ZiYuan-3 Research Articles

Co-registration and residual correction of digital elevation models: a comparative study

Abstract. Digital elevation models (DEMs) are currently one of the most widely used data sources in glacier thickness change research, due to the high spatial resolution and continuous coverage. However, raw DEM data are often misaligned with each other, due to georeferencing errors, and a co-registration procedure is required before DEM differencing. In this paper, we present a comparative analysis of the two classical co-registration methods proposed by Nuth and Kääb (2011) and Rosenholm and Torlegard (1988). The former is currently the most commonly used method in glacial studies, while the latter is a seminal work in the photogrammetric field that has not been extensively investigated by the cryosphere community. Furthermore, we also present a new residual correction method using a generalized additive model (GAM) to eliminate the remaining systematic errors in DEM co-registration results. The performance of the two DEM co-registration methods and three residual correction algorithms (the GAM-based method together with two parametric-model-based methods) was evaluated using multiple DEM pairs from the Greenland Ice Sheet and mountain glaciers, including Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEMs, ZiYuan-3 (ZY-3) DEMs, the Shuttle Radar Topography Mission (SRTM) DEM, and the Copernicus DEM. The experimental results confirm our theoretical analysis of the two co-registration methods. The method of Rosenholm and Torlegard has a greater ability to remove DEM misalignments (an average of 4.6 % and 13.7 % for the test datasets from Greenland Ice Sheet and High Mountain Asia, respectively) because it models the translation, scale, and rotation-induced biases, while the method of Nuth and Kääb considers translation only. The proposed GAM-based method performs statistically better than the two residual correction methods based on parametric regression models (high-order polynomials and the sum of the sinusoidal functions). A visual inspection reveals that the GAM-based method, as a non-parametric regression technique, can capture complex systematic errors in the DEM co-registration residuals.

Multispectral versus texture features from ZiYuan-3 for recognizing on deciduous tree species with cloud and SVM models

Tree species recognition accuracy greatly affects forest remote sensing mapping and forestry resource monitoring. The multispectral and texture features of the remote sensing images from the ZiYuan-3 (ZY-3) satellite at two phenological phases of autumn and winter (September 29th and December 7th) were selected for constructing and optimizing sensitive spectral indices and texture indices. Multidimensional cloud model and support vector machine (SVM) model were constructed by the screened spectral and texture indices for remote sensing recognition of Quercus acutissima (Q. acutissima) and Robinia pseudoacacia (R. pseudoacacia) on Mount Tai. The results showed that, the correlation intensities of the constructed spectral indices with tree species were preferable in winter than in autumn. The spectral indices constructed by band 4 showed the superior correlation compared with other bands, both in the autumn and winter time phases. The optimal sensitive texture indices for both phases were mean, homogeneity and contrast for Q. acutissima, and contrast, dissimilarity and second moment for R. pseudoacacia. Spectral features were found to have a higher recognition accuracy than textural features for recognizing on both Q. acutissima and R. pseudoacacia, and winter showing superior recognition accuracy than autumn, especially for Q. acutissima. The recognition accuracy of the multidimensional cloud model (89.98%) does not show a superior advantage over the one-dimensional cloud model (90.57%). The highest recognition accuracy derived from a three-dimensional SVM was 84.86%, which was lower than the cloud model (89.98%) in the same dimension. This study is expected to provide technical support for the precise recognition and forestry management on Mount Tai.

Geomorphological transformations and future deformation estimations of a large potential landslide in the high-order position area of Diexi, China

Landslides in high-order position areas pose a serious threat to residents located below such areas. Therefore, research on the evolution process and underlying dynamic mechanisms is crucial. The majority of relevant studies are based on landslides that have already occurred; however, the investigation of potential landslides is of higher value. In this study, a progressive potential landslide identification method is proposed with the application of SBAS-InSAR (Small Baseline Subset Interferometric Synthetic Aperture Radar) and subsequently combined with Google Earth, GF-1 (Gaofen-1), GF-2 (Gaofen-2), ZY-3 (Ziyuan-3) and UAV (Unmanned Aerial Vehicle) imagery, and DEM (Digital Elevation Model) for further validation. The proposed method is employed to determine the potential landslide in Tuanjiecun by integrating the multi-period data to simultaneously analyze the evolution and mechanism of the potential landslide. The long short-term memory method is then adopted to predict the evolution trend based on accumulative deformation from SBAS-InSAR. The results suggest that the deformation of Tuanjiecun potential landslide will increase to −238.57 mm. This article provides a new approach for disaster prevention and mitigation by determining a potential landslide using composite remote sensing and predicting the development of the potential landslide on indirect contact monitoring.

