Remote sensing image encryption algorithm based on novel hyperchaos and an elliptic curve cryptosystem

Remote sensing images carry crucial ground information,often involving the spatial distribution and spatiotemporal changes of surface elements.To safeguard this sensitive data,image encryption technology is essential.In this paper,a novel Fibonacci sine exponential map is designed,the hyperchaotic performance of which is particularly suitable for image encryption algorithms.An encryption algorithm tailored for handling the multi-band attributes of remote sensing images is proposed.The algorithm combines a three-dimensional synchronized scrambled diffusion operation with chaos to efficiently encrypt multiple images.Moreover,the keys are processed using an elliptic curve cryptosystem,eliminating the need for an additional channel to transmit the keys,thus enhancing security.Experimental results and algorithm analysis demonstrate that the algorithm offers strong security and high efficiency,making it suitable for remote sensing image encryption tasks.

Reduced Imaging Time and Improved Image Quality of 3D Isotropic T2-Weighted Magnetic Resonance Imaging with Compressed Sensing for the Female Pelvis

This study is to compare three-dimensional(3D)isotropic T2-weighted magnetic resonance imaging(MRI)with compressed sensing-sampling perfection with application optimized contrast(CS-SPACE)and the conventional image(3D-SPACE)sequence in terms of image quality,estimated signal-to-noise ratio(SNR),relative contrast-to-noise ratio(CNR),and the lesions’conspicuous of the female pelvis.Thirty-six females(age:51,28-73)with cervical carcinoma(n=20),rectal carcinoma(n=7),or uterine fibroid(n=9)were included.Patients underwent magnetic resonance(MR)imaging at a 3T scanner with the sequences of 3D-SPACE,CS-SPACE,and twodimensional(2D)T2-weighted turbo-spin echo(TSE).Quantitative analyses of estimated SNR and relative CNR between tumors and other tissues,image quality,and tissue conspicuity were performed.Two radiologists assessed the difference in diagnostic findings for carcinoma.Quantitative values and qualitative scores were analyzed,respectively.The estimated SNR and the relative CNR of tumor-to-muscle obturator internus,tumor-to-myometrium,and myometrium-to-muscle obturator internus was comparable between 3D-SPACE and CS-SPACE.The overall image quality and the conspicuity of the lesion scores of the CS-SPACE were higher than that of the 3D-SPACE(P<0.01).The CS-SPACE sequence offers shorter scan time,fewer artifacts,and comparable SNR and CNR to conventional 3D-SPACE,and has the potential to improve the performance of T2-weighted images.

The First Global Map of Atmospheric Ammonia(NH_(3)) as Observed by the HIRAS/FY-3D Satellite

Atmospheric ammonia(NH_(3)) is a chemically active trace gas that plays an important role in the atmospheric environment and climate change. Satellite remote sensing is a powerful technique to monitor NH_(3) concentration based on the absorption lines of NH_(3) in the thermal infrared region. In this study, we establish a retrieval algorithm to derive the NH_(3)column from the Hyperspectral Infrared Atmospheric Sounder(HIRAS) onboard the Chinese Feng Yun(FY)-3D satellite and present the first atmospheric NH_(3) column global map observed by the HIRAS instrument. The HIRAS observations can well capture NH_(3) hotspots around the world, e.g., India, West Africa, and East China, where large NH_(3) emissions exist. The HIRAS NH_(3) columns are also compared to the space-based Infrared Atmospheric Sounding Interferometer(IASI)measurements, and we find that the two instruments observe a consistent NH_(3) global distribution, with correlation coefficient(R) values of 0.28–0.73. Finally, some remaining issues about the HIRAS NH_(3) retrieval are discussed.

Correg-Yolov3:a Method for Dense Buildings Detection in High-resolution Remote Sensing Images

The exploration of building detection plays an important role in urban planning,smart city and military.Aiming at the problem of high overlapping ratio of detection frames for dense building detection in high resolution remote sensing images,we present an effective YOLOv3 framework,corner regression-based YOLOv3(Correg-YOLOv3),to localize dense building accurately.This improved YOLOv3 algorithm establishes a vertex regression mechanism and an additional loss item about building vertex offsets relative to the center point of bounding box.By extending output dimensions,the trained model is able to output the rectangular bounding boxes and the building vertices meanwhile.Finally,we evaluate the performance of the Correg-YOLOv3 on our self-produced data set and provide a comparative analysis qualitatively and quantitatively.The experimental results achieve high performance in precision(96.45%),recall rate(95.75%),F1 score(96.10%)and average precision(98.05%),which were 2.73%,5.4%,4.1%and 4.73%higher than that of YOLOv3.Therefore,our proposed algorithm effectively tackles the problem of dense building detection in high resolution images.

