MAPPING OF OPHIOLITES IN THE INDUS-SUTURE ZONE OF NORTHWESTERN HIMALAYA, USING SATELLITE REMOTE SENSING DATA

Ophiolites, which have been tectonically emplaced along continental margins and island arcs, are significant to the understanding of mountain belt evolution. In the Himalayas, the ophiolitic suite of rocks occur along the Indussuture zone from Hanle in the southeast to Dras\|Kargil sector in the northwest and it represents the remnant of the compressed uplifted wedge of the oceanic crust between the two colliding continental masses, the Indian and the Asian plates.. These ophiolites are temporally and spatially correlated with the culminating phase of the Himalayan orogeny. The Indus River flows to its north separating the ophiolite from the Trans Himalayan litho\|units. Geological mapping in the hostile and inaccessible mountainous terrains of the Himalaya has always posed a great challenge to geologists. Nevertheless, a number of geologists have undertaken such arduous mapping expeditions in the past and prepared fairly good geological maps of these terrains .However there always existed disputes on the accuracy of lithological boundaries and structural details in these maps because many of these boundaries and structural features were completed through extrapolations and/or interpolations as the ruggedness and inaccessibility of a large part of the terrain forbid physical examination of every outcrop. It is in this context the potential of remote sensing, especially of satellite images, is to be appreciated.

SOME KEY ISSUES ON THE APPLICATION OF SATELLITE REMOTE SENSING TO MINING AREAS

In order to apply Satellite Remote Sensing (RS) to mining areas, some key issues should be solved. Based on an introduction to relative studying background, related key issues are proposed and analyzed oriented to the development of RS information science and demands of mining areas. Band selection and combination optimization of Landsat TM is discussed firstly, and it proved that the combination of Band 3, Band 4 and Band 5 has the largest information amount in all three-band combination schemes by both N-dimensional entropy method and Genetic Algorithm (GA). After that the filtering of Radarsat image is discussed. Different filtering methods are experimented and compared, and adaptive methods are more efficient than others. Finally the classification of satellite RS image is studied, and some new methods including classification by improved BPNN(Back Propagation Neural Network) and classification based on GIS and knowledge are proposed.

A Review of Satellite Remote Sensing Monitoring Methods for Sea Surface Oil Spill

Using satellite remote sensing to monitor oil spill on the sea is an advanced means of oil spill monitoring, and it has the characteristics of wide coverage, speediness and real time, synchronization, continuity, and low cost. Hence, accelerating the research on this technology and establishing a satellite remote sensing monitoring mechanism suitable for oil spill emergency situations is of great significance to improve China's oil spill monitoring capability and prevent or reduce the pollution damage caused by oil spill in the marine environment.This paper analyzes and studies the current situation using satellite remote sensing to monitor oil spills at home and abroad. Based on the basic principle of satellite remote sensing, this paper systematically studies the satellite remote sensing monitoring oil spill principles, satellite data processing methods and oil spill information identification, and summarizes an oil spill identification system that can realize oil spill information reproduction. This system provides an important means of support for the handling of oil spill accidents.

Delineation of groundwater potential zones using remote sensing and Geographic Information Systems(GIS)in Kadaladi region,Southern India

The primary objective of this research is to delineate potential groundwater recharge zones in the Kadaladi taluk of Ramanathapuram,Tamil Nadu,India,using a combination of remote sensing and Geographic Information Systems(GIS)with the Analytical Hierarchical Process(AHP).Various factors such as geology,geomorphology,soil,drainage,density,lineament density,slope,rainfall were analyzed at a specific scale.Thematic layers were evaluated for quality and relevance using Saaty's scale,and then inte-grated using the weighted linear combination technique.The weights assigned to each layer and features were standardized using AHP and the Eigen vector technique,resulting in the final groundwater potential zone map.The AHP method was used to normalize the scores following the assignment of weights to each criterion or factor based on Saaty's 9-point scale.Pair-wise matrix analysis was utilized to calculate the geometric mean and normalized weight for various parameters.The groundwater recharge potential zone map was created by mathematically overlaying the normalized weighted layers.Thematic layers indicating major elements influencing groundwater occurrence and recharge were derived from satellite images.2 Results indicate that approximately 21.8 km of the total area exhibits high potential for groundwater recharge.Groundwater recharge is viable in areas with moderate slopes,particularly in the central and southeastern regions.

