ZY-3 Satellite Research Articles

Qinghai-Tibet Plateau Glacier-DEM product derived from the Chinese stereo mapping satellites

Abstract. The Digital Elevation Model (DEM) is an important basic data for glacier research on the Qinghai-Tibet Plateau. With the rapid development of China 's stereo mapping satellite, it is possible to obtain high-precision DEM in the glacier area of the Qinghai-Tibet Plateau. In this study, the stereo images and laser altimetry data of ZY-3 and GF-7 satellites were comprehensively used, and the matching and joint adjustment methods of laser elevation control points and stereo images were proposed. A high-precision DEM product in glacier area was established, and the elevation accuracy of DEM was verified by using UAV data and ICESat-2 data. The results show that the accuracy of GF-7 DEM is better than 1.5 m when the slope is less than 6 degrees, and the overall accuracy is better than 2.8 m. The accuracy of ZY-3 DEM is better than 3.5 m when the slope is less than 6 degrees, and the overall accuracy is better than 5.0 m. Compared with the open source DEM with medium spatial resolution, the DEM elevation accuracy based on domestic stereo mapping satellite images is better, and the grid is finer and more detailed to describe the texture characteristics of the end of the glaciers. Compared with the high spatial resolution data set HMA DEM, the ZY-3 DEM is slightly worse and the GF-7 DEM is better, and both the ZY-3 DEM and GF-7 DEM are better than the HMA DEM in terms of coverage integrity in the Qinghai- Tibet Plateau. Chinese stereo mapping satellites can generate high-precision DEM in the glacier area, and can provide more accurate and reliable terrain reference data than the existing open free data for the study of glaciers in the Qinghai-Tibet Plateau.

Mapping urban green space of the 36 major cities in China using ZY-3 satellite images based on a feature robust to low-brightness pixels

Abstract. Urban green spaces (UGS), integral to modern urban life, play a pivotal role in shaping ecological, low-carbon, resilient, and livable cities. Understanding the microstructure and macro patterns of UGS is necessary but limited. In this context, we investigated the spatial patterns in the 36 major cities in China using Ziyuan-3 remote sensing images. We used a method for extracting UGS suitable for complex urban environments, and then selected 5 landscape pattern indices such as percent of landscape and patch density to analyse the spatial pattern of UGS in the 36 cities. The results show that: (1) The overall accuracy, recall, F1 value, and intersection over union of the extraction results of UGS were 94.62%, 94.11%, 94.36%, and 89.33% respectively; (2) Overall, the green space rate of the 36 cities exhibited a spatial distribution pattern of “high in the east and low in the west, low in the north and high in the south”; (3) Variations among cities were weak in terms of patch shape, but significant in patch density. The findings of this study cater to the escalating demands of urban planning and management.

Comprehensive Vibration Analysis and Modeling for ZY-3 Satellite's Lifecycle

Abstract. A stable platform environment is essential for achieving high-quality imaging and accurate attitude measurement of the ZY-3's stereo camera. It serves as the fundamental guarantee for the satellite's high geometric precision. Throughout its orbital operation, the platform encounters periodic vibrational occurrences resulting from internal load mechanics and significant external environmental variations, thereby impeding the high-fidelity collection of information by the satellite payload. The detection and modeling of vibrations are crucial tasks for the satellite ground system, as they provide reliable parameter inputs for optimizing attitude accuracy and image quality. This paper presents a method for developing a comprehensive frequency variation vibrational model of the ZY-3 satellite platform throughout its lifecycle, utilizing the inertial gyroscopes (gyros) data of the ZY-3 satellite. The instrument has identified vibrations at three specific frequencies: 0.21 Hz, 0.56 Hz, and 1.12 Hz, each with amplitudes of 0.07″, 0.04″, and 0.04″, respectively. Additionally, it has detected two fluctuating frequencies around 0.3 Hz and 0.6Hz. The period of frequency variation is approximately one year, with vibration ranges of (0.267, 0.299) and (0.603, 0.695). Following a malfunction of the solar panels on the satellite, there has been a shift in the vibration range. Moreover, as the satellite's on-orbit duration increases, this range is also undergoing gradual changes.