Airborne sparse flight array SAR 3D imaging based on compressed sensing in frequency domain

In airborne array synthetic aperture radar(SAR), the three-dimensional(3D) imaging performance and cross-track resolution depends on the length of the equivalent array. In this paper, Barker sequence criterion is used for sparse flight sampling of airborne array SAR, in order to obtain high cross-track resolution in as few times of flights as possible. Under each flight, the imaging algorithm of back projection(BP) and the data extraction method based on modified uniformly redundant arrays(MURAs) are utilized to obtain complex 3D image pairs. To solve the side-lobe noise in images, the interferometry between each image pair is implemented, and compressed sensing(CS) reconstruction is adopted in the frequency domain. Furthermore, to restore the geometrical relationship between each flight, the phase information corresponding to negative MURA is compensated on each single-pass image reconstructed by CS. Finally,by coherent accumulation of each complex image, the high resolution in cross-track direction is obtained. Simulations and experiments in X-band verify the availability.

3D visualization of the material flow in friction stir welding process

The material flow in friction stir welded 2014 Al alloy has been investigated using a marker insert technique （MIT）. Results of the flow visualization show that the material flow is asymmetrical during the friction stir welding （FSW） process and there are also significant differences in the flow patterns observed on advancing side and retreating side. On advancing side, some material transport forward and some move backward, but on retreating side, material only transport backward. At the top surface of the weld, significant material transport forward due to the action of the rotating tool shoulder. Combining the data from all the markers, a three-dituensional flow visualization, similar to the 3D image reconstruction technique, was obtained. The three-dimensional plot gives the tendency chart of material flow in friction stir welding process and from the plot it can be seen that there is a vertical, circular motion around the longitudinal axis of the weld. On the advancing side of the weld, the material is pushed downward but on the retreating side, the material is pushed toward the crown of the weld. The net result of the two relative motions in both side of the advancing and the retreating is that a circular motion comes into being. Comparatively, the material flow around the longitudinal axis is a secondary motion.

Bayesian and Geostatistical Approaches to Combining Categorical Data Derived from Visual and Digital Processing of Remotely Sensed Images

This paper seeks a synthesis of Bayesian and geostatistical approaches to combining categorical data in the context of remote sensing classification. By experiment with aerial photographs and Landsat TM data, accuracy of spectral, spatial, and combined classification results was evaluated. It was confirmed that the incorporation of spatial information in spectral classification increases accuracy significantly. Secondly, through test with a 5-class and a 3-class classification schemes, it was revealed that setting a proper semantic framework for classification is fundamental to any endeavors of categorical mapping and the most important factor affecting accuracy. Lastly, this paper promotes non-parametric methods for both definition of class membership profiling based on band-specific histograms of image intensities and derivation of spatial probability via indicator kriging, a non-parametric geostatistical technique.

3-D Visualization of Medical Images with Arbitrary Sections

In this paper, with the general retrospect to the research on surface reconstruction and the marching cubes algorithm, we gave detailed description of an algorithm on the construction of object surfaces. The possible ambiguity problem in the original marching cubes algorithm was eliminated by its index mechanism. Some results on the MRI images were presented. Based on extracting and clipping contours from a set of medial slice images and setting the patch vertices values according to the gray images, this algorithm may be applied to form the arbitrary section images with three dimensional effects. It can also enhance the visual effect and interpretation of medical data.

Remote Sensing and GIS Application to Superlarge Mineral Deposits Prediction in Western Slope of Great Xing'an Mountains, China

Supported by Geographic Information Systems (GIS), the multi-source geoscience information in the Western Slope of Great Xing’an Mountains are visual analyzed, including remote sensing data, magnetic data, gravity data and gamma-ray spectrometry data. Thus the structural framework is built up. And the remote sensing image pattern of mineral deposits is established by comparing the remote sensing geological features of five large or superlarge mineral deposits located in the same metallogenic belt in China, Russia and Mongolia. Guided by the image pattern, seven prospective locations of large or superlarge mineral resource are delineated.