Geometric positioning integrating optical satellite stereo imagery and a global database of ICESat-2 laser control points: A framework and key technologies

ABSTRACT Block Adjustment (BA) is one of the essential techniques for producing high-precision geospatial 3D data products with optical stereo satellite imagery. For block adjustment with few ground-control points or without ground control, the vertical error of the model is the decisive factor that constrains the accuracy of 3D data products. The elevation data obtained by spaceborne laser altimeter have the advantages of short update periods, high positioning precision, and low acquisition cost, providing sufficient data support for improving the elevation accuracy of stereo models through the combined BA. This paper proposes a geometric positioning model based on the integration of Optical Satellite Stereo Imagery (OSSI) and spaceborne laser altimeter data. Firstly, we elaborate the principle and necessity of this work through a literature review of existing methods. Then, the framework of our geo-positioning models. Secondly, four key technologies of the proposed model are expounded in order, including the acquisition and management of global Laser Control Points, the association of LCPs and OSSI, the block adjustment model combining LCPs with OSSI, and the accuracy estimation and quality control of the combined BA. Next, the combined BA experiment using Ziyuan-3 (ZY-3) OSSI and ICESat-2 laser data was carried out at the testing site in Shandong Province, China. Experimental results prove that our method can automatically select LCPs with high accuracy. The elevation deviation of the combined BA eventually achieved the Mean Error (ME) of 0.06 m and the Root Mean Square Error (RMSE) of 1.18 m, much lower than the ME of 13.20 m and the RMSE of 3.88 m before the block adjustment. A further research direction will be how to perform more adequate accuracy analysis and quality control using massive laser points as checkpoints.

A stable block adjustment method without ground control points using bound constrained optimization

ABSTRACT Block adjustment is a key technology for achieving large-scale accurate mapping of space via remote sensing. When block adjustment without ground control points (GCPs) is conducted, the adjustment model has large condition number of design matrix, and an ill-conditioned normal equation, which eventually leads to abnormal convergence of direct least squares solution. In this study, we investigated block adjustment without GCPs based on Rational Function Model (RFM), and applied image space affine model of RFM as the systematic error compensation model of single scene imagery. The value range of each parameter of the affine model was determined by calculating the inverse projection errors of tie points and the adjustment model parameters were solved by the optimization method with attached inequalities constraint. Finally, the abnormal convergence problem was solved by reducing the degree of freedom of convergence. Comparative tests of block adjustment without GCPs using various methods were conducted using multiple regional imagery from Ziyuan-3 (ZY-3), Tianhui-1 (TH-1), Luojia-1 (LJ-1) and Gaofen-3 (GF-3). The results show that the proposed method can achieve rapid and correct convergence, and has strong practicability.

DETECTION AND CORRECTION OF JITTER EFFECT FOR SATELLITE TDICCD IMAGERY

Abstract. For the on-orbit high resolution satellites, attitude jitter is a common and complex phenomenon which may deteriorate the image quality and mapping accuracy. In this paper, an image-based jitter inversion and estimation model is derived considering time delay integration (TDI) effect, and the attitude is then updated by taking into account the estimated jitter information. The imagery is finally resampled through virtual re-imaging using the updated accurate attitude to improve the image geometric quality. The proposed method is validated by experiments using the Chinese Ziyuan-3 (ZY-3) satellite imagery.

