ConvNeXt-UperNet-Based Deep Learning Model for Road Extraction from High-Resolution Remote Sensing Images

When existing deep learning models are used for road extraction tasks from high-resolution images,they are easily affected by noise factors such as tree and building occlusion and complex backgrounds,resulting in incomplete road extraction and low accuracy.We propose the introduction of spatial and channel attention modules to the convolutional neural network ConvNeXt.Then,ConvNeXt is used as the backbone network,which cooperates with the perceptual analysis network UPerNet,retains the detection head of the semantic segmentation,and builds a new model ConvNeXt-UPerNet to suppress noise interference.Training on the open-source DeepGlobe and CHN6-CUG datasets and introducing the DiceLoss on the basis of CrossEntropyLoss solves the problem of positive and negative sample imbalance.Experimental results show that the new network model can achieve the following performance on the DeepGlobe dataset:79.40%for precision(Pre),97.93% for accuracy(Acc),69.28% for intersection over union(IoU),and 83.56% for mean intersection over union(MIoU).On the CHN6-CUG dataset,the model achieves the respective values of 78.17%for Pre,97.63%for Acc,65.4% for IoU,and 81.46% for MIoU.Compared with other network models,the fused ConvNeXt-UPerNet model can extract road information better when faced with the influence of noise contained in high-resolution remote sensing images.It also achieves multiscale image feature information with unified perception,ultimately improving the generalization ability of deep learning technology in extracting complex roads from high-resolution remote sensing images.

Weakly Supervised Network with Scribble-Supervised and Edge-Mask for Road Extraction from High-Resolution Remote Sensing Images

Significant advancements have been achieved in road surface extraction based on high-resolution remote sensingimage processing. Most current methods rely on fully supervised learning, which necessitates enormous humaneffort to label the image. Within this field, other research endeavors utilize weakly supervised methods. Theseapproaches aim to reduce the expenses associated with annotation by leveraging sparsely annotated data, such asscribbles. This paper presents a novel technique called a weakly supervised network using scribble-supervised andedge-mask (WSSE-net). This network is a three-branch network architecture, whereby each branch is equippedwith a distinct decoder module dedicated to road extraction tasks. One of the branches is dedicated to generatingedge masks using edge detection algorithms and optimizing road edge details. The other two branches supervise themodel’s training by employing scribble labels and spreading scribble information throughout the image. To addressthe historical flaw that created pseudo-labels that are not updated with network training, we use mixup to blendprediction results dynamically and continually update new pseudo-labels to steer network training. Our solutiondemonstrates efficient operation by simultaneously considering both edge-mask aid and dynamic pseudo-labelsupport. The studies are conducted on three separate road datasets, which consist primarily of high-resolutionremote-sensing satellite photos and drone images. The experimental findings suggest that our methodologyperforms better than advanced scribble-supervised approaches and specific traditional fully supervised methods.

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.

Automatic Extraction of Typical River Vegetation Elements Based on Low-altitude Remote Sensing Images

Based on low-altitude remote sensing images,this paper established sample set of typical river vegetation elements and proposed river vegetation extraction technical solution to adaptively extract typical vegetation elements of river basins.The main research of this paper were as follows:(1)a typical vegetation extraction sample set based on low-altitude remote sensing images was established.(2)A low-altitude remote sensing image vegetation extraction model based on the focus perception module was designed to realize the end-to-end automatic extraction of different types of vegetation areas of low-altitude remote sensing images to fully learn the spectral spatial texture information and deep semantic information of the images.(3)By comparison with the baseline method,baseline method with embedded focus perception module showed an improvement in the precision by 7.37%and mIoU by 49.49%.Through visual interpretation and quantitative calculation analysis,the typical river vegetation adaptive extraction network has effectiveness and generalization ability,consistent with the needs of practical applications of vegetation extraction.

Privacy‐preserving remote sensing images recognition based on limited visual cryptography

With the arrival of new data acquisition platforms derived from the Internet of Things(IoT),this paper goes beyond the understanding of traditional remote sensing technologies.Deep fusion of remote sensing and computer vision has hit the industrial world and makes it possible to apply Artificial intelligence to solve problems such as automatic extraction of information and image interpretation.However,due to the complex architecture of IoT and the lack of a unified security protection mechanism,devices in remote sensing are vulnerable to privacy leaks when sharing data.It is necessary to design a security scheme suitable for computation‐limited devices in IoT,since traditional encryption methods are based on computational complexity.Visual Cryptography(VC)is a threshold scheme for images that can be decoded directly by the human visual system when superimposing encrypted images.The stacking‐to‐see feature and simple Boolean decryption operation make VC an ideal solution for privacy‐preserving recognition for large‐scale remote sensing images in IoT.In this study,the secure and efficient transmission of high‐resolution remote sensing images by meaningful VC is achieved.By diffusing the error between the encryption block and the original block to adjacent blocks,the degradation of quality in recovery images is mitigated.By fine‐tuning the pre‐trained model from large‐scale datasets,we improve the recognition performance of small encryption datasets for remote sensing images.The experimental results show that the proposed lightweight privacy‐preserving recognition framework maintains high recognition performance while enhancing security.