Quantifying River Bank Erosion and Accretion Patterns along the Gorai River in Kushtia, Bangladesh: A Geospatial Analysis Utilizing GIS and Remote Sensing Techniques

River bank erosion is a natural process that occurs when the water flow of a river exceeds the bank’s ability to withstand it. It is a common phenomenon that causes extensive land damage, displacement of people, loss of crops, and infrastructure damage. The Gorai River, situated on the right bank of the Ganges, is a significant branch of the river that flows into the Bay of Bengal via the Mathumati and Baleswar rivers. The erosion of the banks of the Gorai River in Kushtia district is not a recent occurrence. Local residents have been dealing with this issue for the past hundred years, and according to the elderly members of the community, the erosion has become more severe activities. Therefore, the main objective of this research is to quantify river bank erosion and accretion and bankline shifting from 2003 to 2022 using multi-temporal Landsat images data with GIS and remote sensing technique. Bank-line migration occurs as a result of the interplay and interconnectedness of various factors such as the degree of river-related processes such as erosion, transportation, and deposition, the amount of water in the river during the high season, the geological and soil makeup, and human intervention in the river. The results show that the highest eroded area was 4.6 square kilometers during the period of 2016 to 2019, while the highest accreted area was 7.12 square kilometers during the period of 2013 to 2016. However, the erosion and accretion values fluctuated from year to year.

Estimating Rice Yield by HJ-1A Satellite Images

As illustrated by the case of Xuyi County, Jinhu County and Hongze County in Jiangsu Province, China, monitoring and forecasting of rice production were carried out by using HJ-1A satellite remote sensing images. The handhold GPS machines were used to measure the geographical position and some other information of these samples such as area shape. The GPS data and the interpretation marks were used to correct H J-1 image, assist human-computer interactive interpretation, and other operations. The test data had been participated in the whole classification process. The accuracy of interpreted information on rice planting area was more than 90% By using the leaf area index from the normalized difference vegetation index inversion, the biomass from the ratio vegetation index inversion, and combined with the rice yield estimation model, the rice yield was estimated. Further, the thematic map of rice production classification was made based on the rice yield data. According to the comparison results between measured and fitted values of yields and areas of sampling sites, the accuracy of the yield estimation was more than 85%. The results suggest that HJ-A/B images could basically meet the demand of rice growth monitoring and yield forecasting, and could be widely applied to rice production monitoring.

Land-Cover Classification and its Impact on Peshawar’s Land Surface Temperature Using Remote Sensing

Spatial and temporal informationon urban infrastructure is essential and requires various land-cover/land-use planning and management applications.Besides,a change in infrastructure has a direct impact on other land-cover and climatic conditions.This study assessed changes in the rate and spatial distribution of Peshawar district’s infrastructure and its effects on Land Surface Temperature(LST)during the years 1996 and 2019.For this purpose,firstly,satellite images of bands7 and 8 ETM+(Enhanced Thematic Mapper)plus and OLI(Operational Land Imager)of 30 m resolution were taken.Secondly,for classification and image processing,remote sensing(RS)applications ENVI(Environment for Visualising Images)and GIS(Geographic Information System)were used.Thirdly,for better visualization and more in-depth analysis of land sat images,pre-processing techniques were employed.For Land use and Land cover(LU/LC)four types of land cover areas were identified-vegetation area,water cover,urbanized area,and infertile land for the years under research.The composition of red,green,and near infra-red bands was used for supervised classification.Classified images were extracted for analyzing the relative infrastructure change.A comparative analysis for the classification of images is performed for SVM(Support Vector Machine)and ANN(Artificial Neural Network).Based on analyzing these images,the result shows the rise in the average temperature from 30.04℃ to 45.25℃.This only possible reason is the increase in the built-up area from 78.73 to 332.78 Area km^(2) from 1996 to 2019.It has also been witnessed that the city’s sides are hotter than the city’s center due to the barren land on the borders.