What controls the surging of Karayaylak glacier in eastern Pamir? New insights from remote sensing data

Glacier surges in the Pamir have been widely documented, but the surge mechanism remains poorly known. The Karayaylak Glacier is the first reported surging glacier in eastern Pamir but different dates and possible triggers were published. In this research, the Karayaylak Glacier was studied using an extensive Landsat and Ziyuan-3 (ZY-3) satellite image set. The active phase initiated in May to June 2014, and experienced a peak velocity of 20.1 ± 0.7 m d−1 in May 2015, terminating in October 2015. During 2014–2015, the reservoir zone experienced an average surface elevation reduction of 33.1 ± 1.5 m, and the receiving zone experienced an average surface elevation gain of 36.8 ± 1.5 m. The large basal sliding during the active phase (2014–2015) was likely driven by high water pressure at the bed in an inefficient drainage system. A large volume of water released at the end of the active phase indicated the drainage at the glacier base became more efficient. The abrupt peak velocity, the short active phase and the changes in drainage suggest that the Karayaylak Glacier surge was controlled by hydrology. The behaviour of the Karayaylak Glacier was similar to that of the Kyagar Glacier and Shispare Glacier in the Karakoram.

Geopositioning Improvement of ZY-3 Satellite Imagery Integrating GF-7 Laser Altimetry Data

Improving the accuracy of block adjustment with few or no ground control points (GCPs) is one of the core issues for satellite imagery high-precision mapping. The GaoFen-7 (GF-7) satellite laser altimeter system is the first Chinese formal spaceborne laser altimeter system, which is equipped with laser footprint cameras for the first time. Given the very high height accuracy and high planimetric accuracy of the instrument, this letter proposed an adjustment method of ZiYuan-3 (ZY-3) satellite imagery by integrating GF-7 laser altimetry data. The laser control points (LCPs) were automatically extracted according to the registration of the footprint images and stereo images. Furthermore, the combined adjustment was performed with the LCPs as the vertical control and horizontal constraint. The performance of the proposed method was evaluated by using 417 stereo scenes of ZY-3 images and 954 LCPs, covering an area of 158 000 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> in Shandong, China. The results demonstrated that, only by using all LCPs as the vertical control, the root mean square error (RMSE) of elevation can be significantly improved from the original 8.82 to 1.40 m. Moreover, the planimetric RMSE decreased from the original 15.31 to 3.58 m with residual randomness when 45 LCPs were used as the horizontal constraint. Our method provides an alternative solution for satellite imagery geopositioning improvement without GCPs.

A remote sensing-guided forest inventory concept using multispectral 3D and height information from ZiYuan-3 satellite data

AbstractIncreased frequencies of storms and droughts due to climate change are changing central European forests more rapidly than in previous decades. To monitor these changes, multispectral 3D remote sensing (RS) data can provide relevant information for forest management and inventory. In this case study, data of the multispectral 3D-capable satellite system ZiYuan-3 (ZY-3) were used in a RS-guided forest inventory concept to reduce the field sample size compared to the standard grid inventory. We first pre-stratified the forest area via the ZY-3 dataset into coniferous, broadleaved and mixed forest types using object-based image analysis. Each forest type was then split into three height strata using the ZY-3 stereo module-derived digital canopy height model (CHM). Due to limited sample sizes, we reduced the nine to six strata. Then, for each of the six strata, we randomly selected representative segments for inventory plot placement. We then conducted field inventories in these plots. The collected field data were used to calculate forest attributes, such as tree species composition, timber volume and canopy height at plot level (terrestrially measured tree height and height information from ZY-3 CHM). Subsequently, we compared the resulting forest attributes from the RS-guided inventory with the reference data from a grid inventory based only on field plots. The difference in mean timber volumes to the reference was +30.21 m3ha−1 (8.99 per cent) for the RS-guided inventory with terrestrial height and −11.32 m3ha−1 (−3.37 per cent) with height information from ZY-3 data. The relative efficiency (RE) indicator was used to compare the different sampling schemes. The RE as compared to a random reduction of the sample size was 1.22 for the RS-guided inventory with terrestrial height measurements and 1.85 with height information from ZY-3 data. The results show that the presented workflow based on 3D ZY-3 data is suitable to support forest inventories by reducing the sample size and hence potentially increase the inventory frequency.