Retrieving Surface Deformation of Mining Areas Using ZY-3 Stereo Imagery and DSMs

Measuring surface deformation is crucial for a better understanding of spatial-temporal evolution and the mechanism of mining-induced deformation, thus effectively assessing the mining-related geohazards, such as landslides or damage to surface infrastructures. This study proposes a method of retrieving surface deformation by combining multi-temporal digital surface models (DSMs) with image homonymous features using China’s ZY-3 satellite stereo imagery. DSM is generated from three-line-array images of ZY-3 satellite using a rational function model (RFM) as the imaging geometric model. Then, elevation changes in deformation are extracted using the difference of DSMs acquired at different times, while planar displacements of deformation are calculated using image homonymous features extracted from multi-temporal digital orthographic maps (DOMs). Scale invariant feature transform (SIFT) points and line band descriptor (LBD) lines are selected as two kinds of salient features for image homonymous features generation. Cross profiles are also extracted for deformation in typical regions. Four sets of stereo imagery acquired in 2012 to 2022 are used for deformation extraction and analysis in the Fushun coalfield of China, where surface deformation is quite distinct and coupled with rising and descending elevation together. The results show that 21.60% of the surface in the study area was deformed from 2012 to 2017, while a decline from 2017 to 2022 meant that 17.19% of the surface was deformed with a 95% confidence interval. Moreover, the ratio of descending area was reduced to 6.44% between 2017 and 2022, which is lower than the ratios in other years. The slip deformation area in the west open pit mine is about 1.22 km2 and the displacement on the south slope is large, reaching an average of 26.89 m and sliding from south to north to the bottom of the pit between 2012 and 2017, but elevations are increased by an average of about 16.35 m, involving an area of about 0.86 km2 between 2017 and 2022 due to the restoration of the open pit. The results demonstrate that more quantitative features and specific surface deformation can be retrieved in mining areas by combining image features with DSMs derived from ZY-3 satellite stereo imagery.

A Two-Step Block Adjustment Method for DSM Accuracy Improvement with Elevation Control of ICESat-2 Data

Digital surface models (DSMs) have been widely utilized in various applications as fundamental geographic information data. Block adjustment is normally performed on satellite images to enhance the geometric accuracy and DSMs are then generated by stereo mapping. However, new errors may be introduced during the stereo mapping processing and geometric discrepancies between DSMs may still exist. In particular, block adjustment is difficult for multisource satellite images. Therefore, this paper presents a two-step block adjustment approach directly performed on DSMs, with high-accuracy ICESat-2 laser altimetry data used as elevation control. In the method, DSM tie-point matching, elevation control/check point selection from ICESat-2 laser points, and planar and elevation block adjustments are performed in sequence. In the experiments, ZY-3 satellite stereo images and corresponding generated DSMs, as well as SRTM and ALOS DSMs, are used for verification. The experimental results show that the absolute elevation accuracy and the relative geometric consistency of the DSMs are both significantly improved after two-step DSM block adjustment and it can efficiently improve the accuracy, not only for DSMs acquired by the same sensor type, but also for DSMs acquired by different sensor types, which demonstrates the feasibility and advantage of the proposed method.

Assessment of the Spatial Distribution and Mapping Soil Physico-Chemical Properties of Nalgad Municipality, Jajarkot, Nepal

A detailed soil inventory study using Global Positioning System (GPS) and Geographical Information System (GIS) was conducted in Nalgad Municipality of Jajarkot, Nepal in 2019. A preliminary reconnaissance survey during pre–field activities and detail field work was carried out to study the soil type and physico-chemical properties based on the soil pit. A total of 51 soil pits were taken in the field representing varied micro topography. Soil Sample pits covering all the units were dug based on the interpreted soil map, topographical map, ZY-3 Satellite imagery for determination of soil profile. Soil classification of the area was done based on the USDA soil taxonomy and the dominant soil orders found in the region were Entisols (15.13 %) and Inceptisols (83.60%). A total of 51 geo-referenced composite soil samples from a depth of 0-20 cm was collected from each pit and analyzed in laboratory for texture, soil pH, soil organic matter, total nitrogen, available phosphorus and exchangeable potassium. Majority of the soil are loam and loamy sand type. There is very low to high level of organic matter present in the study area with more proportion of land under high range of organic matter (64.98%). Total Nitrogen content in soil of the study area ranges from very low to very high level with high level of Nitrogen (65.51%) in major proportion. Low to very high level of available phosphorous content was found in the study area with the dominance of very high level of phosphorous (71.40%). Exchangeable Potassium level in the study area is very high to low. Around 42% land have very high level of potassium. From the soil test result, major nutrients status in soil were found to be good but integrated land management practices should be encouraged for improving land productivity. The generated soil maps may be helpful to stakeholders for planning, monitoring and evaluating the soil status for effective agricultural production.

Dynamic PSF-based jitter compensation and quality improvement for push-broom optical images considering terrain relief and the TDI effect.

