Feature extraction and analysis of reclaimed vegetation in ecological restoration area of abandoned mines based on hyperspectral remote sensing images

The vegetation growth status largely represents the ecosystem function and environmental quality.Hyperspectral remote sensing data can effectively eliminate the effects of surface spectral reflectance and atmospheric scattering and directly reflect the vegetation parameter information.In this study,the abandoned mining area in the Helan Mountains,China was taken as the study area.Based on hyperspectral remote sensing images of Zhuhai No.1 hyperspectral satellite,we used the pixel dichotomy model,which was constructed using the normalized difference vegetation index(NDVI),to estimate the vegetation coverage of the study area,and evaluated the vegetation growth status by five vegetation indices(NDVI,ratio vegetation index(RVI),photochemical vegetation index(PVI),red-green ratio index(RGI),and anthocyanin reflectance index 1(ARI1)).According to the results,the reclaimed vegetation growth status in the study area can be divided into four levels(unhealthy,low healthy,healthy,and very healthy).The overall vegetation growth status in the study area was generally at low healthy level,indicating that the vegetation growth status in the study area was not good due to short-time period restoration and harsh damaged environment such as high and steep rock slopes.Furthermore,the unhealthy areas were mainly located in Dawukougou where abandoned mines were concentrated,indicating that the original mining activities have had a large effect on vegetation ecology.After ecological restoration of abandoned mines,the vegetation coverage in the study area has increased to a certain extent,but the amplitude was not large.The situation of vegetation coverage in the northern part of the study area was worse than that in the southern part,due to abandoned mines mainly concentrating in the northern part of the Helan Mountains.The combination of hyperspectral remote sensing data and vegetation indices can comprehensively extract the characteristics of vegetation,accurately analyze the plant growth status,and provide technical support for vegetation health evaluation.

Hyperspectral remote sensing identification of marine oil emulsions based on the fusion of spatial and spectral features

Marine oil spill emulsions are difficult to recover,and the damage to the environment is not easy to eliminate.The use of remote sensing to accurately identify oil spill emulsions is highly important for the protection of marine environments.However,the spectrum of oil emulsions changes due to different water content.Hyperspectral remote sensing and deep learning can use spectral and spatial information to identify different types of oil emulsions.Nonetheless,hyperspectral data can also cause information redundancy,reducing classification accuracy and efficiency,and even overfitting in machine learning models.To address these problems,an oil emulsion deep-learning identification model with spatial-spectral feature fusion is established,and feature bands that can distinguish between crude oil,seawater,water-in-oil emulsion(WO),and oil-in-water emulsion(OW)are filtered based on a standard deviation threshold–mutual information method.Using oil spill airborne hyperspectral data,we conducted identification experiments on oil emulsions in different background waters and under different spatial and temporal conditions,analyzed the transferability of the model,and explored the effects of feature band selection and spectral resolution on the identification of oil emulsions.The results show the following.(1)The standard deviation–mutual information feature selection method is able to effectively extract feature bands that can distinguish between WO,OW,oil slick,and seawater.The number of bands was reduced from 224 to 134 after feature selection on the Airborne Visible Infrared Imaging Spectrometer(AVIRIS)data and from 126 to 100 on the S185 data.(2)With feature selection,the overall accuracy and Kappa of the identification results for the training area are 91.80%and 0.86,respectively,improved by 2.62%and 0.04,and the overall accuracy and Kappa of the identification results for the migration area are 86.53%and 0.80,respectively,improved by 3.45%and 0.05.(3)The oil emulsion identification model has a certain degree of transferability and can effectively identify oil spill emulsions for AVIRIS data at different times and locations,with an overall accuracy of more than 80%,Kappa coefficient of more than 0.7,and F1 score of 0.75 or more for each category.(4)As the spectral resolution decreasing,the model yields different degrees of misclassification for areas with a mixed distribution of oil slick and seawater or mixed distribution of WO and OW.Based on the above experimental results,we demonstrate that the oil emulsion identification model with spatial–spectral feature fusion achieves a high accuracy rate in identifying oil emulsion using airborne hyperspectral data,and can be applied to images under different spatial and temporal conditions.Furthermore,we also elucidate the impact of factors such as spectral resolution and background water bodies on the identification process.These findings provide new reference for future endeavors in automated marine oil spill detection.