Study of the propagation direction of the internal waves in the South China Sea using satellite images

Internal wave propagation carries considerable vertical shear which can lead to turbulence and mixing. Based on the analysis of more than 2 500 synthetic aperture radar （SAR） and optical satellite images, the in- ternal wave propagation in the whole South China Sea was investigated systematically. The results show that （1） in the northeastern South China Sea, most internal waves propagate westward from the Luzon Strait and are diffracted by coral reefs near the Dongsha Islands. Some impinge onto the shelf and a few are reflected; （2） in the northwestern South China Sea, most internal waves are generated at the shelf and propagate northwestward or westward to the coast; （3） in the western South China Sea, most internal waves propagate westward to the Vietnamese coast, except a few propagate southward to the deep sea; and （4） in the southern South China Sea, most internal waves propagate southwestward to the coast. Some prop- agate southeastward to the coast of Kalimantan Island, and a few propagate southeastward because of the influence of the Mekon~ River.

Fusing Satellite Images Using ABC Optimizing Algorithm

Fusing satellite(remote sensing)images is an interesting topic in processing satellite images.The result image is achieved through fusing information from spectral and panchromatic images for sharpening.In this paper,a new algorithm based on based the Artificial bee colony(ABC)algorithm with peak signalto-noise ratio(PSNR)index optimization is proposed to fusing remote sensing images in this paper.Firstly,Wavelet transform is used to split the input images into components over the high and low frequency domains.Then,two fusing rules are used for obtaining the fused images.The first rule is“the high frequency components are fused by using the average values”.The second rule is“the low frequency components are fused by using the combining rule with parameter”.The parameter for fusing the low frequency components is defined by using ABC algorithm,an algorithm based on PSNR index optimization.The experimental results on different input images show that the proposed algorithm is better than some recent methods.

Towards an Intelligent Predictive Model for Analyzing Spatio-Temporal Satellite Image Based on Hidden Markov Chain

Nowadays remote sensing is an important technique for observing Earth surface applied to different areas such as, land use, urban planning, remote monitoring, real time deformation of the soil that can be associated with earthquakes or landslides, the variations in thickness of the glaciers, the measurement of volume changes in the case of volcanic eruptions, deforestation, etc. To follow the evolution of these phenomena and to predict their future states, many approaches have been proposed. However, these approaches do not respond completely to the specialists who process yet more commonly the data extracted from the images in their studies to predict the future. In this paper, we propose an innovative methodology based on hidden Markov models (HMM). Our approach exploits temporal series of satellite images in order to predict spatio-temporal phenomena. It uses HMM for representing and making prediction concerning any objects in a satellite image. The first step builds a set of feature vectors gathering the available information. The next step uses a Baum-Welch learning algorithm on these vectors for detecting state changes. Finally, the system interprets these changes to make predictions. The performance of our approach is evaluated by tests of space-time interpretation of events conducted over two study sites, using different time series of SPOT images and application to the change in vegetation with LANDSAT images.

Application Advantages of Staring Area Imaging Technology(SAIT) for Microsatellites

The Staring Area Imaging Technology(SAIT) satellite continuously "images" the target over a certain time range, and can realize continuous imaging and multi-angle imaging of the area of interest. It has the characteristics of flexible imaging parameter setting and fast image preprocessing speed, enabling dynamic target detection and tracking, super-resolution, surface 3 D model construction, night-time imaging and many other application tasks. Based on the technical characteristics of the SAIT satellite, this paper analyzes the challenges in satellite development and data processing, focuses on the quasi-realtime application of SAIT satellite data, and looks at the development trend of the SAIT satellite.

