Monitoring of Karst Rocky Desertification Control Projects Based on Remote Sensing Images with Medium and High Spatial Resolution——A Case Study of Disi River Basin in Puan County

[ Objective] The study aimed to improve methods of monitoring Karst Rocky Desertification （KRD） control projects and increase the working efficiency. [Method] Based on remote sensing images with medium and high spatial resolution, KRD control projects in Disi River basin in Puan County were monitored, that is, information of the project construction in the study area was extracted using supervised classification and hu- man-computer interactive interpretation, and the monitoring results were testified with the aid of GPS. [Result] It was feasible to monitor KRD con- trol projects in Disi River basin based on remote sensing images with medium and high resolution, and the monitoring accuracy was satisfactory, reaching above 80% or 90%, so the method is worthy of popularizing. [ Conclusion] Remote sensing images with medium and high resolution can be used to monitor other KRD control Droiects.

Integrating cross-sensor high spatial resolution satellite images to detect subtle forest vegetation change in the Purple Mountains,a national scenic spot in Nanjing,China

Accurate information on the location and magnitude of vegetation change in scenic areas can guide the configuration of tourism facilities and the formulation of vegetation protection measures.High spatial resolution remote sensing images can be used to detect subtle vegetation changes.The major objective of this study was to map and quantify forest vegetation changes in a national scenic location,the Purple Mountains of Nanjing,China,using multi-temporal cross-sensor high spatial resolution satellite images to identify the main drivers of the vegetation changes and provide a reference for sustainable management.We used Quickbird images acquired in 2004,IKONOS images acquired in 2009,and WorldView2 images acquired in 2015.Four pixel-based direct change detection methods including the normalized difference vegetation index difference method,multi-index integrated change analysis(MIICA),principal component analysis,and spectral gradient difference analysis were compared in terms of their change detection performances.Subsequently,the best pixel-based detection method in conjunction with object-oriented image analysis was used to extract subtle forest vegetation changes.An accuracy assessment using the stratified random sampling points was conducted to evaluate the performance of the change detection results.The results showed that the MIICA method was the best pixel-based change detection method.And the object-oriented MIICA with an overall accuracy of 0.907 and a kappa coefficient of 0.846 was superior to the pixel-based MIICA.From 2004 to 2009,areas of vegetation gain mainly occurred around the periphery of the study area,while areas of vegetation loss were observed in the interior and along the boundary of the study area due to construction activities,which contributed to 79%of the total area of vegetation loss.During 2009–2015,the greening initiatives around the construction areas increased the forest vegetation coverage,accounting for 84%of the total area of vegetation gain.In spite of this,vegetation loss occurred in the interior of the Purple Mountains due to infrastructure development that caused conversion from vegetation to impervious areas.We recommend that:(1)a local multi-agency team inspect and assess law enforcement regarding natural resource utilization;and(2)strengthen environmental awareness education.

Feasibility of High Spatial Resolution Working Modes for Clinical PET Scanner

Contemporary PET scanners for clinical use have spatial-resolution of 4 - 5 mm, caused by fundamental factors in medical imaging: detector sizes, free path of positrons, and non-colinearity uncertainty of annihilation photon-pairs. The drawback in resolution significantly restrained the sensitivity of PET in imaging small lesions, which could be either early-stage cancers or small metastasis. In this study, the principle for a novel scanning mode to acquire high spatial-resolution images is proposed for clinical PET scanners. The concept of equivalent position was first proposed as different angular orientations of the scanner ring, at which comparable images could be achieved. Due to this concept, a typical static PET scan can be separated into m (m ≥ 2) equivalent sub-scans at different equivalent positions, when the scanner ring is systematically adjusted to m equivalent-positions of equal distance within one detector size. In this case each detector is virtually divided into m equal sub-detectors, without physical minimizing the detector size, and imaging contributions from every 1/m part of the detector can be determined by an analytically matrix, since there are m variables and m sub-scans. This novel concept is quite feasible to contemporary design because the high spatial resolution working modes (m ≥ 2) only demand the scanner to be slightly adjustable to other angular orientations. Adding high spatial resolutions modes to the scanner only has trifling influence on contrast resolutions as all imaging events at each sub-scan are independent. The time for performing a high-resolution scan could be comparable to a typical PET scan, as long as the Poisson noises are insignificant to low-uptake voxels. As a result, for a typical scanner design e.g. 80 cm in diameter with 18F as tracers, the spatial resolution of double sub-scans (m = 2) is 2.56 mm, and 2.19 mm for triple sub-scans (m = 3), which are significant improvements. The novelty of high spatial resolution design is compatible to digital PET or any other technological evolutions.