Remote sensing of air pollution incorporating integrated-path differential-absorption and coherent-Doppler lidar

An innovative complex lidar system deployed on an airborne rotorcraft platform for remote sensing of atmospheric pollution is proposed and demonstrated.The system incorporates integrated-path differential absorption lidar(DIAL) and coherent-doppler lidar(CDL) techniques using a dual tunable TEA CO_(2)laser in the 9—11 μm band and a 1.55 μm fiber laser.By combining the principles of differential absorption detection and pulsed coherent detection,the system enables agile and remote sensing of atmospheric pollution.Extensive static tests validate the system’s real-time detection capabilities,including the measurement of concentration-path-length product(CL),front distance,and path wind speed of air pollution plumes over long distances exceeding 4 km.Flight experiments is conducted with the helicopter.Scanning of the pollutant concentration and the wind field is carried out in an approximately 1 km slant range over scanning angle ranges from 45°to 65°,with a radial resolution of 30 m and10 s.The test results demonstrate the system’s ability to spatially map atmospheric pollution plumes and predict their motion and dispersion patterns,thereby ensuring the protection of public safety.

Quantifying glacier surging and associated lake dynamics in Amu Darya river basin using UAV and remote sensing data

Glaciers in the Pamir region are experiencing rapid melting and receding due to climate change,which has a significant implication for the Amu Darya river basin.Predominantly,surging glaciers,which undergo unpredictable advances,are potentially leading to the obstruction of high-altitude river channels and also glacial lake outburst floods.decrease of-703.5±30.0 m.There is a substantial increase in the number(from 19 to 75)and area(from 4889.7±0.6 m2 to 15345.5±0.6 m2)of RGS lakes along with supra-glacier ponds based on a comparison of ArcGIS base map in 2011 and high-resolution UAV data in 2023.For M glacier,number of lakes increased from 4 to 22 but the lake area declined from 10715.2±0.6 to 365.6±0.6 m2.It was noted that the largest lake in 2011 with an area of 10406.4±0.6 m2 at the southeastern portion of the glacier was not observed in 2023 due to outburst.Both the glaciers have substantially impacted the river flow(Abdukahor river)by obstructing a significant proportion of river channel in recent years and might cause outburst floods.These findings enhance the understanding of glacier dynamics and their impacts on the surrounding areas,emphasizing the urgent need for continued monitoring and appropriate management strategies,with a specific focus on surging glaciers and the associated risks.

Using ontology and rules to retrieve the semantics of disaster remote sensing data

Remote sensing data plays an important role in natural disaster management.However,with the increase of the variety and quantity of remote sensors,the problem of“knowledge barriers”arises when data users in disaster field retrieve remote sensing data.To improve this problem,this paper proposes an ontology and rule based retrieval(ORR)method to retrieve disaster remote sensing data,and this method introduces ontology technology to express earthquake disaster and remote sensing knowledge,on this basis,and realizes the task suitability reasoning of earthquake disaster remote sensing data,mining the semantic relationship between remote sensing metadata and disasters.The prototype system is built according to the ORR method,which is compared with the traditional method,using the ORR method to retrieve disaster remote sensing data can reduce the knowledge requirements of data users in the retrieval process and improve data retrieval efficiency.

Retrieval of chlorophyll a and suspended solid concen-trations by hyperspectral remote sensing in Taihu Lake,China

Chlorophyll α（ch1-α） and suspended solid concentrations are two frequently used water quality parameters for monitoring a lake. Traditional measurement of ch1-α and suspended solids, requiring laborious laboratory work, which is often expensive and time consuming. Hyperspectral remote-sensing measurement provides a fast and easy tool for estimating water trophic status. In situ hyperspectral data on March 7-8, July 6-7, September 20 and December 7-8, 2004 and the corresponding water chemical data were used to regress the algorithm of water quality parameters. Results showed that the peak of water leaving radiance around 700 nm （R700） varied proportionally with ch1-α concentration, and moved to infrared when algal bloom occurred. The reflectance ratio of R702/R685 was well correlated with ch1-α when water surface in no algal bloom case and the correlation coefficient was better if absorption of phycocyanin was considered. The reflectance ratio R620/R531 was highly correlated to the concentration of suspended solids. The relationship between suspended solids and other band groups were also compared. Secchi disk depth could be calculated by non-linear correlation with suspended solids concentration.

