Typical Ground Objects Research Articles

Moderate Red-Edge vegetation index for High-Resolution multispectral remote sensing images in urban areas

Urban green space (UGS) monitoring is significant in optimizing urban planning, protecting the ecological environment, and improving residents’ quality of life. However, in urban environments, shadow interference and the emergence of new construction materials pose challenges to monitoring green vegetation in high-resolution imagery. This study found that existing vegetation indices (VIs), such as normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), perform inadequately in extracting vegetation in urban areas, resulting in significant omissions and errors. By conducting in-depth analysis and quantitative experiments on the reflectance of typical urban ground objects, this study developed a new vegetation extraction method, the moderate red-edge vegetation index (MREVI), to enhance the extraction accuracy of UGS vegetation from unmanned aerial vehicle (UAV) high-resolution multispectral remote sensing (RS) images. Experimental results demonstrate that MREVI performs exceptionally well in complex urban environments, significantly suppressing non-vegetation areas, achieving an overall accuracy (OA) of 98.6% and a Kappa coefficient of 0.97. This study supports for urban planning, UGS monitoring, and the evaluation of urban plant carbon sequestration capacity.

A new method to extract coal-covered area in open-pit mine based on remote sensing

ABSTRACT Coal is a prominent energy resource in China. Therefore, it is crucial to accurately extract the coal-covered areas. Taking Xinjiang’s Zhundong mining region as the study area, Landsat 8 OLI data were employed to assess the spectral curves of four typical ground objects, including vegetation, bare land, water body and bare coal. The Normalized Difference Bare Coal Index (NDBCI) and the Normalized Difference Coal Index (NDCI) were developed to extract coal-covered area. Higher-resolution Sentinel-2B data for the same period were used for verification, with extraction accuracy evaluated by five metrics including Kappa coefficient, Overall accuracy, Checking accuracy, Checking completeness and F1-score. The results of extracting coal-covered areas showed that (1) the NDBCI showed ‘internal fragmentation’ and the NDCI demonstrated ‘pixel overflow’ during the extraction process. Therefore, we determined the optimal thresholds −0.03 for NDBCI and 0.04 for NDCI. (2) NDBCI distinguished pixels with lower grey-scale values, such as water body, road and gangue. However, some dump zones and shed patches were misclassified. (3) NDCI generated clear boundaries and more complete interiors, and the dump zone and shed could be distinguished. However, some water body parts were misclassified as coal-covered areas. (4) Combined application of NDBCI and NDCI generated a ‘complementary’ effect better than both individual modes. Kappa coefficient, Overall accuracy and F1-score reached 0.95, 98.76% and 0.75, respectively. This study successfully extracted coal-covered areas by developing a remote sensing index based on spectral traits and a priori knowledge of the study area. The proposed combined extraction mode achieved high accuracy for rapid and reliable identification of coal-covered areas.

Infrared remote-sensing image super-resolution based on physical characteristic deduction

In this paper, we propose a high-confidence, super-resolution method for short-wave infrared remote-sensing images based on characteristic deduction form high-resolution visible images. First, based on the radiative transmission mechanism of full remote-sensing imaging link, a ground object characterization model based on the dark object method was established to achieve the inversion of the object’s ground reflection characteristics of high-resolution visible images. Second, the reflectance information of the specified infrared band can be deduced combined with the reflectance matching of the typical ground object spectral library. On this basis, combined with the characterization modeling of remote-sensing imaging link—such as atmosphere, optical system, platform, and detector—a high-resolution infrared remote-sensing image reconstruction method is proposed. Based on the imaging degradation model between high- and low-resolution infrared images, the results of high-resolution reconstruction were corrected using measured infrared images. Lastly, the final fusion-super-resolution image can be obtained. The results show that the proposed fusion-super-resolution method yields high-resolution infrared images more realistically than traditional image fusion algorithms and shows better structural similarity index and visual information fidelity results, verifying the effectiveness of the proposed infrared remote-sensing image fusion-super-resolution method.