Luojia-HSSR:A high spatial-spectral resolution remote sensing dataset for land-cover classification with a new 3D-HRNet

High Spatial and Spectral Resolution(HSSR)remote-sensing images can provide rich spectral bands and detailed ground information,but there is a relative lack of research on this new type of remote-sensing data.Although there are already some HSSR datasets for deep learning model training and testing,the data volume of these datasets is small,resulting in low classification accuracy and weak generalization ability of the trained models.In this paper,an HSSR dataset Luojia-HSSR is constructed based on aerial hyperspectral imagery of southern Shenyang City of Liaoning Province in China.To our knowledge,it is the largest HSSR dataset to date,with 6438 pairs of 256×256 sized samples(including 3480 pairs in the training set,2209 pairs in the test set,and 749 pairs in the validation set),covering area of 161 km2 with spatial resolution 0.75 m,249 Visible and Near-Infrared(VNIR)spectral bands,and corresponding to 23 classes of field-validated ground coverage.It is an ideal experimental data for spatial-spectral feature extraction.Furthermore,a new deep learning model 3D-HRNet for interpreting HSSR images is proposed.The conv-neck in HRNet is modified to better mine the spatial information of the images.Then,a 3D convolution module with attention mechanism is designed to capture the global-local fine spectral information simultaneously.Subsequently,the 3D convolution is inserted into the HRNet to optimize the performance.The experiments show that the 3D-HRNet model has good interpreting ability for the Luojia-HSSR dataset with the Frequency Weighted Intersection over Union(FWIoU)reaching 80.54%,indicating that the Luojia-HSSR dataset constructed in this paper and the proposed 3D-HRnet model have good applicable prospects for processing HSSR remote sensing images.

基于深度特征提取残差网络的高光谱图像分类

深度学习由于其模块化设计和强大的特征提取能力,已成为高光谱图像分类的重要手段之一。然而,如何有效地提取更深层次的特征以及同时提高分析空间和光谱联合特征的能力仍是亟待解决的问题。针对这些问题,本文提出了一种深度特征提取的残差网络,该网络由两个关键部分组成:多级传递融合残差网络和空间-光谱多分辨率融合注意力残差网络。多级传递融合残差网络可以有效促进特征信息之间的相互作用,获得更深层次的特征。接着利用空间-光谱多分辨率融合注意力残差网络可以确保从高光谱数据中全面提取空间-光谱联合特征和多分辨率特征。为了验证其有效性,本文在Indian Pines,Pavia University和Salinas Valley三个高光谱数据集上对所提出方法的性能进行了评估,分类精度分别达到了98.10%,99.81%和99.94%。实验结果表明,与其他方法相比,该网络具有更好的泛化能力和分类性能。

基于高分辨率遥感影像的土耳其地震建筑物震害特征

选取土耳其卡赫拉曼马拉什省努尔达吉镇城区和周边乡村地区作为研究区,通过获取地震前后的高空间分辨率遥感影像数据,提取房屋建筑震害信息,识别房屋建筑震害等级,利用震害指数计算区域内地震烈度等级,综合分析得到建筑物震害特征与主要成因,对地震灾情快速评估、应急救援和恢复重建具有重要参考。