Study on Soil Salinization Information in Arid Region Using Remote Sensing Technique

Extracting information about saline soils from remote sensing data is useful, particularly given the environmental significance and changing nature of these areas in arid environments. One interesting case study to consider is the delta oasis of the Weigan and Kuqa rivers, China, which was studied using a Landsat Enhanced Thematic Mapper Plus （ETM＋） image collected in August 2001. In recent years, decision tree classifiers have been successfully used for land cover classification from remote sensing data. Principal component analysis （PCA） is a popular data reduction technique used to help build a decision tree; it reduces complexity and can help the classification precision of a decision tree to be improved. A decision tree approach was used to determine the key variables to be used for classification and ultimately extract salinized soil from other cover and soil types within the study area. According to the research, the third principal component （PC3） is an effective variable in the decision tree classification for salinized soil information extraction. The research demonstrated that the PC3 was the best band to identify areas of severely salinized soil; the blue spectral band from the ETM＋ sensor （TM1） was the best band to identify salinized soil with the salt-tolerant vegetation of tamarisk （Tamarix chinensis Lour）; and areas comprising mixed water bodies and vegetation can be identified using the spectral indices MNDWI （modified normalized difference water index） and NDVI （normalized difference vegetation index）. Based upon this analysis, a decision tree classifier was applied to classify landcover types with different levels of soil saline. The results were checked using a statistical accuracy assessment. The overall accuracy of the classification was 94.80%, which suggested that the decision tree model is a simple and effective method with relatively high precision.

Quantizing and analyzing the feature information of coastal zone based on high-resolution remote sensing image

On the basis of realization of beach information and its differentiating of high-resolution remote sensing image on coastal zone, extracting objects are carried through RS multi-scale diagnostic analysis, and fast information extraction methods and key technologies are put forward. Meanwhile image segmentation methods are set forth for objects of coastal zone. And through the application of Otsu2D to the segmentation of water area and dock and the applying of Gabor filter to the separation and extraction of construction, some typical applications of high-resolution RS image are presented in the field of coastal zone surface objects＇ recognition. Quantizing high-resolution RS information on the coastal zone proved to be of great scientific and practical significance for coastal development and management.

Remote Sensing Technology for Identification of Alteration Information of Gold Deposits in the Eastern Tianshan Area, Xinjiang

Based on specific well-exposed rocks useful for high-quality remote sensing interpretation in the gold-prospecting area in the eastern Tianshan, this paper gives a detailed description of a remote sensing model for metallogenic prediction. The model reveals that multi-spectral remote sensing data are integrated with high-resolution remote sensing data, and enhanced extraction and visual description of weak remote sensing information are used for prospecting. This model has tested in the given gold deposit, and used successfully in Au-Cu prospecting in the Kalatage area.

A New Generation of Intelligent Mapping and Remote Sensing Scientific Test Satellite Luojia-301

With the continuous improvement of the performance and the increasing variety of optical mapping and remote sensing satellites,they have become an important support for obtaining global accurate surveying and mapping remote sensing information.At present,optical mapping and remote sensing satellites already have sub-meter spatial resolution capabilities,but there is a serious lag problem in mapping and remote sensing information services.It is urgent to develop intelligent mapping and remote sensing satellites to promote the transformation and upgrading to real-time intelligent services.Firstly,based on the three imaging systems of the optical mapping and remote sensing satellites and their realization methods and application characteristics,this paper analyzes the applicable system of the intelligent mapping and remote sensing satellites.Further,according to the application requirements of real-time,intelligence,and popularization,puts forward the design concept of integrated intelligent remote sensing satellite integrating communication,navigation,and remote sensing and focuses on the service mode and integrated function composition of intelligent remote sensing satellite.Then expounds on the performance and characteristics of the Luojia-301 satellite,a new generation of intelligent surveying and mapping remote sensing scientific test satellite.And finally summarizes and prospects the development and mission of intelligent mapping remote sensing satellites.Luojia-301 satellite integrates remote sensing and communication functions.It explores an efficient and intelligent service mode of mapping and remote sensing information from data acquisition to the application terminal and provides a real service verification platform for on-orbit processing and real-time transmission of remote sensing data based on space-ground internet,which is of great significance to the construction of China’s spatial information network.