Visualization of Three-dimensional Human Data Based on CT Image

Three-dimensional medical image visualization becomes an essential part for medical field, including computer aided diagnosis, surgery planning and simulation, artificial limb surgery, radiotherapy planning, and teaching etc. In this paper, marching cubes algorithm is adopted to reconstruct the 3-D images for the CT image sequence in DICOM format under theVC＋＋6.0 and the visual package VTK platform. The relatively simple interactive operations such as rotation and transfer can be realized on the platform. Moreover, the normal vector and interior point are calculated to form the virtual clipping plane, which is then used to incise the 3-D object. Information of the virtual slice can be obtained, in the mean while the virtual slice images are displayed on the screen. The technique can realize the real time interaction extraction of virtual slice on 3-D CT image. The cuboids structured can be zoomed, moved and eircumrotated by operating mouse to incise the 3-D reconstruction object. Real time interaction can be realized by clipping the reconstruction object. The coordinates can be acquired by the mouse clicking in the 3D space, to realize the point mouse pick-up as well angle and distance interactive measurement. We can get quantitative information about 3-D images through measurement.

Floodplain Mapping and Risks Assessment of the Orashi River Using Re­mote Sensing and GIS in the Niger Delta Region, Nigeria

Residents along the shoreline of the Orashi River have yearly been dis­placed and recorded loss of lives,farmland,and infrastructures.The Gov­ernment’s approach has been the provision of relief materials to the victims instead of implementing adequate control measures.This research employs Shuttle Radar Topographic Mission and Google Earth imagery in devel­oping a 3D floodplain map using ArcGIS software.The result indicates the drainage system in the study area is dendritic with catchment of 79 subbasins and 76 pour point implying the area is floodplain.Incorporating the 3D slope which reveals that>8 and<8 makes up 1.15%and 98.85%of the study area respectively confirms the area is a floodplain.Aspect indicate west-facing slope are dark blue,3D hillshade indicate yellow is very low area and the high area is pink and also the buffer analysis result reveals wa­terbodies reflecting blue with an estimated area of 1.88 km2,yellow indicate 0.79 km2 of the shoreline,red indicate 0.81 km2 of the minor floodplain and pink contain 0.82 km2 with the length of 32.82 km.The result from google earth image in 2007 indicate absent of settlement,2013 indicate minimal settlement and 2020 indicate major settlement in the study area when cor­related with 3D Floodplain mapping before and during the flood in other to analyze and manage flooding for further purpose and the majority of the area are under seize with flood like in 2020.Therefore,Remote Sensing and GIS techniques are useful for Floodplain mapping,risk analysis for control measures for better flood management.

基于Visualization Toolkit的脑模型三维重建方法研究

目的利用可视化工具包VisualizationToolkit(VTK)结合VC++实现医学图像三维可视化。方法基于头部CT测量数据,采用MarchingCubes算法和Raycasting算法重建出头模型的表皮和颅骨。结果和结论VTK使用灵活,功能强大,利用它进行图像重建,具有重建步骤简单、效果好、速度快、交互能力强等优点,可以被广泛应用于医学图像的重建中。

基于RSSD的遥感图像目标检测算法

针对SSD算法检测遥感图像目标时存在容易漏检且检测精度低的问题,提出基于残差SSD网络的遥感图像目标检测算法。该算法在SSD网络结构的基础上,将基准网络模型VGG替换为残差网络模型ResNet-50,通过增加网络深度,充分提取遥感图像小目标数据集的底层特征,引入注意力模块,使感受野更关注目标特征,增强低层网络的信息表征能力,采用特征金字塔融合方法融合网络结构的高层语义特征和低层视觉特征,增强检测目标的定位能力。实验结果表明,该算法增强了复杂背景的干扰抑制性,提高了小目标的检测精度,比传统的SSD算法具有更强的检测性能。

基于YOLOv7-RS的遥感图像目标检测研究

针对遥感图像目标检测过程中存在的背景复杂、目标特征不明显、小目标排列密集的问题,基于YOLOv7算法,提出了一种改进的遥感图像目标检测算法YOLOv7-RS(YOLOv7-Remote Sensing),提高了遥感图像的目标检测精度。首先,向特征提取网络中融合SimAM减少背景噪声的干扰;其次,提出了D-ELAN网络增强遥感目标的特征提取能力;再次,利用SIOU损失函数以提高算法模型的收敛速度;最后,优化了正负样本分配策略,改善了遥感图像中小目标密集排列时的漏检问题。实验结果表明,YOLOv7-RS在NWPU VHR-10和DOTA数据集上的mAP达到95.4%和74.1%,相较于其他主流算法有了明显提升。