FY-4 Meteorological Satellite

FY-4 is the second generation of Chinese geostationary satellite for quantitative remote sensing meteorological application. The detection efficiency, spectral bands, spatial and time resolution have been greatly improved with respect to those of first generation, as well as the radiometric calibration and sensitivity. The combination of multichannel detection and vertical sounding was first realized on FY-4, because both the Advanced Geostationary Radiation Imager(AGRI) and Geostationary Interferometric Infrared Sounder(GIIRS) are on the same spacecraft. The main performance of the payloads including AGRI, GIIRS and Lightning Mapping Imager, and the spacecraft bus are presented, the performance being equivalent to the level of the third generation meteorological satellites in Europe and USA. The acquiring methods of remote sensing data including multichannel and high precision quantitative observing, imaging collection of the ground and cloud, vertical observation of atmospheric temperature and moisture, lightning imaging observation and space environment detection are shown. Several innovative technologies including high accuracy rotation angle detection and scanning control, high precision calibration, micro vibration suppression, unified reference of platform and payload and on-orbit measurement, real-time image navigation and registration on-orbit were applied in FY-4.

Classifications of Satellite Imagery for Identifying Urban Area Structures

This study compares three types of classifications of satellite data to identify the most suitable for making city maps in a semi-arid region. The source of our data was GeoEye 1 satellite. To classify this data, two pro-grammes were used: an Object-Based Classification and a Pixel-Based Classification. The second classification programme was further subdi-vided into two groups. The first group included classes (buildings, streets, vacant land, vegetations) which were treated simultaneously and on a single image basis. The second, however, was where each class was identified individually, and the results of each class produced a single image and were later enhanced. The classification results were then as-sessed and compared before and after enhancement using visual then automatic assessment. The results of the evaluation showed that the pix-el-based individual classification of each class was rated the highest after enhancement, increasing the Overall Classification Accuracy by 2%, from 89% to 91.00%. The results of this classification type were adopted for mapping Jeddah’s buildings, roads, and vegetations.

Ship detection and classification from optical remote sensing images: A survey

Considering the important applications in the military and the civilian domain, ship detection and classification based on optical remote sensing images raise considerable attention in the sea surface remote sensing filed. This article collects the methods of ship detection and classification for practically testing in optical remote sensing images, and provides their corresponding feature extraction strategies and statistical data. Basic feature extraction strategies and algorithms are analyzed associated with their performance and application in ship detection and classification.Furthermore, publicly available datasets that can be applied as the benchmarks to verify the effectiveness and the objectiveness of ship detection and classification methods are summarized in this paper. Based on the analysis, the remaining problems and future development trends are provided for ship detection and classification methods based on optical remote sensing images.

Surveillance of pine wilt disease by high resolution satellite

Pine wilt disease caused by the pinewood nematode Bursaphelenchus xylophilus has led to the death of a large number of pine trees in China.This destructive disease has the characteristics of bring wide-spread,fast onset,and long incubation time.Most importantly,in China,the fatality rate in pines is as high as 100%.The key to reducing this mortality is how to quickly find the infected trees.We proposed a method of automatically identifying infected trees by a convolution neural network and bounding box tool.This method rapidly locates the infected area by classifying and recognizing remote sensing images obtained by high resolution earth observation Satellite.The recognition accuracy of the test data set was 99.4%,and the remote sensing image combined with convolution neural network algorithm can identify and determine the distribution of the infected trees.It can provide strong technical support for the prevention and control of pine wilt disease.

