Integrating of Remote Sensing and Airborne Magnetic Data to Outline the Geologic Structural Lineaments That Controlled Mineralization Deposits for the Area around Gabal El-Niteishat, Central Eastern Desert, Egypt

The Gabal (G.) El-Niteishat area lies in the Central Eastern Desert of Egypt which is known for various mineral resources and geological structures. Umm Gheig, Umm Naggat, Umm Shaddad, Wadi (W.) Zeidun and Sigdit represent some important regions that contain mineral deposits in the study area. Various filters such as first vertical derivative (FVD), horizontal gradient magnitude (HGM), tilt derivative (TDR) and near-surface were applied to the airborne magnetic data for the study area to deduce the structural lineaments and magnetic source edges which were controlled by the presence of mineral deposits. Processed Landsat ETM+ images are used for delineating the rock unit boundaries that are exposed in the study area such as serpentinite, metagabbro, metavolcanics and metasediments. Also, band ratios, principal component analysis (PCA) and false-color composite image (Crosta alteration image) were applied to get specific results about the alteration zones. The structural lineaments analysis illustrated that the common trends that affected the study area were NW-SE, NE-SW, E-W and N-S. Integration of remote sensing and airborne magnetic data exhibited the relation between mineralization and structural lineaments.

Pegmatite remote sensing extraction and metallogenic prediction in Azubai area,Xinjiang

Remote sensing technique plays an important role in geological prospecting in Altay because of the remote location and steep terrain with mountains. Pegmatite has important implications for metallogenic prospecting as most of rare metals occurs in it. Pegmatite information from optical and radar images was extracted, and the spatial distribution and scale of pegmatite were generalized in Azubai, Altay. Three mining targets, that is, Halon-Azubai, Kuermutu-Tuyibaguo and Zhuolute-Akuoyige, were delineated based on the analysis of pegmatite information, structure interpretation and other geological data.

Remote Sensing Study of Gold Biogeochemical Effects in the Western Guangdong-Hainan Region——A Case Study of the Hetai Gold Deposit

The vegetation has been poisoned by gold in the western Guangdong-Hainan region. The gold content ofthe leaves there is as high as 10-1961 times the abundance, the chlorophyll content is 10%-30% lower thanthat of the vegetation in metamorphic terrains and 10%-20% higher than that in granite terrains, and thecarotenoid content is 10%-44% lower than the background value. The water content of leaves is 10% to 20%lower than the background value. The cells of leaves are deformed and broken. The leaf surface shows colourspots and becomes yellow or dark green. The spectral reflectance of the leaf surface is 5%-30% higher than thebackground value: the spectral shape has shifted 5-15 nm to the short wavelength. The gray scales of eanopyon images of Landsat TM and airborne imaging scanner (AIS) are 10%-100% higher than the backgroundvalues. On Landsat TM and AIS false colour images, plants poisoned by gold display a yellow color, whichdisinguishes them from background plants. According to the spectral and image features of goldbiogeochemical effects, the author has constructed a gold information system and expert prediction system,and thus two gold target areas and two gold prospect areas have been identified rapidly, economically andaccurately in the western Guangdong-Hainan region which is extensively covered by vegetation.

Remote Sensing Estimation of Crop Lead Pollution Stress Degree Using Wavelet Analysis

Accurate estimation of soil lead pollution degree is one of the key steps in controlling soil lead pollution; vegetable hyperspectral features research provided a new approach to discovering and monitoring soil heavy metal pollution.Spectral reflectance implies information of pollution impacts on vegetation;estimation of lead pollution degree based on the spectral reflectance is equivalent to extraction of weak information.This study puts forward a new feature extraction method based

Research on Extraction Method of Surface Information Based on Multi-Feature Combination Such as Fractal Texture