Separate Analysis of Remote Sensing Information of Structures of Different Geological Periods and Quantitative Study of Corresponding Tectonic Stress Fields

The structural feature shown on a remote sensing image is a synthetic result ofcombination of the deformations produced during the entire geological history of an area.Therefore, the respective tectonic stress field of each of the different stages in the complexdeformation of an area can be reconstructed in three steps: (1) geological structures formed atdifferent times are distinguished in remote sensing image interpretation; (2) structuraldeformation fields at different stages are determined by analyzing relationships betweenmicrostructures (joints and fractures) and the related structures (folds and faults); and (3)tectonic stress fields at different stages are respectively recovered through a study of the featuresof structural deformation fields in different periods. Circular structures and related circlular and radial joints are correlated in space to con-cealed structural rises. The authors propose a new method for establishing a natural model ofthe concealed structural rises and calculating the tectonic stress field by using quantitative dataof the remote sensing information of circular structures and related linear structures.

Analysis of Morphometric Parameters and Radioactive Characteristics Using Remote Sensing Data and GIS Techniques in the Wadi Wizr Basin Area, Central Eastern Desert, Egypt

The purpose of this study is to demonstrate how modern technologies such as geographic information systems (GIS) and digital elevation models can help in the creation of a geographic database for the Wadi Wizr basin in Egypt’s Central Eastern Desert, in addition to examining and analysing the radioactive properties of various rocks. This was accomplished with the help of a digital elevation model (DEM) with a 30 metre accuracy and GIS software in 10.8 Arc Map. The RS-230 was also used to measure uranium and thorium concentrations. GIS softwares and digital elevation models have been shown to be more effective than the traditional method. This was demonstrated by the flexible and quick working method, the accuracy of the parameters used, and the results of the morphometric analysis of the basin river network. In addition to, the main drainage pattern from subtype to tree type, where the branching ratio was (1.59). This basin could also cause flooding. Similar studies, according to the results of this study, should make greater use of geographic information system technology and modern data sources. Wadi Wizr also has a radioactive anomaly, with uranium equivalent concentrations reaching 70 ppm in some fault parts.

A Road Extraction Method for Remote Sensing Image Based on Encoder-Decoder Network

According to the characteristics of the road features,an Encoder-Decoder deep semantic segmentation network is designed for the road extraction of remote sensing images.Firstly,as the features of the road target are rich in local details and simple in semantic features,an Encoder-Decoder network with shallow layers and high resolution is designed to improve the ability to represent detail information.Secondly,as the road area is a small proportion in remote sensing images,the cross-entropy loss function is improved,which solves the imbalance between positive and negative samples in the training process.Experiments on large road extraction datasets show that the proposed method gets the recall rate 83.9%,precision 82.5%and F1-score 82.9%,which can extract the road targets in remote sensing images completely and accurately.The Encoder-Decoder network designed in this paper performs well in the road extraction task and needs less artificial participation,so it has a good application prospect.

Mapping potential areas for groundwater storage in the High Guir Basin(Morocco):Contribution of remote sensing and geographic information system

Identification of water potential areas in arid regions is a crucial element for the enhancement of their water resources and socio-economic development. In fact, water resources system-planning can be used to make various decisions and implement manage- ment of water resources policies. The purpose of this study is to identify groundwater sto- rage areas in the high Guir Basin by implementing Geographic Information System (GIS) and Remote Sensing methods. The required data for this study can be summarized into five critical factors: Topography (slope), lithology, rainfall, rock fracture and drainage. These critical factors have been converted by the GIS into thematic maps. For each cri- tical parameter, a coefficient with weight was attributed according to its importance. The map of potential groundwater storage areas is obtained by adding the products (coeffi- cient × weight) of the five parameters. The results show that 50% to 64% of the total area of the High Guir Basin is potentially rich in groundwater, where most of fracture systems are intensely developed. The obtained results are validated with specific yield of the aqui- fer in the study area. It is noted that there is a strong positive correlation between excel- lent groundwater potential zones with high flows of water points and it diminishes with low specific yield with poor potential zones.