基于递归门控卷积的遥感图像超分辨率研究

由于受到硬件条件的限制,通常难以获得具有高分辨率(HR)的遥感图像。利用单幅图像超分辨率(SISR)技术对低分辨率(LR)遥感图像进行超分辨率重建是获取高分辨率遥感图像的常用方法。近年来,在图像超分辨率领域引入的卷积神经网络(CNN)改进了图像重建性能。然而,现有的基于CNN的超分辨率模型通常使用低阶注意力机制提取深层特征,其表征能力有待提高,且常规卷积的感受野有限,缺乏对远距离依赖关系的学习。为了解决以上问题,提出了一种基于递归门控卷积的遥感图像超分辨率方法RGCSR。该方法引入递归门控卷积g n Conv学习全局依赖和局部细节,通过高阶空间交互来获取高阶特征。首先,使用由高阶交互子模块(HorBlock)和前馈神经网络(FFN)组成的高阶交互——前馈神经网络模块(HFB)提取高阶特征。其次,利用由通道注意力(CA)和g n Conv构建的特征优化模块(FOB)优化各个中间模块的输出特征。最后,在多个数据集上的对比结果表明,RGCSR比现有的基于CNN的超分辨率方法具备更好的重建性能和视觉效果。

Combination of super-resolution reconstruction and SGA-Net for marsh vegetation mapping using multi-resolution multispectral and hyperspectral images

Vegetation is crucial for wetland ecosystems.Human activities and climate changes are increasingly threatening wetland ecosystems.Combining satellite images and deep learning for classifying marsh vegetation communities has faced great challenges because of its coarse spatial resolution and limited spectral bands.This study aimed to propose a method to classify marsh vegetation using multi-resolution multispectral and hyperspectral images,combining super-resolution techniques and a novel self-constructing graph attention neural network(SGA-Net)algorithm.The SGA-Net algorithm includes a decoding layer(SCE-Net)to preciselyfine marsh vegetation classification in Honghe National Nature Reserve,Northeast China.The results indicated that the hyperspectral reconstruction images based on the super-resolution convolutional neural network(SRCNN)obtained higher accuracy with a peak signal-to-noise ratio(PSNR)of 28.87 and structural similarity(SSIM)of 0.76 in spatial quality and root mean squared error(RMSE)of 0.11 and R^(2) of 0.63 in spectral quality.The improvement of classification accuracy(MIoU)by enhanced super-resolution generative adversarial network(ESRGAN)(6.19%)was greater than that of SRCNN(4.33%)and super-resolution generative adversarial network(SRGAN)(3.64%).In most classification schemes,the SGA-Net outperformed DeepLabV3+and SegFormer algorithms for marsh vegetation and achieved the highest F1-score(78.47%).This study demonstrated that collaborative use of super-resolution reconstruction and deep learning is an effective approach for marsh vegetation mapping.

高分辨率空间遥感影像在森林植被变化监测中的应用现状研究

本研究旨在探索高分辨率空间遥感影像在森林植被变化监测方面的应用现状。本文深入研究了高分辨率空间遥感影像在森林植被变化监测方面的潜力,以及其在环境监测、生态保护和自然资源管理等领域的实际应用,还讨论了高分辨率空间遥感影像技术所面临的一些技术挑战和限制。本文通过综合考虑这些因素,得出结论,高分辨率空间遥感影像在森林植被变化监测方面具有巨大潜力,并为进一步研究提供了重要的理论基础。

高光谱高空间分辨率遥感观测、处理与应用

高光谱遥感技术是遥感领域的研究热点之一。然而,由于成像口径与能量等限制因素,难以同时获得高光谱和高空间分辨率的图像,这极大限制了高光谱遥感在精细尺度任务中的应用。近年来,随着高光谱成像技术及无人机为代表的新型观测平台的发展,高光谱高空间(双高,同时具备纳米级光谱分辨率与亚米级空间分辨率)遥感技术发展迅猛,推动了高光谱遥感技术的应用,但同时也带来了更多问题。极高的空间与光谱分辨率使得数据更加海量高维,加剧了高光谱数据的空间异质性和光谱变异性,为影像智能信息处理带来更大的挑战。为此,本文将从双高遥感影像基准数据集、双高遥感影像智能信息处理、双高遥感影像典型应用3个方面论述双高遥感应用与发展现状。