Multimodal Data and Multiscale Kernel-Based Multistream CNN for Fine Classification of a Complex Surface-Mined Area

Fine land cover classification (FLCC) of complex landscapes is a popular and challenging task in the remote sensing community. In complex surface-mined areas (CSMAs), researchers have conducted FLCC using traditional machine learning methods and deep learning algorithms. However, convolutional neural network (CNN) algorithms that may be useful for FLCC of CSMAs have not been fully investigated. This study proposes a multimodal remote sensing data and multiscale kernel-based multistream CNN (3M-CNN) model. Experiments based on two ZiYuan-3 (ZY-3) satellite imageries of different times and seasons were conducted in Wuhan, China. The 3M-CNN model had three main features: (1) multimodal data-based multistream CNNs, i.e., using ZY-3 imagery-derived true color, false color, and digital elevation model data to form three CNNs; (2) multisize neighbors, i.e., using different neighbors of optical and topographic data as inputs; and (3) multiscale convolution flows revised from an inception module for optical and topographic data. Results showed that the proposed 3M-CNN model achieved excellent overall accuracies on two different images, and outperformed other comparative models. In particular, the 3M-CNN model yielded obvious better visual performances. In general, the proposed process was beneficial for the FLCC of complex landscape areas.

Stitching and Geometric Modeling Approach Based on Multi-Slice Satellite Images

Time delay and integration (TDI) charge-coupled device (CCD) is an image sensor for capturing images of moving objects at low light levels. This study examines the model construction of stitched TDI CCD original multi-slice images. The traditional approaches, for example, include the image-space-oriented algorithm and the object-space-oriented algorithm. The former indicates concise principles and high efficiency, whereas the panoramic stitching images lack the clear geometric relationships generated from the image-space-oriented algorithm. Similarly, even though the object-space-oriented algorithm generates an image with a clear geometric relationship, it is time-consuming due to the complicated and intensive computational demands. In this study, we developed a multi-slice satellite images stitching and geometric model construction method. The method consists of three major steps. First, the high-precision reference data assist in block adjustment and obtain the original slice image bias-corrected RFM to perform multi-slice image block adjustment. The second process generates the panoramic stitching image by establishing the image coordinate conversion relationship from the panoramic stitching image to the original multi-slice images. The final step is dividing the panoramic stitching image uniformly into image grids and employing the established image coordinate conversion relationship and the original multi-slice image bias-corrected RFM to generate a virtual control grid to construct the panoramic stitching image RFM. To evaluate the performance, we conducted experiments using the Tianhui-1(TH-1) high-resolution image and the Ziyuan-3(ZY-3) triple liner-array image data. The experimental results show that, compared with the object-space-oriented algorithm, the stitching accuracy loss of the generated panoramic stitching image was only 0.2 pixels and that the mean value was 0.799798 pixels, achieving the sub-pixel stitching requirements. Compared with the object-space-oriented algorithm, the RFM positioning difference of the panoramic stitching image was within 0.3 m, which achieves equal positioning accuracy.

Estimating canopy cover in artificial forests using high spatial resolution GF-1 and ZY-3 images: across-sensor and across-site comparison

ABSTRACT Canopy cover is an important parameter in forest ecosystems and has diverse applications in a wide variety of fields. However, estimation of forest canopy cover from high spatial resolution images is an arduous task. To evaluate the utility of high spatial resolution images for estimation of canopy cover of artificial forests, the performance of canopy cover estimation models across sensors and across sites was evaluated. Choosing Wangyedian Forest Farm and Gaofeng Forest Farm as experimental areas, based on Chinese high spatial resolution Gaofen-1 (GF-1) and Ziyuan-3 (ZY-3) satellite images, three models, namely multiple linear regression (MLR), generalized additive model (GAM), and Random Forest (RF) were established. The capabilities of the two data sources and three model types to estimate canopy cover of the forests were compared and the portability of the three models across sensors and sites was analysed. The ranges of the root mean square error (RMSE) and relative root mean square error (rRMSE) of the MLR, GAM, and RF models for each study area established from GF-1 and ZY-3 were 0.0632–0.1205 and 9.98–19.93%, respectively. GF-1 showed better performance than ZY-3 when using the same model type. Whether the objective was across sensors or across sites transplantation, the accuracy of the model decreased with transplantation of the model. The MLR model was non-significant when transplanted across sites and model migration failed, whereas across sensors and across sites transplantation of RF and GAM models was successful. The three evaluation indices of transplanted GAM models were significantly superior to those of transplanted RF and MLR models. Thus, the GAM model showed strong portability and the highest model stability.