Because of the developed economy and lush vegetation in southern China, the following obstacles or difficulties exist in remote sensing land surface classification: 1) Diverse surface composition types;2) Undulating terrains;3) Small fragmented land;4) Indistinguishable shadows of surface objects. It is our top priority to clarify how to use the concept of big data (Data mining technology) and various new technologies and methods to make complex surface remote sensing information extraction technology develop in the direction of automation, refinement and intelligence. In order to achieve the above research objectives, the paper takes the Gaofen-2 satellite data produced in China as the data source, and takes the complex surface remote sensing information extraction technology as the research object, and intelligently analyzes the remote sensing information of complex surface on the basis of completing the data collection and preprocessing. The specific extraction methods are as follows: 1) extraction research on fractal texture features of Brownian motion;2) extraction research on color features;3) extraction research on vegetation index;4) research on vectors and corresponding classification. In this paper, fractal texture features, color features, vegetation features and spectral features of remote sensing images are combined to form a combination feature vector, which improves the dimension of features, and the feature vector improves the difference of remote sensing features, and it is more conducive to the classification of remote sensing features, and thus it improves the classification accuracy of remote sensing images. It is suitable for remote sensing information extraction of complex surface in southern China. This method can be extended to complex surface area in the future.

Some essential questions in remote sensing science and technology

In this paper,I propose a personal view on the general contents of remote sensing science and technology,which includes sensor research and manufacturing,remotely sensed data acquisition,data processing,information extraction and remote sensing applications.Serving as the basis for all these components is radiative transfer process modeling and inversion.Also of importance is the effective visualization of remotely sensed data and their efficient distribution to end users.In all these areas,there are critical research questions.In particular,I consider 4 fundamental areas for improved application of remote sensing.These include the scale and angular issues in remote sensing,removal of topographic effects on the radiance and geometry of remotely sensed imagery and the related question of multisource and multitemporal data registration,integrating knowledge and remotely sensed data into effective information extraction,and four dimensional data assimilation techniques.Strategies of information extraction can be broadly divided into manual visual analysis and computer-based analysis.The computer based information analysis include radiative transfer model inversion,image classification,regression analysis,three dimensional information extraction,shape analysis and change detection.Successful information extraction is the key to the success of remote sensing.There are many important issues that need to be solved including how to make better use of the spatial and temporal data present in remotely sensed data in information extraction.How to effectively combine the strength of both computer analysis and human interpretation? Finally,4D data assimilation is the new direction that allows for the integration of instantaneous observation with process-based climate,hydrological and ecological models.Further work along this direction will enhance the contribution of remote sensing in global change studies.In return,the quality of remotely sensed parameters can be improved.

ANALYSIS OF SURFACE SINK OF EXPLORATION VACANCY IN GOLD MINING AREA OF ZHAOYUAN CTIY BASED ON RS AND GIS

Surface sink is a main geological calamity of gold mining areas and a main factor to restrict economic sustainable development of mining zone. Based on former investigations, this article draws the environment information of surface sink of exploration vacancy in gold mining area of Zhaoyuan City, Shangdong Province by RS technology. Through spatial simulation analysis and expert diagnoses on the basis of GIS technology, the article affirms the inducement factors of the surface sink. Then using these factors as distinguishing ones the authors prognosticate the criticality of other exploration vacancies. The results indicate that the surface sink area of study area in Zhaoyuan City, has already come to 0.78km2 and it is forecasted that 0.97km2 of the exploration vacancy belongs to high danger area. Decisive measures need taking in order to prevent this crucial problem. Another 1.57km2 of the exploration vacancies belongs to middle danger area, which will sink when meeting some inducing factors, such as earthquake. Still another 1.53km2 of the exploration vacancies belongs to low danger area that can not lead to surface sink when meeting common inducing factors.

Fast Remote-Sensing Image Registration Using Priori Information and Robust Feature Extraction

In this paper, we propose a fast registration scheme for remote-sensing images for use as a fundamental technique in large-scale online remote-sensing data processing tasks. First, we introduce priori-information images,and use machine learning techniques to identify robust remote-sensing image features from state-of-the-art ScaleInvariant Feature Transform（SIFT） features. Next, we apply a hierarchical coarse-to-fine feature matching and image registration scheme on the basis of additional priori information, including a robust feature location map and platform imaging parameters. Numerical simulation results show that the proposed scheme increases position repetitiveness by 34%, and can speed up the overall image registration procedure by a factor of 7：47 while maintaining the accuracy of the image registration performance.