基于空间光调制器构建二维任意形状的87Rb原子阵列

超冷原子系统是一个纯净的、高度可控的量子体系,可对凝聚态物理、高能物理、天体物理和化学反应等领域的重要物理问题进行量子模拟.构造不同构型的光晶格是模拟多样化的复杂量子系统的一个重要前提.本文采用权重Gerchberg-Saxton算法生成多种形状的光晶格全息图,利用液晶型空间光调制器和高分辨率光学系统,把全息图(动量空间)变换到实空间构造出多种形状的二维晶格阵列,包括简单的三角、六角、正方晶格和更为复杂的蜂巢晶格等,并实现对87Rb超冷原子二维晶格阵列的装载,晶格的最小间距为3μm.这种方法具有通用性强、操控灵活的优势,将有助于拓展光晶格中超冷原子量子模拟的应用.

植被指数在北京城市园林绿化覆盖率提取中的应用

为快速准确地获取城市园林绿化覆盖率,以北京市西城区的陶然亭街道为对象,基于高分辨率遥感影像,探讨了多种植被指数在城市园林绿化覆盖率提取中的可行性。结果表明:随着遥感影像空间分辨率的提高,有助于减少可见光植被指数提取结果中的地物误分,但基于0.5 m与0.8 m空间分辨率遥感影像的可见光植被指数所提取的绿化覆盖率均与实际值相差较大,因此不建议将其应用于园林绿化覆盖率提取研究中。而基于0.8 m空间分辨率遥感影像的归一化植被指数的绿化覆盖率提取比较适用于内部建筑高度较低的区域,可作为园林绿化资源普查工作的重要参考。在此基础上,还应用归一化植被指数,获取到北京2019年北京城市核心区(东城区和西城区)的绿化覆盖率为23.82%。

基于不规则尺度区域光谱信息的高光谱图像亚像元定位

亚像元定位技术可以分析混合像元,并实现从丰度图像到亚像元级精细土地覆盖定位图像的转换。然而,传统的亚像元定位方法所使用的光谱信息通常在指定的矩形局部窗口中构造,并且很少使用所有波段的光谱信息,影响了亚像元定位的性能。为了解决这一问题,本文提出了一种基于不规则尺度区域光谱信息的高光谱图像亚像元定位方法(SIISA)。在三幅遥感图像上的实验结果表明,所提出的SIISA优于现有的亚像元定位方法。

基于自适应上下文聚合网络的双高遥感影像分类

融合高光谱和高空间分辨率(双高)遥感的优势可以实现地物目标更为全面和精细的属性识别。然而,空间分辨率的显著提升使得双高影像中地物细节特征凸显出来,呈现出极高的空谱异质性,进而导致同物异谱现象大量发生,地物类内方差明显增大。基于此,本文提出一种局部-全局上下文信息自适应聚合的快速双高影像分类框架(adaptive context aggregation network,ACANet),通过编码-解码的全卷积网络架构顾及全局空谱信息,在编码器中构建局部到全局的长距离上下文感知模块缓解双高影像极大的类内方差,在解码器中构建自适应上下文聚合模块进一步实现局部和全局的上下文信息自适应聚合。本文方法在WHU-Hi双高影像分类基准数据集中取得了优异的分类性能,试验表明可以很好缓解双高影像极高空谱异质性对地物精细分类的影响。

基于深度学习的高分遥感影像尾矿库提取

尾矿库检测和边界提取对于尾矿库的数字化管理和监测非常重要。利用改进U-Net模型进一步对目标区域准确地提取尾矿库,框架的整体精度达到98.12%。该方法能够从大面积高空间分辨率遥感影像中高精度、高速度地提取各种尾矿库,有效减少了尾矿库数字化管理的人力和财力成本,为政府部门快速获取尾矿库边界信息提供一定的参考。