Combined Block Adjustment for Optical Satellite Stereo Imagery Assisted by Spaceborne SAR and Laser Altimetry Data

To ensure the accuracy of large-scale optical stereo image bundle block adjustment, it is necessary to provide well-distributed ground control points (GCPs) with high accuracy. However, it is difficult to acquire control points through field measurements outside the country. Considering the high planimetric accuracy of spaceborne synthetic aperture radar (SAR) images and the high elevation accuracy of satellite-based laser altimetry data, this paper proposes an adjustment method that combines both as control sources, which can be independent from GCPs. Firstly, the SAR digital orthophoto map (DOM)-based planar control points (PCPs) acquisition is realized by multimodal matching, then the laser altimetry data are filtered to obtain laser altimetry points (LAPs), and finally the optical stereo images’ combined adjustment is conducted. The experimental results of Ziyuan-3 (ZY-3) images prove that this method can achieve an accuracy of 7 m in plane and 3 m in elevation after adjustment without relying on GCPs, which lays the technical foundation for a global-scale satellite image process.

A Spliced Satellite Optical Camera Geometric Calibration Method Based on Inter-Chip Geometry Constraints

When in orbit, spliced satellite optical cameras are affected by various factors that degrade the actual image stitching precision and the accuracy of their data products. This is a major bottleneck in the current remote sensing technology. Previous geometric calibration research has mostly focused on stitched satellite images and has largely ignored the inter-chip relationship among original multi-chip images, resulting in accuracy loss in geometric calibration and subsequent image products. Therefore, in this paper, a novel geometric calibration method is proposed for spliced satellite optical cameras. The integral geometric calibration model was developed on inter-chip geometry constraints among multi-chip images, including the corresponding external and internal calibration models. The proposed approach improves uncontrolled geopositioning accuracy and enhances mosaic precision at the same time. For evaluation, images from the optical butting satellite ZiYuan-3 (ZY-3) and mechanical interleaving satellite Tianhui-1 (TH-1) were used for the experiments. Multiple sets of satellite data of the Songshan Calibration field and other regions were used to evaluate the reliability, stability, and applicability of the calibration parameters. The experiment results found that the proposed method obtains reliable camera alignment angles and interior calibration parameters and generates high-precision seamless mosaic images. The calibration scheme is not only suitable for mechanical interleaving cameras with large geometric displacement among multi-chip images but is also effective for optical butting cameras with minor chip offset. It also significantly improves uncontrolled geopositioning accuracy for both types of spliced satellite images. Moreover, the proposed calibration procedure results in multi-chip satellite images being seamlessly stitched together and mosaic errors within one pixel.

Mapping Panax Notoginseng Plantations by Using an Integrated Pixel- and Object-Based (IPOB) Approach and ZY-3 Imagery

Plantations of Panax notoginseng (PN), traditional herbal medicine for the prevention and treatment of vascular diseases, are expanding rapidly in China, especially in the Yunnan province of China, due to its increasing demands and prices and causing dramatic environmental concerns. However, existing information on its planting area and spatial distribution are limited. Here, we mapped the PN planting area by using a new integrated pixel- and object-based (IPOB) approach, the Random Forest (RF) classifier, and the high-resolution ZiYuan-3 (ZY-3) imagery. We improved the procedures of classification in three aspects: (1) a new spectral index—Normalized Difference PN Index (NDPI)—was proposed, (2) the efficiency and scale of segmentation were optimized by using the Bi-level Scale-sets Model (BSM), and (3) feature variables were selected through an iteration analysis from 99 feature variables (spectral, textural, geometric, and geographic). Compared with the pixel- and the object-based methods, the IPOB has the highest F1 score of 0.98 and also has high robustness in terms of user and producer accuracies (97% and 99%, respectively), following by the object-based method (F1 = 0.94) and the pixel-based method (F1 = 0.93). The high accuracy was expected since the target class has very distinctive spectral and textural characteristics. Although all three approaches showed reasonably high accuracies due to the application of the NDPI and optimized procedures, the result showed the outperformance of the proposed IPOB approach. The framework established in this study expects to apply for regional or national PN surveys extensively. The information on the area and spatial distribution of PN can guide the government on policy making for the planting and exporting of traditional Chinese medicine resources.

