work,a multi-scale feature fusion network is proposed for the joint classification of HSI and LiDAR data.First,we design a multi-scale spatial feature extraction module with cross-channel connections,by which spatial information of HSI data and elevation information of LiDAR data are extracted and fused.In addition,a multi-scale spectral feature extraction module is employed to extract the multi-scale spectral features of HSI data.Finally,joint multi-scale features are obtained by weighting and concatenation operations and then fed into the classifier.To verify the effective-ness of the proposed network,experiments are carried out on the MUUFL Gulfport and Trento datasets.The experimental results demonstrate that the classification performance of the proposed method is superior to that of other state-of-the-art methods.

Shape recovery using high dynamic range images

An effective method for object shape recovery using HDRIs (high dynamic range images) is pro-posed.The radiance values of each point on the reference sphere and target object are firstly calculated,thus the set of candidate normals of each target point are found by comparing its radiance to that of eachreference sphere point.In single-image shape recovery,a smoothness operation is applied to the targetnonnals to obtain a stable and reasonable result;while in photometric stereo,radiance vectors of refer-ence and target objeets formed due to illuminations under different light source direetions are directly com-pared to get the most suitable target normals.Finally,the height values ean be recovered from the result-ing normal field.Because diffuse and specular refleetion are handled in an unified framework with radi-ance,our approach eliminates the limitation presented in most reeovery strategies,i.e.,only Lambertianmodel can be used.The experiment results from the real and synthesized images show the performance ofour approach.

High Dynamic Range Image Fusion Based on Wavelet

With the developpment of image fusion technology and the maturity of wavelet theory, wavelet transform with its good time-frequency characteristics stands out in the field of image fusion. On the basis of wavelet transforms theory, this article proposes a high dynamic range imaging confusion method which combines with wavelet decomposition. First, perform a wavelet multi-scale decomposition to the two registered source image; then conduct wavelet inverse transform to the decomposed images. This paper focuses on the characteristics of high frequency and low frequency domain after wavelet decomposition,using different fusion methods in each of the frequency domain, finally obtain the fused image through inverse wavelet transform image reconstruction. The simulation results and evaluation index results show that, compared with other similar methods, this method is better in retaining the original image's details information, and improves the quality of fusion image.

激光距离选通三维成像技术研究进展(特邀)

随着人工智能时代的到来,同时获得反映目标辐射特性和纹理特征的高分辨率强度图像以及反映目标和所处场景的三维空间信息的稠密点云数据/三维图像的激光相机雷达技术已成为激光雷达的发展趋势。传统的摄像机与激光雷达复合的技术方案存在异源数据融合问题,尤其是在雾雨雪天气条件下以及水下等传输链路中存在严重散射的情况时难以有效工作。激光距离选通三维成像技术利用单一门控成像器件可同时获得高质量二维强度图像和高分辨率三维图像,其二维图像中的像素和三维图像中的体素一一对应,并继承了激光距离选通成像透散射成像的技术优势,具有实现高性能激光相机雷达的技术潜力。论文系统综述了步进延时扫描、增益调制、距离能量相关等激光距离选通三维成像技术的研究进展,介绍了该类技术的国内外典型应用情况,最后分析了该领域所面临的挑战及进一步发展方向和应用前景。

盖革APD激光雷达高空间分辨率三维重构方法研究

盖革模式阵列APD激光雷达由于像元数的限制导致重构的三维距离像分辨率低,严重限制了对场景信息的感知识别能力。为此,提出一种基于改进图像引导的盖革模式阵列APD激光雷达超分辨三维图像重构算法。主要研究基于配准后的盖革APD低分辨距离图像、增强电荷耦合器件(ICCD)相机高分辨强度图像和马尔科夫随机场模型,建立具有距离保真项和正则化项约束的全局能量函数,最终对优化模型的最优化求解实现高分辨强度图像对低分辨距离图像的超分辨引导重构。实验结果表明,相较于现有算法,文中算法在提升重构三维图像分辨率的同时,也使图像的边缘更加锐利。该研究对于盖革模式阵列APD激光雷达的目标探测、识别和跟踪具有重要意义。

火光烟雾条件下无人机激光探测与跟踪实验研究

高能激光在打击低小慢目标时,目标容易燃烧产生火光烟雾,传统的可见光和红外探测方式对目标的跟踪和瞄准在火光烟雾干扰的情况下易受影响进而导致目标失跟。提出了一种基于主动激光雷达体制的高精度探测和瞄准方法。首先,对火光烟雾条件下无人机表面的激光反射特性进行理论分析和仿真模拟,以此设计了基于APD的单光子激光雷达探测系统,获得了仿真探测概率随激光脉冲能量变化的理论曲线;其次,构建了基于InGaAs-SPAD的光子成像探测系统,进行了无人机室内实验。实验结果表明:在无火光烟雾条件下,基于距离像跟踪的目标质心位置相较于基于可见光图像跟踪的目标质心位置平均角偏差小于0.55 mrad,基于距离像序列的跟踪轨迹与基于可见光图像序列的跟踪轨迹基本一致,证明了Mean-Shift跟踪算法用于距离像的跟踪的可行性。在距离30 m处的模拟烟雾干扰条件下,采用选通延时滤除烟雾干扰能够获得轮廓清晰的目标距离像。在模拟火光干扰条件下,跟踪框中心X、Y坐标偏离目标质心约为0.58 mrad和0.39 mrad。

基于Transformer和动态3D卷积的多源遥感图像分类

多源遥感数据具有互补性和协同性,近年来,基于深度学习的方法已经在多源遥感图像分类中取得了一定进展,但当前方法仍面临关键难题,如多源遥感图像特征表达不一致,融合困难,基于静态推理范式的神经网络缺乏对不同类别地物的适应性。为解决上述问题,提出了基于跨模态Transformer和多尺度动态3D卷积的多源遥感图像分类模型。为提高多源特征表达的一致性,设计了基于Transformer的融合模块,借助其强大的注意力建模能力挖掘高光谱和LiDAR数据特征之间的相互作用;为提高特征提取方法对不同地物类别的适应性,设计了多尺度动态3D卷积模块,将输入特征的多尺度信息融入卷积核的调制,提高卷积操作对不同地物的适应性。采用多源遥感数据集Houston和Trento对所提方法进行验证,实验结果表明:所提方法在Houston和Trento数据集上总体准确率分别达到94.60%和98.21%,相比MGA-MFN等主流方法,总体准确率分别至少提升0.97%和0.25%,验证了所提方法可有效提升多源遥感图像分类的准确率。