优于2m多时相影像的松材线虫病受害木识别

针对现有松材线虫病受害木遥感监测研究中高分辨率影像研究应用不足的问题,提出适用于优于2m分辨率卫星影像的图像分割策略、特征分类规则及信息提取方法,对比分析了多时相与单一时相影像的识别精度,并以林业小班为单位对研究区域病虫害感染程度进行了定量研究。研究表明,优于1m卫星影像采用基于规则的面向对象方法,可较好定位识别病死单木,正确率达40%~50%;就2m卫星影像而言,基于面向像元的信息提取方法相较于面向对象方法更为合适;相较单时相影像而言,多时相影像可剔除因季节变化的阔叶林、低矮植被等地物干扰,识别精度相对较高。本文为高分辨率卫星影像应用于林业病虫害识别提供有力的支撑。

Optimization of post-classification processing of high-resolution satellite image:A case study

The application of remote sensing monitoring techniques plays a crucial role in evaluating and governing the vast amount of ecological construction projects in China. However, extracting information of ecological engineering target through high-resolution satellite image is arduous due to the unique topography and complicated spatial pattern on the Loess Plateau of China. As a result, enhancing classification accuracy is a huge challenge to high-resolution image processing techniques. Image processing techniques have a definitive effect on image properties and the selection of different parameters may change the final classification accuracy during post-classification processing. The common method of eliminating noise and smoothing image is majority filtering. However, the filter function may modify the original classified image and the final accuracy. The aim of this study is to develop an efficient and accurate post-processing technique for acquiring information of soil and water conservation engineering, on the Loess Plateau of China, using SPOT image with 2.5 rn resolution. We argue that it is vital to optimize satellite image filtering parameters for special areas and purposes, which focus on monitoring ecological construction projects. We want to know how image filtering influences final classified results and which filtering kernel is optimum. The study design used a series of window sizes to filter the original classified image, and then assess the accuracy of each output map and image quality. We measured the relationship between filtering window size and classification accuracy, and optimized the post-processing techniques of SPOT5satellite images. We conclude that (1) smoothing with the majority filter is sensitive to the information accuracy of soil and water conservation engineering, and (2) for SPOT5 2.5 m image, the 5×5 pixel majority filter is most suitable kernel for extracting information of ecological construction sites in the Loess Plateau of China.

Tb^(3+)-doped borosilicate glass scintillators for highresolution X-ray imaging

Scintillators are the vital component in X-ray perspective image technology that is applied in medical imaging,industrial nondestructive testing,and safety testing.But the high cost and small size of single-crystal commercialized scintillators limit their practical application.Here,a series of Tb^(3+)-doped borosilicate glass(BSG)scintillators with big production size,low cost,and high spatial resolution are designed and fabricated.The structural,photoluminescent,and scintillant properties are systematically investigated.Benefiting from excellent transmittance(87%at 600 nm),high interquantum efficiency(60.7%),and high X-ray excited luminescence(217%of Bi4Ge3O12),the optimal sample shows superhigh spatial resolution(exceeding 20 lp/mm).This research suggests that Tb^(3+)-doped BSG scintillators have potential applications in the static X-ray imaging field.

GF-1和Landsat 8 OLI遥感影像在草地资源调查中的应用——以甘南州天然草地为例

以甘南州天然草地为研究对象,利用高空间分辨率遥感影像(GF-1)数据,提取了甘南州天然草地空间分布数据;结合地形数据和行政区划单元数据分析了不同类型天然草地的空间分布特征;利用Landsat 8 OLI数据和草地盖度、草地地上生物量实测数据构建了甘南州草地盖度、草地地上生物量遥感反演模型,并分析了2019年甘南州草地的生长状况。结果表明:①全州天然草地面积为1995740.04 hm^(2),草地地上生物量为2250.28 kg/hm^(2),草地盖度为93.11%。②高寒草甸和山地草甸分别占63.69%和28.31%,二者分别集中分布于海拔3500~5000 m,和3000~3500 m。研究结果为甘南州草地调查、日常监管提供了数据与理论支持。