Classification of hyperspectral remote sensing images based on simulated annealing genetic algorithm and multiple instance learning

A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algonthrn and multiple instance learning （MIL）. The band selection method was proposed from subspace decomposition, which combines the simulated annealing algorithm with the genetic algorithm in choosing different cross-over and mutation probabilities, as well as mutation individuals. Then MIL was combined with image segmentation, clustering and support vector machine algorithms to classify hyperspectral image. The experimental results show that this proposed method can get high classification accuracy of 93.13% at small training samples and the weaknesses of the conventional methods are overcome.

The application of hyperspectral remote sensing to coast environment investigation

Requirements for monitoring the coastal zone environment are first summarized. Then the appli- cation of hyperspectral remote sensing to coast environment investigation is introduced, such as the classification of coast beaches and bottom matter, target recognition, mine detection, oil spill identification and ocean color remote sensing. Finally, what is needed to follow on in application of hyperspectral remote sensing to coast environment is recommended.

Progress of Geological Survey Using Airborne Hyperspectral Remote Sensing Data in the Gansu and Qinghai Regions

Hyperspectral remote sensing is now a frontier of the remote sensing technology. Airborne hyperspectral remote sensing data have hundreds of narrow bands to obtain complete and continuous ground-object spectra. Therefore, they can be effectively used to identify these grotmd objects which are difficult to discriminate by using wide-band data, and show much promise in geological survey. At the height of 1500 m, have 36 bands in visible to the CASI hyperspectral data near-infrared spectral range, with a spectral resolution of 19 nm and a space resolution of 0.9 m. The SASI data have 101 bands in the shortwave infrared spectral range, with a spectral resolution of 15 nm and a space resolution of 2.25 m. In 2010, China Geological Survey deployed an airborne CASI/SASI hyperspectral measurement project, and selected the Liuyuan and Fangshankou areas in the Beishan metallogenic belt of Gansu Province, and the Nachitai area of East Kunlun metallogenic belt in Qinghai Province to conduct geological survey. The work period of this project was three years.

Towards a semi-empirical model of the sea ice thickness based on hyperspectral remote sensing in the Bohai Sea

Sea ice thickness is one of the most important input parameters for the prevention and mitigation of sea ice disasters and the prediction of local sea environments and climates. Estimating the sea ice thickness is currently the most important issue in the study of sea ice remote sensing. With the Bohai Sea as the study area, a semiempirical model of the sea ice thickness（SEMSIT） that can be used to estimate the thickness of first-year ice based on existing water depth estimation models and hyperspectral remote sensing data according to an optical radiative transfer process in sea ice is proposed. In the model, the absorption and scattering properties of sea ice in different bands（spectral dimension information） are utilized. An integrated attenuation coefficient at the pixel level is estimated using the height of the reflectance peak at 1 088 nm. In addition, the surface reflectance of sea ice at the pixel level is estimated using the 1 550–1 750 nm band reflectance. The model is used to estimate the sea ice thickness with Hyperion images. The first validation results suggest that the proposed model and parameterization scheme can effectively reduce the estimation error associated with the sea ice thickness that is caused by temporal and spatial heterogeneities in the integrated attenuation coefficient and sea ice surface. A practical semi-empirical model and parameterization scheme that may be feasible for the sea ice thickness estimation using hyperspectral remote sensing data are potentially provided.

Indication of the Expression of Transgene in Rice Plant Based on Hyperspectral Remote Sensing Technique Ⅱ——Growth Monitoring of Samples in the Contrast Experiment

Since the complication of monitoring and evaluating the problems about the transgenic expression and its impacts on the receptor in the transgenic crop breeding and other relevant evaluated works,the authors in the present work tried to assess the differences of spectral parameters of the transgenic rice in contrast with its parent group quantitatively and qualitatively,fulfilling the growth monitoring of the transgenic samples.The spectral parameters(spectral morphological characteristics and indices) chosen are highly related to internal or external stresses to the receipts,and thus could be applied as indicators of biophysical or biochemical processes changes of plant.By ASD portable field spectroradiometer with high-density probe,fine foliar spectra of 8 groups were obtained.By analyzing spectral angle and continuum removal,the spectral morphological differences and their locations of sample spectra were found which could be as auxiliary priori knowledge for quantitative analysis.By investigating spectral indices of the samples,the quantitative differences of spectra were revealed about foliar chlorophyll a+b and carotenoid content.In this study both the spectral differences between transgenic and parent groups and among transgenic groups were investigated.The results show that hyperspectral technique is promising and a helpful auxiliary tool in the study of monitoring the transgenic crop and other relevant researches.By this technique,quantitative and qualitative results of sample spectra could be provided as prior knowledge,as certain orientation,for laboratory professional advanced transgenic breeding study.