Characterizing the Topographic Changes and Land Subsidence Associated with the Mountain Excavation and City Construction on the Chinese Loess Plateau

A mega project, Mountain Excavation and City Construction (MECC), was launched in the hilly and gully region of the Chinese Loess Plateau in 2012, in order to address the shortage of available land and create new flat land for urban construction. However, large-scale land creation and urban expansion significantly alters the local geological environment, leading to severe ground deformation. This study investigated the topographic changes, ground deformation, and their interactions due to the MECC project in the Yan’an New District (YND). First, new surface elevations were generated using ZiYuan-3 (ZY-3) stereo images acquired after the construction in order to map the local topographic changes and the fill thickness associated with the MECC project. Then, the interferometric synthetic aperture radar (InSAR) time series and 32 Sentinel-1A images were used to assess the spatial patterns of the ground deformation in the YND during the postconstruction period (2017–2018). By combining the InSAR-derived results and topographic change features, the relationship between the ground deformation and large-scale land creation was further analyzed. The results indicated that the MECC project in the YND has created over 22 km2 of flat land, including 10.8 km2 of filled area, with a maximum fill thickness of ~110 m. Significant uneven ground deformation was detected in the land-creation area, with a maximum subsidence rate of approximately 121 mm/year, which was consistent with the field survey. The strong correlation between the observed subsidence patterns and the land creation project suggested that this recorded uneven subsidence was primarily related to the spatial distribution of the filling works, along with the changes in the thickness and geotechnical properties of the filled loess; moreover, rapid urbanization, such as road construction, can accelerate the subsidence process. These findings can guide improvements in urban planning and the mitigation of geohazards in regions experiencing large-scale land construction.

Evaluating the Vertical Accuracy of DEM Generated from ZiYuan-3 Stereo Images in Understanding the Tectonic Morphology of the Qianhe Basin, China

Currently available high-resolution digital elevation model (DEM) is not particularly useful to geologists for understanding the long-term changes in fluvial landforms induced by tectonic uplift, although DEMs that are generated from satellite stereo images such as the ZiYuan-3 (ZY3) satellite include characteristics with significant coverage and rapid acquisition. Since an ongoing analysis of fluvial systems is lacking, the ZY3 DEM was generated from block adjustment to describe the mountainous area of the Qianhe Basin that have been induced by tectonic uplift. Moreover, we evaluated the overall elevation difference in ZY3 DEM, Shuttle Radar Topography Mission (1″ × 1″) (SRTM1), and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) by using the Ice Cloud and Land Elevation Satellite/Geoscience Laser Altimeter (ICESat/GLAH14) point cloud and a DEM of 1:50,000 scale. The values of the root mean square error (RMSE) of the elevation difference for ZY3 DEM were 9.31 and 9.71 m, respectively, and are in good agreement with SRTM1. The river long profiles and terrace heights were also extracted to compare the differences in channel steepness and the incision rates with SRTM1 and ASTER GDEM. Our results prove that ZY3 DEM would be a good alternative to SRTM1 in achieving the 1:50,000 scale for DEM products in China, while ASTER GDEM is unsuitable for extracting river longitudinal profiles. In addition, the northern and southern river incision rates were estimated using the ages and heights of river terraces, demonstrating a range from 0.12–0.45 to 0.10–0.33 m/kyr, respectively. Collectively, these findings suggest that ZY3 DEM is capable of estimating tectonic geomorphological features and has the potential for analyzing the continuous evolutionary response of a landscape to changes in climate and tectonics.