Typical Ground Object Recognition in Desert Areas Based on DYDCNet: A Case Study in the Circum-Tarim Region, Xinjiang, China

Typical Ground Object Recognition in Desert Areas Based on DYDCNet: A Case Study in the Circum-Tarim Region, Xinjiang, China

A Novel Remote Sensing Image Enhancement Method, the Pseudo-Tasseled Cap Transformation: Taking Buildings and Roads in GF-2 as an Example

With the improvements in sensor accuracy, the spectral features of high-resolution remote sensing images become more complex. As a result, the classification accuracy for land cover classification decreases. Remote sensing image enhancements can improve the visual effect and the intra-class consistency and enhance the characteristics of ground objects. These enhancements are important for both image interpretation and improving image segmentation accuracy. In this study, we propose a pseudo-tasseled cap transformation (pseudo-TCT) through an orthogonal linear transformation of Gaofen-2 (GF-2) images using the untransposed tasseled cap transformation (TCT) coefficients, and further, enhance the visual effect and the separability among ground objects by linear stretching and percentage truncation stretching. To examine the separability among ground objects in the pseudo-TCT image, we used K-Means clustering, ISODATA clustering and 3D visualization of the spectral features of typical ground objects. The results show that the separability of buildings and roads from background objects is better than in the original image and the TCT image, and typical ground objects are effectively distinguished. Additionally, we visualized intra-class consistency by calculating the mean Euclidean distance between the pixel values of each point and the pixel values of its eight neighboring points and calculated the standard deviation of the intra-class consistency images. The results indicate that the secondary textures of the objects were weakened, and edges were made clearer, enhancing intra-class consistency. The pseudo-TCT is effective, at least in our work, and could be a candidate for image enhancement under certain applications.

Spectral characteristics analysis of typical ground objects in Poyang Lake wetland based on “Zhuhai-1” hyperspectral data

Hyperspectral image data provides new ideas and methods for the fine classification of wetland. Research on wetland spectral characteristics lays the foundation for wetland classification and improves classification accuracy. Based on “Zhuhai-1” hyperspectral image data, combined with the Pure Pixel Index (PPI), this paper extracted the average spectral curves of 11 typical ground objects in Poyang Lake wetland. Two mathematical transformation methods, namely, the continuum removal and the first derivative, were used to analyze the spectral characteristics of the ground objects before and after transformation, in combination with spectral characteristic parameters. The method of combining the mean distance of ground objects with the range of data error was used to select characteristic wavelengths before and after spectral transformation. Finally, the Mahalanobis distance method was used to verify the recognition effect. The results demonstrate that: (1) Spectral transformation can effectively amplify the differences between ground objects. The continuum removal led to an increase in the characteristic bands of four types of vegetation and the emergence of characteristic bands of four types of other ground objects. The first derivative method extended the characteristic bands of four vegetation from red-edge to near-infrared. (2) The method of combining the mean distance and the data error range can effectively screened out the spectral characteristic bands of typical ground objects in Poyang Lake wetland, resulting in hyperspectral dimensionality reduction. (3) The Mahalanobis distance method validates the recognition effect of the characteristic bands. In the screened characteristic bands, the Mahalanobis distance between different ground objects is larger than that between the same ground object, except between shoaly land and road. This study offers valuable insights into spectral recognition and classification of freshwater lake wetlands, and also demonstrates the potential of “Zhuhai-1” hyperspectral image data in wetland research.

Composite extraction index to enhance impervious surface information in remotely sensed imagery

Fast and accurate impervious surface extraction is important to monitor urban dynamics and ensure people's well-being. In order to improve the extraction accuracy of impervious surface, a composite impervious surface extraction index CBI based on NDBI, NDVI and MNDWI is proposed in the paper. The impervious surface extraction images in different land cover areas were compared with the NDBI and NDISI, respectively, and were assessed by using visual and statistical analysis. The results show that CBI has good robustness, less affected by the type of ground objects and sensors. The Kappa coefficients of CBI are the highest in different sensors and OLI images with different land cover areas, and the overall accuracy is above 91%. In the area with high coverage of vegetation and water, the extraction accuracy of CBI was significantly improved. CBI is more suitable for areas with high coverage of vegetation and water and has an improved Kappa coefficient of 0.1116 than that of the areas containing only a small amount of water and vegetation. As the NDISI algorithm uses the thermal infrared band, when the area of the study area is small, its extraction accuracy is low, indicating that the accuracy of the algorithm is affected by the size of the study area.