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.

CrossFormer Embedding DeepLabv3+ for Remote Sensing Images Semantic Segmentation

High-resolution remote sensing image segmentation is a challenging task. In urban remote sensing, the presenceof occlusions and shadows often results in blurred or invisible object boundaries, thereby increasing the difficultyof segmentation. In this paper, an improved network with a cross-region self-attention mechanism for multi-scalefeatures based onDeepLabv3+is designed to address the difficulties of small object segmentation and blurred targetedge segmentation. First,we use CrossFormer as the backbone feature extraction network to achieve the interactionbetween large- and small-scale features, and establish self-attention associations between features at both large andsmall scales to capture global contextual feature information. Next, an improved atrous spatial pyramid poolingmodule is introduced to establish multi-scale feature maps with large- and small-scale feature associations, andattention vectors are added in the channel direction to enable adaptive adjustment of multi-scale channel features.The proposed networkmodel is validated using the PotsdamandVaihingen datasets. The experimental results showthat, compared with existing techniques, the network model designed in this paper can extract and fuse multiscaleinformation, more clearly extract edge information and small-scale information, and segment boundariesmore smoothly. Experimental results on public datasets demonstrate the superiority of ourmethod compared withseveral state-of-the-art networks.

Determining the planting year of navel orange trees in mountainous and hilly areas of southern China:a remote sensing based method

Remote sensing has demonstrated validity in determining the planting year of deciduous fruit trees;however,its effectiveness in ascertaining the planting year of evergreen fruit trees remains unverified.Furthermore,the sources of error associated with using remote sensing to determine the planting year of fruit trees remain unclear.This study investigates several cultivated sweet orange(Citrus sinensis)varieties,which are extensively cultivated throughout subtropical China.We analyzed Landsat time series data from 132 navel orange orchards in Gannan,covering the period from 1993 to 2021.For each orchard,Google Earth Engine was employed to extract three vegetation indices—Enhanced Vegetation Index(EVI),Normalized Difference Vegetation Index(NDVI),and Normalized Burn Ratio(NBR)—for each available date,thereby generating three distinct vegetation index time series.The planting year of navel orange trees was identified based on abrupt changes observed in these time series.The principal sources of error in determining the planting year were investigated using the Wilcoxon signed-rank test,Spearman's correlation analysis,and Kruskal-Wallis H test.Key findings include:(1)Following the planting of navel orange trees,EVI,NDVI,and NBR exhibited fluctuations and a gradual increase over time,peaking approximately 10 to 15 years later.(2)The vegetation index time series derived from Landsat imagery effectively determined the planting year of evergreen navel orange trees in orchards,even within highly fragmented landscapes.Among these indices,NDVI and NBR time series outperformed the EVI time series.Specifically,the average determination errors for EVI,NDVI,and NBR time series were 6.4,1.8,and 2.8 years,respectively.(3)Major sources of error included the methods used to construct the time series,the selection of vegetation indices,and the orchard management practices.Overall,this study provides a viable method for determining the planting year of evergreen navel orange trees in fragmented landscapes and offers insights into factors contributing to uncertainty in planting year determination.

Remote sensing of quality traits in cereal and arable production systems:A review

Cereal is an essential source of calories and protein for the global population.Accurately predicting cereal quality before harvest is highly desirable in order to optimise management for farmers,grading harvest and categorised storage for enterprises,future trading prices,and policy planning.The use of remote sensing data with extensive spatial coverage demonstrates some potential in predicting crop quality traits.Many studies have also proposed models and methods for predicting such traits based on multiplatform remote sensing data.In this paper,the key quality traits that are of interest to producers and consumers are introduced.The literature related to grain quality prediction was analyzed in detail,and a review was conducted on remote sensing platforms,commonly used methods,potential gaps,and future trends in crop quality prediction.This review recommends new research directions that go beyond the traditional methods and discusses grain quality retrieval and the associated challenges from the perspective of remote sensing data.

Transformer-Based Cloud Detection Method for High-Resolution Remote Sensing Imagery

Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and environmentalmonitoring.Addressing the limitations of conventional convolutional neural networks,we propose an innovative transformer-based method.This method leverages transformers,which are adept at processing data sequences,to enhance cloud detection accuracy.Additionally,we introduce a Cyclic Refinement Architecture that improves the resolution and quality of feature extraction,thereby aiding in the retention of critical details often lost during cloud detection.Our extensive experimental validation shows that our approach significantly outperforms established models,excelling in high-resolution feature extraction and precise cloud segmentation.By integrating Positional Visual Transformers(PVT)with this architecture,our method advances high-resolution feature delineation and segmentation accuracy.Ultimately,our research offers a novel perspective for surmounting traditional challenges in cloud detection and contributes to the advancement of precise and dependable image analysis across various domains.