Deep learning in extracting tropical cyclone intensity and wind radius information from satellite infrared images—A review

热带气旋(TC)严重危害人类生命和财产安全,TC的实时监测一直是研究热点,随着空间和传感器技术的发展,卫星遥感已成为监测TC的主要手段.此外,深度学习具有卓越的数据挖掘能力,在地球科学中的表现优于基于物理或统计的算法,越来越多的深度学习算法被开发和应用于TC信息的提取,本文系统地回顾了深度学习在TC信息提取中的应用,并给出了深度学习模型在TC强度和风圈半径提取中的应用.此外,本文还展望了深度学习在TC信息提取中的应用前景.

MSCANet: multiscale context information aggregation network for Tibetan Plateau lake extraction from remote sensing images

Qinghai-Tibet Plateau lakes are important carriers of water resources in the‘Asian’s Water Tower’,and it is of great significance to grasp the spatial distribution of plateau lakes for the climate,ecological environment,and regional water cycle.However,the differences in spatial-spectral characteristics of various types of plateau lakes,and the complex background information of plateau both influence the extraction effect of lakes.Therefore,it is a great challenge to completely and effectively extract plateau lakes.In this study,we proposed a multiscale contextual information aggregation network,termed MSCANet,to automatically extract Plateau lake regions.It consists of three main components:a multiscale lake feature encoder,a feature decoder,and a Multicore Pyramid Pooling Module(MPPM).The multiscale lake feature encoder suppressed noise interference to capture multiscale spatial-spectral information from heterogeneous scenes.The MPPM module aggregated the contextual information of various lakes globally.We applied the MSCANet to the lake extraction of the Qinghai-Tibet Plateau based on Google data;additionally,comparative experiments showed that the MSCANet proposed had obvious improvement in lake detection accuracy and morphological integrity.Finally,we transferred the pre-trained optimal model to the Landsat-8 and Sentinel-2A dataset to verify the generalization of the MSCANet.

Monitoring of Pinus massoniana spatial pattern changes based on RS and GIS techniques

Our research focused on Pinus massoniana information extracted from remote sensing images based on the knowledge detection and decision tree algorithm and established a spatial pattern model, combining quantitative theoretical ecology with remote sensing （RS） and geometric information system （GIS） techniques. Applying information extraction methods and a spatial pattern model, we studied P. massoniana spatial patterns changes before and after the invasion by pine wood nematode （Bursaphelenchus xylophilus） in Fuyang and Zhoushan counties, Zhejiang Province, east China. The P. massoniana spatial patterns are clustering, whether the invasion happened or not. But the degree of clustering is different. Our results show good agreement with field data. Applying the results, we analyzed the relationship between spatial patterns and the invasion level. Then we drew the elementary conclusion that there are two kinds of patterns for pine wood nematode to spread： continuous and discontinuous diffusion. This approach can help monitor and evaluate the changes in ecological systems.

遥感技术新进展赋能水利行业应用

随着遥感技术的不断发展和水利信息化进程的加快,水利遥感有了飞速的发展,与水利现代化发展同步。目前,遥感技术在水利行业的应用范围非常广,在为水利高质量发展提供有力支撑和强力驱动的数字孪生水利建设中发挥了重要的作用。近十多年来,快速发展的遥感技术为提升水利遥感应用能力提供了强有力的支撑,具体表现在数据源的丰富化和多源化、数据获取的速度和效率显著提升、定量估算与反演精度大大提高、专题信息可获得性不断增加、数据处理和信息提取的智能化水平取得长足进步。介绍了遥感技术发展赋能水利遥感应用领域的现状和前景展望,为遥感技术赋能水利新质生产力提供参考。

煤层气富集区遥感勘查研究进展与展望

煤层气是一种自生自储式非常规清洁能源,它赋存于煤层及其围岩中。传统勘查方法费时费力,而遥感技术提供了煤层气富集区快速勘查的新途径。煤层气富集区遥感勘查的基本原理是基于典型地物波谱特征与富集区烃微渗漏导致的地表物体异常波谱特征,包括岩矿蚀变、植被异常和热异常等波谱特征的对比,结合煤田地质、地震、大地电磁等物探方法获取的数据,进行多源信息提取与综合分析,逐步查明煤层气富集区分布范围与含气特性。文章综述了煤层气富集区烃类物质渗漏与地表岩矿波谱和植被波谱异常的响应机制,以及基于地表岩矿波谱与植被波谱参数反演的多种方法与地物波谱异常反演在煤层气潜在富集区的勘查应用;阐述了含煤层气地层导致地表热异常的不同解释与提高地表温度反演准确率的主要方法与应用。未来,遥感与煤田地质数据、地震探测和大地电磁探测相结合,开展立体多元信息分析与信息提取,将成为实现煤层气富集区低成本快速勘查的主要途径。