Multi-temporal Cloud Pixels Reconstruction Method for Optical Remote Sensing Satellite Images

Bachground: The existence of cloud pixels reduces the practicability of optical satellite remote sensing data. Existing problems: Existing cloud reconstruction methods generally cannot solve the following problems:(1)Large-scale thick cloud cannot be well reconstructed. (2)There are high requirements for reconstructed data. (3)Most data used to reconstructed are single temporal images. Ways: In order to overcome these problems, a new multi temporal weighted aggregation method is proposed. Specifically, we adopt a multi-temporal iterative aggregation method for cloud pixels to reconstruct and a multi-temporal weighted aggregation method for cloud shadow pixels to reconstruct. Result: Finally, the experiment proves that our method can quickly and accurately complete the cloud reconstruction, and under the effective uniform color strategy, a cloud- free image with accurate geometric position and uniform gray scale can be obtained. Conclusion: Experiments prove that the pixel reconstruction method proposed in this paper has achieved good cloud and cloud shadow pixel reconstruction effects in different types of ground objects.

Vegetation Coverage Monitoring Model Design Based on Deep Learning

Based on the medium-resolution Landsat TM and OLI satellite images in the study area, the deep learning ENVINet-5 model is adopted for vegetation coverage monitoring. By referring to the fusion image and Google Earth high-resolution satellite image, the training samples and verification samples are manually labeled, and the labels of four types of ground objects (desert, water body, cultivated land, and construction land) are made. Through the ENVI deep learning binary classification model, the labeled training samples are trained, and a large number of samples of desert, water, and cultivated land are extracted and transformed into corresponding label images. Then, a large number of training sample labels extracted from the model are combined with the manually made construction land sample labels and both of them are used as the training samples of the ENVI deep learning multiclassification model. According to the classification process of the deep learning model (creating label image, initializing training model, and training model and model classification), through the adjustment of various parameters, the four types of ground objects in the study area are finally classified. Finally, the classification results that meet the accuracy requirements are statistically analyzed. It is proved that the model classification results can meet the use requirements.

A Multilevel Point Cloud Classification Method for Underground Tunnels Based on Three-Dimensional Moving LiDAR Measurements

Underground tunnel engineering requires complex systematic engineering. A tunnel requires internal measurements after the completion of shield construction to check the real construction quality of the tunnel and provide measurement data for the next tunnel project acceptance team. When measuring a tunnel’s internal construction and performing associated data analysis, it is necessary to accurately count the size and type information of the built tunnel internal structure. In this study, mobile three-dimensional laser scanning technology is used to collect a tunnel’s internal point cloud data, and many unordered point cloud data are collected. Thus, classifying the ground objects inside the tunnel automatically and accurately is a critical problem to be solved in a tunnel construction survey. Additionally, this study proposes a multilayer underground tunnel point cloud classification method that uses the hierarchical clustering structure to deal with the original tunnel point cloud. This method extracts the specific ground objects, such as tracks or roads, platforms, and pipelines, on the tunnel surface and inside the tunnel step by step. Concurrently, the accuracy of the projection plane and the accuracy of point cloud classification are introduced to evaluate the accuracy and finally calculate the statistics of ground object information in the tunnel. To verify the engineering practicability of this method, we first collected the point cloud data of a railway tunnel inside the tunnel using a rail car equipped with high-precision LiDAR and divided the data results into four sample areas for the classification test. To verify the algorithm’s robustness, we use the proposed method to test the highway tunnel data according to the same experimental process. Experiments show that this paper’s multilevel tunnel point cloud classification method can accurately extract these four types of ground objects. The average accuracy of the projection plane in each experimental area is not less than 91.49%, and the average accuracy of point cloud classification is not less than 92.63%. Compared with the other three types of classification methods in the same field, the method in this paper is more suitable for processing tunnel point cloud data and has the advantages of high classification accuracy, strong robustness, and a simple implementation process. The proposed method can also meet the real needs of underground tunnel internal construction surveys.