Untethered Micro/Nanorobots for Remote Sensing:Toward Intelligent Platform

Untethered micro/nanorobots that can wirelessly control their motion and deformation state have gained enormous interest in remote sensing applications due to their unique motion characteristics in various media and diverse functionalities.Researchers are developing micro/nanorobots as innovative tools to improve sensing performance and miniaturize sensing systems,enabling in situ detection of substances that traditional sensing methods struggle to achieve.Over the past decade of development,significant research progress has been made in designing sensing strategies based on micro/nanorobots,employing various coordinated control and sensing approaches.This review summarizes the latest developments on micro/nanorobots for remote sensing applications by utilizing the self-generated signals of the robots,robot behavior,microrobotic manipulation,and robot-environment interactions.Providing recent studies and relevant applications in remote sensing,we also discuss the challenges and future perspectives facing micro/nanorobots-based intelligent sensing platforms to achieve sensing in complex environments,translating lab research achievements into widespread real applications.

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.

Advancements in Remote Sensing Image Dehazing: Introducing URA-Net with Multi-Scale Dense Feature Fusion Clusters and Gated Jump Connection

The degradation of optical remote sensing images due to atmospheric haze poses a significant obstacle,profoundly impeding their effective utilization across various domains.Dehazing methodologies have emerged as pivotal components of image preprocessing,fostering an improvement in the quality of remote sensing imagery.This enhancement renders remote sensing data more indispensable,thereby enhancing the accuracy of target iden-tification.Conventional defogging techniques based on simplistic atmospheric degradation models have proven inadequate for mitigating non-uniform haze within remotely sensed images.In response to this challenge,a novel UNet Residual Attention Network(URA-Net)is proposed.This paradigmatic approach materializes as an end-to-end convolutional neural network distinguished by its utilization of multi-scale dense feature fusion clusters and gated jump connections.The essence of our methodology lies in local feature fusion within dense residual clusters,enabling the extraction of pertinent features from both preceding and current local data,depending on contextual demands.The intelligently orchestrated gated structures facilitate the propagation of these features to the decoder,resulting in superior outcomes in haze removal.Empirical validation through a plethora of experiments substantiates the efficacy of URA-Net,demonstrating its superior performance compared to existing methods when applied to established datasets for remote sensing image defogging.On the RICE-1 dataset,URA-Net achieves a Peak Signal-to-Noise Ratio(PSNR)of 29.07 dB,surpassing the Dark Channel Prior(DCP)by 11.17 dB,the All-in-One Network for Dehazing(AOD)by 7.82 dB,the Optimal Transmission Map and Adaptive Atmospheric Light For Dehazing(OTM-AAL)by 5.37 dB,the Unsupervised Single Image Dehazing(USID)by 8.0 dB,and the Superpixel-based Remote Sensing Image Dehazing(SRD)by 8.5 dB.Particularly noteworthy,on the SateHaze1k dataset,URA-Net attains preeminence in overall performance,yielding defogged images characterized by consistent visual quality.This underscores the contribution of the research to the advancement of remote sensing technology,providing a robust and efficient solution for alleviating the adverse effects of haze on image quality.

Probability-Enhanced Anchor-Free Detector for Remote-Sensing Object Detection

Anchor-free object-detection methods achieve a significant advancement in field of computer vision,particularly in the realm of real-time inferences.However,in remote sensing object detection,anchor-free methods often lack of capability in separating the foreground and background.This paper proposes an anchor-free method named probability-enhanced anchor-free detector(ProEnDet)for remote sensing object detection.First,a weighted bidirectional feature pyramid is used for feature extraction.Second,we introduce probability enhancement to strengthen the classification of the object’s foreground and background.The detector uses the logarithm likelihood as the final score to improve the classification of the foreground and background of the object.ProEnDet is verified using the DIOR and NWPU-VHR-10 datasets.The experiment achieved mean average precisions of 61.4 and 69.0 on the DIOR dataset and NWPU-VHR-10 dataset,respectively.ProEnDet achieves a speed of 32.4 FPS on the DIOR dataset,which satisfies the real-time requirements for remote-sensing object detection.