一种基于IGA的融合点云和影像的建筑物提取方法

针对精细化实景三维建筑物建模,提出一种基于改进遗传算法(IGA)的建筑物LiDAR点云与正射影像融合提取方法:计算并提取基于点云和影像的特征,实现点云特征空间的扩张;再改进遗传算法选择点云特征,构建并优化特征空间;最后使用SVM分类器实现建筑物点云的精准提取。在ISPRS公开数据集Vaihingen测试数据的试验表明本文方法具有较高的建筑物提取精度;在实际生产数据的实验表明建筑物提取精度较高且稳定,证明了本文方法的先进性和普适性。

联合上下文感知与边界约束的遥感影像林地提取方法

准确地提取林地分布信息是林地管理、保护和可持续利用的关键步骤。针对现有林地提取方法精度较低且边缘不够精细的问题,提出一种联合上下文感知与边界约束(CABC-Net)的遥感影像林地提取方法。首先,设计上下文感知(CA)模块,用于探索空间像素信息之间的联系,并通过层间特征传递提取足够的全局上下文信息,以降低上下文差异和复杂背景对检测结果的干扰。其次,为进一步优化边缘细节,提出边界约束(BC)模块,将边界特征与深层特征结合作为模型的辅助特征,缩小定位林地边界的波动范围,校准不确定性区域以提高边界判别能力。最后,为验证方法的有效性,建立一个新的数据集并开展案例试验分析。结果表明,该方法交并比提高0.55%~9.45%,像素准确率提高0.19%~7.53%,本方法在面对复杂场景时具有更好的边界完整度,能够更好地用于林地提取。

融合混合注意力机制与多尺度特征增强的高分影像建筑物提取

由于复杂背景变换和建筑物形状多样化等因素影响,从高分辨率遥感图像中准确提取建筑物信息面临着挑战。该文提出了一种融合混合注意力机制与多尺度特征增强的高分辨率建筑物语义分割网络(building mining net,BMNet)。首先,编码器部分使用VGG-16作为主干网络来提取特征,得到4层特征表示;然后设计解码器用于解决多尺度信息中高层特征的细节信息丢失问题,引入了混合通道注意力和空间注意力的串联注意力机制(series attention module,SAM),增强高层特征的表示能力;同时,设计了一种渐进式特征增强的建筑物信息挖掘模块(building mining module,BMM),进一步提高建筑物分割的准确性。BMM把上采样后的特征映射、经过SAM处理的特征映射以及初始预测结果作为输入,获取背景噪声信息,并利用所设计的上下文信息探索模块滤除背景信息,在经过多次BMM处理后得到最佳预测结果。对比实验结果表明:BMNet在武汉大学建筑数据集上精度和交并比分别优于U-net 4.6%和4.8%,在马萨诸塞州建筑数据集和Inria航空图像标注数据集上精度和交并比分别优于U-net 7.9%,8.9%和6.7%,11.0%,验证了所提模型的有效性以及实用性。

基于改进U-Net的遥感影像建筑物识别

目的近年来,深度学习用于提取影像中的建筑物信息已经成为遥感领域研究热点之一,为了准确高效地提取遥感影像中的建筑物信息。方法使用ResNet替换U-Net的骨干网络,并以此为基础进行改进,融合自校正卷积(SCCnov)和高效通道注意力(ECA),构建了一种新的建筑物提取网络模型。使用马萨诸塞州建筑物数据集设计消融实验,对所构建的网络模型的提取精度和实际效果进行对比分析,并使用WHU数据集验证网络的普适性,同时使用安徽某矿区的无人机影像数据集设计了迁移性实验,验证所构建网络的迁移能力。结果所构建的网络在mIoU、mPrecision和mRecall 3个指标上分别达到了82.89%、92.26%和88.32%,较改进前分别提升了1.70%、1.08%和1.19%;另外,在迁移性实验中,网络在mIoU、mPrecision和mRecall 3个指标上分别达到88.66%、94.37%和93.19%。结论本文所提出的SCEC-Unet在建筑物提取中具有良好的效果,且在独立小建筑物,异形建筑以及边缘建筑物的提取中表现较好,同时该网络具有良好的迁移能力,可用于矿区建筑物提取任务的迁移学习。