Surface deformation extraction from small baseline subset synthetic aperture radar interferometry (SBAS-InSAR) using coherence-optimized baseline combinations

ABSTRACT Surface deformation data can be used to provide early warnings of geohazards and are useful in a variety of research fields. The Small BAseline Subset InSAR (SBAS-InSAR) boosts the data sampling rate and improves the accuracy of deformation extraction by restricting the temporal and spatial baselines. However, various factors, such as the types of ground objects and seasons, affect the coherence between SAR images. Traditional SBAS-InSAR employs a fixed temporal baseline, which does not guarantee good coherence and might lead to decorrelation. In this paper, we propose that instead of using a fixed temporal baseline, we directly use the average coherence between SAR images as the baseline constraint index to perform an optimized selection of SBAS-InSAR interferometric pairs, ensuring good coherence of the interferometric pairs and improving the quality of the interferometric fringes. The proposed approach was used to extract surface deformation in two test experiment areas: Houston and Sydney. Compared with the conventional SBAS-InSAR and GPS data, the standard deviation of error of Houston and Sydney dropped from 0.813 to 0.589 and 0.291 to 0.246, respectively; the root mean square error (RMSE) decreased from 1.082 to 1.041 and 0.485 to 0.334, respectively, indicating that the proposed method has better surface deformation extraction accuracy. After demonstrating the accuracy of the proposed method, it was applied to Pingxiang area, a mining city in China, to effectively extract and analyze the surface deformation induced by mining activities, which proves the universality of this method in different scenarios.

Spectral Variation Augmented Representation for Hyperspectral Imagery Classification With Few Labeled Samples

Due to variation of imaging conditions, spectra of the same type of ground objects usually exhibit certain discrepancy, leading to intra-class spectral distance increase and inter-class distance decrease. As a result, classification accuracy is greatly affected, especially in cases with few labeled samples. For representation based classifiers, the spectral variability within limited training samples is far from sufficient to represent diverse variations within testing ones. To handle this problem, a spectral variation augmented representation for hyperspectral imagery classification (SVARC) with few labeled samples is proposed in this article. Firstly, a novel class-independent and class-dependent components based linear representation model (CICD-LRM) is proposed to emphasize the representation of spectral variation. Secondly, depending on spatial and spectral correlation, the CICD-LRM guided global and local spectral variation extraction schemes are designed, and a fused spectral variation dictionary is constructed by concatenation. Finally, a classifier for hyperspectral images based on the CICD-LRM and spectral variation dictionary is proposed, and specifically three different spectral variation reconstruction strategies are designed. Similar to most of the representation based classifiers, residual-driven decision is also employed in the proposed classifier. Comparative experiments are conducted with eight classical and state-of-the-art methods using two benchmark datasets. The experimental results demonstrate that the proposed SVARC method significantly outperforms the compared ones in cases with few labeled samples.

A Locally Optimized Model for Hyperspectral and Multispectral Images Fusion

The maintenance of spectral variability between subclass objects and the relationship between hyperspectral (HS) bands have been a fundamental but challenging problem for fusing low spatial resolution (LR) HS and high spatial resolution (HR) multispectral (MS) images. This article presents a locally optimized image segmentation fusion (LOISF) framework for HS super-resolution reconstruction. First, LR HS and HR MS are clustered and segmented, and the label attributes of the segmented objects are identified by the prior information. Then, a novel joint fusion model for different typical ground objects is constructed based on spectral unmixing. The fusion problem is formulated mathematically as a convex optimization of a Frobenius norm, which includes spatial, spectral, and index constraints, with an alternating-directions’ optimization featuring linearization providing the solution. Experimental results demonstrate that the proposed LOISF preserves both spatial details and texture, achieving high spectral fidelity, and yielding significantly improved image quality compared to other state-of-the-art fusion methods.