Comparison of multi-source satellite images for classifying marsh vegetation using DeepLabV3 Plus deep learning algorithm

The accurate classification of wetland vegetation is essential for rapid assessment and management. The Honghe National Nature Reserve (HNNR), located in Northeast China, was studied. The multi-scale remote sensing data of a new generation of Chinese high-spatial-resolution earth observation satellites Gaofen-1 (GF-1), Gaofen-2 (GF-2), Ziyuan-3 (ZY-3), and international earth observation satellites Sentinel-2A and Landsat 8 OLI were selected as sources. Based on the DeepLabV3 Plus deep learning model, 12 intelligent marsh vegetation classification models were constructed. We quantitatively analyzed the applicability and identification ability of DeepLabV3 Plus for classifying complex marsh vegetation. We discuss the differences in accuracy of marsh vegetation classification with different remote sensing data sets. The spatial resolution of remote sensing data sets ranges from 30 m to 0.8 m, and spectral bands range from blue bands (450 nm) to shortwave infrared bands (2280 nm). The specific conclusions of this study are as follows: (1) The DeepLabV3 Plus model better identified marsh vegetation, but there were significant differences in the classification accuracy of 12 DeepLabV3 Plus intelligent identification models. (2) Under the same conditions of the spectral bands (four Blue ~ NIR bands), the accuracy of deep-water marsh vegetation classification gradually increased as spatial resolution improved. For shallow-water marsh vegetation, when the accuracy of vegetation classification increased to a certain level, the classification accuracy decreased with the improvement of spatial resolution, which indicated that high-resolution images reduced pixel mixing to a certain extent, but for some vegetation types, the internal spectral difference increased, which made classification more difficult. (3) The increase of spectral bands improved the classification of marsh vegetation, while the classification accuracy of models with spectral indices was better than that of models only including spectral bands. (4) The accuracy of marsh vegetation classification was greatly improved by combining spectral bands and spectral indices. (5) The classification of the five sensor satellite images had statistical differences between models with different spatial resolutions and models with different spectral ranges.

Iterative detail-preserving thin-cloud removal method for panchromatic remote sensing images

Optical remote sensing images are frequently affected by clouds, haze, and mist in the atmosphere. We introduce an iterative minimization light-cloud removal method designed for the specific quality improvement needs of military reconnaissance panchromatic remote sensing images. The proposed method is required to fulfill the military reconnaissance demands for improvements in the quality of panchromatic high-resolution images while guaranteeing high fidelity between the restored and observed images. A heuristic approach based on contrast enhancement is proposed to solve the thin-cloud removal problem. We design the target function of a minimization algorithm that contains a fidelity term, a contrast penalty term, and an information loss penalty term. By minimizing the target function with the iterative steepest descent method, a high-quality image can be restored from the observed satellite cloudy image, and the details are preserved by the penalty terms. The application of our iterative method to Gaofen-1 (GF-1) and Ziyuan-3 (ZY-3) satellite data shows that the iterative method was applicable to GF-1 and ZY-3 satellite and the data showed that for panchromatic remote sensing images, the proposed method could reduce satellite image degradation caused by haze and thin clouds while preserving the details in the observed images.

Combined Geometric Positioning and Performance Analysis of Multi-Resolution Optical Imageries from Satellite and Aerial Platforms Based on Weighted RFM Bundle Adjustment

Combined geometric positioning using images with different resolutions and imaging sensors is being increasingly widely utilized in practical engineering applications. In this work, we attempt to perform the combined geometric positioning and performance analysis of multi-resolution optical images from satellite and aerial platforms based on weighted rational function model (RFM) bundle adjustment without using ground control points (GCPs). Firstly, we introduced an integrated image matching method combining least squares and phase correlation. Next, for bundle adjustment, a combined model of the geometric positioning based on weighted RFM bundle adjustment was derived, and a method for weight determination was given to make the weights of all image points variable. Finally, we conducted experiments using a case study in Shanghai with ZiYuan-3 (ZY-3) satellite imagery, GeoEye-1 satellite imagery, and Digital Mapping Camera (DMC) aerial imagery to validate the effectiveness of the proposed weighted method, and to investigate the positioning accuracy by using different combination scenarios of multi-resolution heterogeneous images. The experimental results indicate that the proposed weighted method is effective, and the positioning accuracy of different combination scenarios can give a good reference for the combined geometric positioning of multi-stereo heterogeneous images in future practical engineering applications.