内蒙古赤峰柴胡栏子金矿田遥感地质解译和蚀变信息提取与找矿预测

内蒙古柴胡栏子金矿田位于赤峰—朝阳金成矿带的最西北侧,是我国重要的黄金产地,但其自然条件恶劣,遥感地质研究程度较低,对于最主要的控矿因素(地层、构造、岩浆岩)分布特征以及与本区金成矿相关的围岩蚀变信息研究相对较少,利用遥感技术对该矿田进行找矿勘查具有重要意义。本文利用Landsat 8和GF-2遥感影像,结合“主成分分析+最佳指数因子”的方法组合,对柴胡栏子金矿田进行遥感地质解译。根据蚀变矿物的波谱特征,设计去除干扰信息-异常信息提取-异常分级-异常信息处理的蚀变信息提取方案,对Landsat 8和Sentinel-2A遥感影像进行蚀变信息提取;在前人区域地质、矿田地质研究的基础上,依据蚀变信息特征,结合地质解译成果圈定3个找矿靶区。研究表明,利用多源遥感卫星影像对研究区进行地质解译与蚀变信息提取,基本能够满足辅助中比例尺找矿预测及地质综合调查的需要,并提升找矿效率。研究成果对柴胡栏子金矿田未来找矿勘查具有基础性指导意义。

新疆地区西昆仑和田玉遥感信息提取与找矿远景区圈定

新疆地区和田玉主要产在西昆仑至阿尔金一带,这些地区属于高海拔和严寒地区,难以直接开展地质调查工作。当前遥感技术已在热液型矿床的找矿工作中取得重要进展。和田玉矿床属于热液型矿床,地表会留下明显的矿物蚀变信息,通过遥感信息技术寻找和田玉矿床在理论上是可行的。本次研究在西昆仑康西瓦地区,使用哨兵二号、ASTER等多光谱数据和高分5号高光谱数据提取与和田玉矿化有关的蚀变信息,构建了一个以蚀变信息为主的遥感找矿找矿模型,圈定了多处和田玉遥感找矿远景区,并对找矿远景区进行了野外验证。在选取的多个和田玉遥感找矿远景区内,发现了和田玉矿的典型蚀变类型,即蛇纹石化与透闪石化,以及多处出露地表的大理岩和多处正在开采的玉石矿。野外验证结果表明,本次研究采用的和田玉遥感勘查技术是有效的。

基于多季相分形特征的Landsat 8 OLI影像耕地信息提取方法

利用遥感技术快速准确地提取耕地信息是耕地保护的关键环节。以山东省商河县为例,提出了一种基于多季相分形特征的Landsat 8 OLI影像耕地信息提取方法。首先采用毯子覆盖法计算多季相遥感影像每个像元的上分形信号和下分形信号,对比分析耕地和其他土地利用类型的分形特征,选取上分形信号的第3尺度作为特征尺度,提取商河县耕地空间分布特征;其次采用同时期的土地利用矢量数据、Esri land cover数据和统计数据进行耕地信息提取精度评价;最后分别设置多季相分形提取与单季相分形提取、现有土地利用数据产品的对比实验,并基于点位匹配度和面积匹配度进行评价。结果表明:多季相数据更能反映农作物生长的复杂性,有助于提高耕地信息的提取精度;不同土地利用类型在不同分形尺度的信号值各不相同,分形特征可以在不同尺度上清晰地刻画出不同土地利用类型的分异性;基于矢量数据和Esri land cover数据评价的多季相分形特征耕地提取点位匹配度为87.13%和89.83%,面积匹配度为99.73%和97.91%,均比单季相分形提取结果精度高;综合考虑点位匹配度、面积匹配度和空间分布特征,研发方法能有效区分耕地和其他土地利用类型,提取结果更优,且与统计数据有更高的一致性。该方法可准确提取耕地信息,为耕地的动态监测和损害评估提供技术支撑。