An Airborne C-Band One-Dimensional Microwave Interferometric Radiometer With Ocean Aviation Experimental Results

The medium- and large- scale global sea surface temperature (SSTs), which is an important ocean parameter, are mainly measured by the space-borne microwave radiometry. However, the resolution and sensitivity are greatly limited by antenna size, orbit altitude, and flight speed. In this paper, an airborne C-band one-dimensional (1-D) microwave interferometric radiometer (ACMIR) is developed to obtain the SSTs with a high resolution and sensitivity. The spatial resolution of the ACMIR ranges from 40.7m to 612m, corresponding to the flight height of 500m to 8000m, while the sensitivity varies from 0.25K to 0.36K, associating with the boresight to the edge of the field of view. In order to evaluate the performance of the ACMIR, an ocean aviation experiment was conducted in the coastal area of the Yellow Sea in September 2020. The land observation results indicate that typical ground objects can be clearly distinguished. Furthermore, thesea surface brightness temperatures acquired by the ACMIR are compared with the estimates based on the radiative transfer model, with SST retrieval error of 0.77°C. The ACMIR can offer a high-resolution and accuracy of SST especially in coastal areas, and can meet several application requirements related to SSTs.

Polarized multispectral image classification of typical ground objects based on neural network (Invited)

Polarized multispectral image classification of typical ground objects based on neural network (Invited)

Graph Attention Feature Fusion Network for ALS Point Cloud Classification.

Classification is a fundamental task for airborne laser scanning (ALS) point cloud processing and applications. This task is challenging due to outdoor scenes with high complexity and point clouds with irregular distribution. Many existing methods based on deep learning techniques have drawbacks, such as complex pre/post-processing steps, an expensive sampling cost, and a limited receptive field size. In this paper, we propose a graph attention feature fusion network (GAFFNet) that can achieve a satisfactory classification performance by capturing wider contextual information of the ALS point cloud. Based on the graph attention mechanism, we first design a neighborhood feature fusion unit and an extended neighborhood feature fusion block, which effectively increases the receptive field for each point. On this basis, we further design a neural network based on encoder–decoder architecture to obtain the semantic features of point clouds at different levels, allowing us to achieve a more accurate classification. We evaluate the performance of our method on a publicly available ALS point cloud dataset provided by the International Society for Photogrammetry and Remote Sensing (ISPRS). The experimental results show that our method can effectively distinguish nine types of ground objects. We achieve more satisfactory results on different evaluation metrics when compared with the results obtained via other approaches.

Land-cover classification of high-resolution remote sensing image based on multi-classifier fusion and the improved Dempster–Shafer evidence theory

The use of the single machine learning classifier for high-resolution remote sensing (RS) image classification makes it difficult to improve the accuracy of classification results. To fully utilize the advantages of different classifiers for different types of ground objects, based on the Dempster–Shafer (DS) evidence theory, we propose a multi-classifier fusion method for classification of high-resolution RS images. Six machine learning classifiers: support vector machines, k-nearest neighbor, random forest, artificial neural network, classification and regression tree, and the C5.0 decision tree were selected for application in the fusion of multiple classifiers. We calculated a classifier difference index based on the accuracy and difference of the classification results of the base classifiers. Base classifiers with large differences were selected to perform integration based on the DS evidence theory. We also improved the classical DS evidence theory. First, based on the classification validity of the base classifier for different ground objects, the classification probability value of the base classifier for different samples was weight optimized. Then different fusion methods were selected according to the classification conflict coefficients between base classifiers. The results reveal that the overall accuracy and kappa coefficients of the fusion classifier are significantly better than those of the base classifier. The producer’s accuracy and user’s accuracy of the fusion results based on the improved DS evidence theory were higher than those of the fusion results based on the classical DS evidence theory.