The remote sensing composite information entropy and types of Nansha coral reef atolls

In the viempint that the coral reef atolls' growth index of the Nansha Islands is influenced by many factors, the measured remote sensing comopite information including some mutually related factors is divided into 10 geographic events as N1, N2 ..., N10, and the analysis of the atolls' information entropy is made. From the value of theentropy, the closed related factors with the index of the emerged atolls are shown. In proper order, the factors are reeftop's area(0. 319), lagoon's area(0. 324), open-degree of atoll(0. 336), trend of atoll(0. 551 ). On the basis of thiswork, a new description function of the emerged atoll growth index is proposed. This function can be used to identifythe open my of Nansha atoll growth.

Extraction of coastline in high-resolution remote sensing images based on the active contour model

While executing tasks such as ocean pollution monitoring,maritime rescue,geographic mapping,and automatic navigation utilizing remote sensing images,the coastline feature should be determined.Traditional methods are not satisfactory to extract coastline in high-resolution panchromatic remote sensing image.Active contour model,also called snakes,have proven useful for interactive specification of image contours,so it is used as an effective coastlines extraction technique.Firstly,coastlines are detected by water segmentation and boundary tracking,which are considered initial contours to be optimized through active contour model.As better energy functions are developed,the power assist of snakes becomes effective.New internal energy has been done to reduce problems caused by convergence to local minima,and new external energy can greatly enlarge the capture region around features of interest.After normalization processing,energies are iterated using greedy algorithm to accelerate convergence rate.The experimental results encompassed examples in images and demonstrated the capabilities and efficiencies of the improvement.

Plotting the Sequential Rock Remote Sensing Information by Optimization Dichotomy Method

The sequential rock remote sensing information is a group of rocks that are correlative in space or in space and time. For the sake of plottiug them, someone had brought forward the optimization segn.entotion metkod. We have ased this method to plot the sequential rock remote sensing information at tbe remote sensing hyperspetral test field of Daqing mountain, Inner Mongolia Autonomous Region, China, and found some disadvantages of this method. Therefore, we put forward the optimization dichotomy to plot them, and get better results. Finally we make a conclusion.

THE CLIMATIC STUDY OF CHANGBAI MOUNTAIN BY INTEGRATION OF REMOTE SENSING INFORMATION AND GEO-CODED IMAGES

Ⅰ. INTRODUCTION Changbai Mountain is situated between E127°54′-128°08′, N40°58′-42°06′ about 2700 meters above sea level. It is the typical area of the mountainous climate in the monsoon area of the temperate zone on the globe. The well reserved primeval vertical distribution of natural landscape belts and the Natural Conservation of Changbai Mountains adopted by the UNESCO′s MAB Program cause the worldwide attention of geographers. Beside the complexity of the climatic structure itself, the mechanical effection of the high mountain body also effect the climate in the eastern part of China. In the mountain area where short of meteorological observation data, the climatic study by remote sensing is favorable for discovery and representation of climatic law in large area.

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 and geographic information systems techniques in studies on treeline ecotone dynamics

We performed a meta-analysis on over 100 studies applying remote sensing(RS)and geographic information systems(GIS)to understand treeline dynamics.A literature search was performed in multiple online databases,including Web of Knowledge(Thomson Reuters),Scopus(Elsevier),BASE(Bielefeld Academic Search Engine),CAB Direct,and Google Scholar using treeline-related queries.We found that RS and GIS use has steadily increased in treeline studies since 2000.Spatialresolution RS and satellite imaging techniques varied from low-resolution MODIS,moderate-resolution Landsat,to high-resolution WorldView and aerial orthophotos.Most papers published in the 1990s used low to moderate resolution sensors such as Landsat Multispectral Scanner and Thematic Mapper,or SPOT PAN(Panchromatic)and MX(Multispectral)RS images.Subsequently,we observed a rise in high-resolution satellite sensors such as ALOS,GeoEye,IKONOS,and WorldView for mapping current and potential treelines.Furthermore,we noticed a shift in emphasis of treeline studies over time:earlier reports focused on mapping treeline positions,whereas RS and GIS are now used to determine the factors that control treeline variation.

Feature extraction and classification of hyperspectral remote sensing image oriented to easy mixed-classified objects

The classification of hyperspectral remote sensing data is an important problem theoretically and practically. With the increase of spectral bands, the separability of objects on remote sensing image should be improved. But the effects of traditional algorithm on feature extraction such as principal component analysis(PCA) is not so good for hyperspectral image. The key problem is that PCA can only represent the linear structure of data set; while the data clouds of different objects on hyperspectral image usually distribute on a nonlinear manifold. This paper established an algorithm of nonlinear feature extraction named as nonlinear principal poly lines, based on the algorithm, a classifier is constructed and the classification accuracy of hyperspectral image can be improved.