基于GIS服务的遥感影像智能识别系统研究与应用

为解决水利行业遥感影像智能识别系统样本制作效率低、影像检测成果使用和管理不便、用户体验差、缺乏可靠水安全地形要素AI智能提取系统的问题,开展基于GIS服务的遥感影像智能识别系统研究。基于WebGIS、数据库、大数据、互联网、人工智能等技术,研究构建遥感影像大数据管理与发布、样本数据集自动化生产与管理系统。在U-Net模型的基础上引入影像预处理和栅格转矢量模块,添加2个残差块以加深网络,从而得到D-Unet模型,并用于地形要素提取。结果表明:基于GIS服务的遥感影像智能识别系统能够实现对多源卫星、无人机遥感影像的高效管理,以及样本数据集生产的自动化,在地形要素识别方面具有较高精度,且具有较好的用户体验。

基于3DS MAX的会泽铅锌矿区遥感影像的三维可视化

随着地球科学及计算机图形图像学的不断发展,三维地质建模及可视化已引起地学界的高度重视。自然地质现象极为复杂,二维地质数据在认识三维状态下的地质体有很大的局限性。利用计算机图形学及可视化技术,将二维抽象的地学信息以三维可视化的图形方式直观形象地显示出来,创建逼真的三维空间地质体模型具有重要意义。本文阐述了以会泽铅锌矿区遥感影像为基础,利用3DS MAX中各种建模、材质贴图等一系列工具对会泽铅锌矿区地表信息的可视化模拟。

基于旋转框表示的光学遥感图像目标检测

旋转目标检测是遥感图像智能解译的关键步骤,然而遥感图像背景复杂、目标方向具有任意性、尺度差异大,实现准确的旋转目标检测有一定的困难。提出的ERDet结合显示视觉中心,提取遥感图像的全局信息与局部信息,结合自适应阈值样本选择的水平目标检测算法和长边定义法,预测遥感图像目标的类别、位置和旋转角度。在DOTA-v1.0数据集上的实验表明,该方法能够对不同尺度和方向的目标进行准确提取,实现了对遥感目标的精准检测。

基于视觉传达的模糊激光遥感图像重建方法

受到成像设备、传输环境等因素的影响,导致激光遥感图像存在明显的模糊现象,为恢复激光遥感图像质量提出基于视觉传达的模糊激光遥感图像重建方法。利用视觉传达技术获取模糊激光遥感图像,从纹理、统计分布、频率等方面提取图像特征,以此作为图像重构规律,通过图像配准、去模糊、噪声等步骤,得出模糊激光遥感图像重建结果,以可视化的形式输出。通过测试实验得出结论:与传统重建方法相比,优化方法得出重建图像峰值信噪比最高达到了38 dB,结构相似性最高达到了0.94,表明优化方法在重建质量方面具有明显优势。

基于改进YOLOv5x的遥感图像目标检测算法

针对遥感图像目标检测任务中小目标数量多、目标特征不明显导致检测精度不高的问题,提出了一种基于改进YOLOv5x的遥感图像目标检测算法。首先,主干网络设计了D-SPP模块,在不加深网络结构的前提下整合了信息,使不同感受野特征能够有效融合。其次,采用SIOU_Loss代替CIOU_Loss作为边界框损失函数,提高边界框定位准确度。最后,增加一个新的检测头以获得更大尺度的特征图进行目标检测,并用Transformer构建网络中最小的检测头。实验结果表明,本算法在RSOD数据集上检测平均精度均值达到了91%,比YOLOv5x算法提升了5.4%。

基于红外影像层次旋转匹配的飞行器定位方法

基于图像匹配的飞行器自主视觉定位技术是飞行器导航制导、态势感知和自主决策的关键技术之一。针对红外遥感影像在大角度旋转下飞行器匹配定位失效的问题,提出了一种基于层次结构强化的特征点旋转匹配定位方法。该方法通过融合深度特征点提取与层次结构强化的旋转匹配定位技术,有效实现了飞行器匹配定位。首先设计了一个集成残差连接编码器的RBN-SuperPoint深度特征点提取模型,用于检测和描述待匹配图像中的深度特征点。其次构建了基于线性注意力和置信度分类器的L-LightGlue自适应匹配算法,利用L-LightGlue进行特征点粗匹配,生成单应性变换矩阵。随后采用层次结构强化的旋转匹配策略,根据粗匹配得到的单应性变换矩阵对图像进行旋转处理,消除图像间的角度差异,并进行精确匹配。再通过将结果映射至原图像,得到旋转校正后的特征点匹配结果和对应的单应性变换矩阵。最后利用图像间变换关系确定飞行器在图像中的位置,完成视觉定位。实验结果表明:RBN-SuperPoint可以高效提取大量均匀分布的特征点,所提基于L-LightGlue的匹配定位算法的匹配准确率最高可达98.57%,平均定位误差仅为4.08 pixel。