Landscape Patterns and Building Functions for Urban Land-Use Classification from Remote Sensing Images at the Block Level: A Case Study of Wuchang District, Wuhan, China

Landscape patterns and building functions are successfully used to provide the social sensing information of urban areas. However, previous studies treated ground objects equally, ignoring their size differences. Considering the different contributions of various types of ground objects in land-use classification, this paper measured nine area-weighted mean landscape-level metrics to describe landscape patterns based on the land-cover map, derived from remote sensing images. Additionally, the same idea was applied for identifying building functions. Impervious surfaces, which occupy the majority of urban areas, have a decisive impact on land-use classes. In terms of this, this paper proposed the impervious surface area-weighted building-based indexes from the building outline data. To better represent the physical structure of urban areas, the entire study was based on the analysis units delineated by the OpenStreetMap road network. Finally, a random forest model combining the landscape-level metrics and building-based indexes was adopted in Wuchang District of Wuhan city, China. The results showed that the proposed method was effective at describing landscape patterns and identifying building functions for accurate urban land-use classification, increasing the precision by 10.67%. In general, the contribution of landscape-level metrics to the urban land-use classification is slightly greater than that of building-based indexes. Moreover, different land-use types of analysis units express different landscape patterns. It is of great significance for improving urban form and guiding future urban design. The paper demonstrates that area-weighted landscape metrics and building-based indexes offer a better understanding of urban land use, which plays a vital role in urban planning, construction, and management.

An Optimized Faster R-CNN Method Based on DRNet and RoI Align for Building Detection in Remote Sensing Images

In recent years, the increase of satellites and UAV (unmanned aerial vehicles) has multiplied the amount of remote sensing data available to people, but only a small part of the remote sensing data has been properly used; problems such as land planning, disaster management and resource monitoring still need to be solved. Buildings in remote sensing images have obvious positioning characteristics; thus, the detection of buildings can not only help the mapping and automatic updating of geographic information systems but also have guiding significance for the detection of other types of ground objects in remote sensing images. Aiming at the deficiency of traditional building remote sensing detection, an improved Faster R-CNN (region-based Convolutional Neural Network) algorithm was proposed in this paper, which adopts DRNet (Dense Residual Network) and RoI (Region of Interest) Align to utilize texture information and to solve the region mismatch problems. The experimental results showed that this method could reach 82.1% mAP (mean average precision) for the detection of landmark buildings, and the prediction box of building coordinates was relatively accurate, which improves the building detection results. Moreover, the recognition of buildings in a complex environment was also excellent.

A RADIOMETRIC NORMALIZATION METHOD OF CONTROLLING NO-CHANGED SET (CNCS) FOR DIVERSE LANDCOVER USING MULTI-SENSOR DATA

Abstract. The accurate acquisition of land surface reflectance (SR) data determines the accuracy of ground objects recognition, classification and land surface parameter inversion using remote sensing data, which is the basis of remote sensing data application. In this study, a Control No-Changed Set (CNCS) radiometric normalization method is proposed to realize spectral information transformation of multi-sensor data, which is based on the Iteratively Reweighted Multivariate Alteration Detection (IR-MAD), and includes automatic selection and step-by-step optimization of no-change pixels. The No-Changed set (NC) is obtained by selecting the original no-change pixels between the target image and the reference image according to the linear relationship. In the obtained original no-change regions, IR-MAD rules with iterative control are used to fix the final no-change pixels, after regression modeling and calculation, the normalized images are obtained. The method is tested on multi-images from multi-sensors in three groups of experiments (GF-1 WFV and Landsat-8 OLI, GF-1 PMS and Sentinel-2 MSI, and Landsat-8 OLI and Sentinel-2 MSI) with different landcover areas. The results of radiometric normalization are evaluated qualitatively and quantitatively. The data of the three groups of experiments have a high correlation (correlation coefficient r values > 0.85), indicating that they can be used together as complementary data. The Root Mean Squared Error (RMSE) values calculate from the NC between the reference and normalized target images are much smaller than those between the reference and original target images. The radiometric colour composition effects, and the typical ground objects spectral reflective curves of the reference and normalized target images are very similar after radiometric normalization. These results indicate that the CNCS method considers the linear relationship of the no-change pixels and is effective, stable, and can be used to improve the consistency of SR of multi-images from multi-sensors.