基于多时相Sentinel-2卫星影像的冬小麦面积提取

及时准确地提取冬小麦种植信息,对开展冬小麦农情遥感监测具有重要的意义.以杭州市余杭区冬小麦越冬期(2021-12-04)、扬花期(2022-04-08)和乳熟期(2022-05-03)Sentinel-2遥感影像为数据源,分别采用最大似然法、支持向量机、归一化差值植被指数(normalized difference vegetation index,NDVI)相加和相减合成运算提取冬小麦种植面积.结合冬小麦地面调查数据与实测种植面积,对不同方法的提取结果进行精度评价.结果显示,利用越冬期影像NDVI阈值将常绿植被区(茶园、林地)掩膜处理,对非常绿植被区(建筑、水体、冬小麦)扬花期与乳熟期影像NDVI值进行和值运算,是提取余杭区冬小麦种植面积的最佳方法,面积精度为91.96%,说明基于多时相遥感影像结合植被物候特征与典型地物类型,能够实现冬小麦种植面积的高精度提取.

Railway Transport Infrastructure Monitoring by UAVs and Satellites

Improving the rail transport security requires development and implementation of neoteric monitoring and control facilities in conditions of increasing speed and intensity of the train movement and high level of terrorist threat. Use of Earth remote sensing (ERS), permitting to obtain information from large areas with a sufficiently high resolution, can provide significant assistance in solving the mentioned problems. This paper discusses the possibility of using various means of remote sensing such as satellites and unmanned aerial vehicles (UAV), also known as drones, for receiving information in different ranges of the electromagnetic spectrum. The paper states that joint using of both these means gives new possibilities in improving railroad security.

基于Swin-Unet的遥感卫星图像分割研究

在过去的几年中,卷积神经网络(CNN)在图像分割方向取得了很大的进展,但是由于卷积运算的局限性,不能很好地处理全局与长距离依赖关系等问题,提出了一种基于Swin-Unet的图像分割模型,通过将Transformer Block模块引入到U-Net网络模型中的编码与解码阶段,并使用更适合二分类的Dice_loss损失函数,来进行特征提取和学习。使用用于城市建筑物遥感卫星图像研究的Inria Aerial Image Labeling数据集进行试验。结果表明,所采用的Swin-Unet模型可以从遥感卫星图像中提取更多的语义信息,从而达到更好的识别效果,IoU分数为0.70。

基于ASPP-SCBAM-DenseUnet的高分遥感影像水体提取研究

针对遥感影像水体提取研究存在细小水体和水体边缘等细节信息关注不足的情况,以及水体连通性较差的问题,文章提出基于改进的空洞空间金字塔池化和随机双注意力机制的密集连接U型网络(ASPP-SCBAM-DenseUnet)。文章首先利用Dense Block块组成Unet的编码器和解码器部分,并引入SCBAM注意力机制,减少噪声干扰,提高水体边界分割的准确性;其次,添加ASPP_SCBAM模块,设置不同的空洞率、扩大感受野,结合小型水体的浅层和深层特征,补偿采样过程造成的特征损失;最后,通过结合Dice系数和像素级二元交叉熵的联合损失函数来训练网络,有效地处理因小水体造成的不平衡数据集,这样不仅确保了分割的精度,还能够产生更加平滑和连续的分割边界,从而防止模型出现过拟合或者过度细化的现象。实验结果表明,ASPP-SCBAM-DenseUnet网络模型提取水体的像素准确率、召回率和F1分数分别为94.19%、94.29%和95.15%,加权交并比和均交并比分别为89.02%、88.63%,明显优于Unet、Linknet等语义分割网络,同时,减少了水体误分类和遗漏,优化了水体边缘细节,提高了对细小水体的识别和水体连通性。

一种基于PIE-Engine的卫星遥感影像大气校正系统研究

该文概述了大气校正的基本内容和主要特点并剖析了其必要性,分别从背景技术、系统架构、业务流程、关键技术及具体实现5个层面,对一种时空遥感云平台(PIE-Engine)的卫星遥感影像大气校正系统进行了具体探讨,以供参考。