Platform attitude jitter is inevitable during a satellite flight. The jitter reduces the quality of push-broom optical images, resulting in geometric deformation and image blur. This paper proposes an approach that offers jitter effect compensation and quality improvement for push-broom optical images based on dynamic point spread function (PSF) estimation and iterative image restoration considering the effect of terrain relief and time delay integration (TDI). First, the attitude jitter is precisely detected and estimated by considering the terrain relief. The jitter information is then used to establish the dynamic PSF of each image line considering the changing jitter values and TDI effect. Finally, based on the constructed PSF, the image is improved by iterative restoration using the optimal-window Richardson-Lucy algorithm. The method is validated by both simulation and real data experiments. In the simulation experiment, we apply jitter with different amplitudes and frequencies to generate the degraded images and then restore the image using the proposed restoration method. The results show that the proposed method can effectively restore images affected by jitter. In addition, real data experiments are carried out with multispectral remote sensing images from the ZY-3 satellite. The results show that in addition to the improvement of the radiometric quality, the geometric quality is also significantly improved in both the across-track and the along-track directions. The experimental results validated that the proposed method outperformed other methods without considering the terrain and TDI effect.

Automatic Extraction of Mountain River Surface and Width Based on Multisource High-Resolution Satellite Images

The extraction of high-resolution geomorphic information from remote sensing images is a key technology for supporting mountain river research. Extracting small rivers (width < 90 m) from complex backgrounds based on satellite images remains a challenging issue. In this research, we propose an improved random forest (RF) algorithm, RF-ANN (artificial neural network), by using neural networks and thermal infrared data for the extraction of river surfaces. We also develop an automated river width extraction (ARWE) method based on the central axis transformation algorithm and centerline automatic correction algorithm for the automatic extraction of the river widths across the whole basin. We chose the Huangfuchuan River Basin on the Loess Plateau, China, as a case study area. Chinese GF-1 and ZY-3 satellite images were implemented as the primary data source. We extracted the bankfull river surface and river widths of the Huangfuchuan River by using these two improved methods. The results show that the RF-ANN method has a total river surface extraction accuracy of 94.7%, and the extracted river surfaces cover more than 85% of the order 3 DEM river network. By implementing high-resolution DEM and thermal infrared data, RF-ANN effectively eliminates the disturbance of shadows of mountains and other features, which ensures the high accuracy of the extracted widths. It was verified that the maximum and minimum river widths that can be extracted in the Huangfuchuan River Basin are 297.4 m and 6.1 m, respectively. The overall error of river width extraction is 0.97 m, which is less than half of the pixel length of remote sensing images. The R2 and root mean square error (RMSE) of the estimated river width values are 0.99 and 1.49, respectively. For tiny rivers with widths narrower than 10 m, the error of river width extraction is 10.9%. The error of thin rivers whose widths range from 10 to 30 m is 4.9%. For small rivers ranging from 30 to 90 and rivers wider than 90 m, the error is 1.1% and 0.6%, respectively. The new approach provides an effective method for extracting the surface and width of mountain rivers in topographically complex regions by using high-resolution satellite images, which may provide a database for estimating river carbon emissions and related research in fluvial morphology and water resource management.

Remote Sensing Plateau Forest Segmentation with Boundary Preserving Double Loss Function Collaborative Learning

Plateau forest plays an important role in the high-altitude ecosystem, and contributes to the global carbon cycle. Plateau forest monitoring request in-suit data from field investigation. With recent development of the remote sensing technic, large-scale satellite data become available for surface monitoring. Due to the various information contained in the remote sensing data, obtain accurate plateau forest segmentation from the remote sensing imagery still remain challenges. Recent developed deep learning (DL) models such as deep convolutional neural network (CNN) has been widely used in image processing tasks, and shows possibility for remote sensing segmentation. However, due to the unique characteristics and growing environment of the plateau forest, generate feature with high robustness needs to design structures with high robustness. Aiming at the problem that the existing deep learning segmentation methods are difficult to generate the accurate boundary of the plateau forest within the satellite imagery, we propose a method of using boundary feature maps for collaborative learning. There are three improvements in this article. First, design a multi input model for plateau forest segmentation, including the boundary feature map as an additional input label to increase the amount of information at the input. Second, we apply a strong boundary search algorithm to obtain boundary value, and propose a boundary value loss function. Third, improve the Unet segmentation network and combine dense block to improve the feature reuse ability and reduces the image information loss of the model during training. We then demonstrate the utility of our method by detecting plateau forest regions from ZY-3 satellite regarding to Sanjiangyuan nature reserve. The experimental results show that the proposed method can utilize multiple feature information comprehensively which is beneficial to extracting information from boundary, and the detection accuracy is generally higher than several state-of-art algorithms. As a result of this investigation, the study will contribute in several ways to our understanding of DL for region detection and will provide a basis for further researches.

The Austrian node of the natural resources satellite remote sensing cloud service platform: examples of Sino-Austrian cooperation

ABSTRACT The Austrian node of the Natural Resources Satellite Remote Sensing Cloud Service Platform was established in 2016 through a cooperation agreement between the Land Satellite Remote Sensing Application Center (LASAC), Ministry of Natural Resources of the Peoples Republic of China and the University of Vienna, Austria. Under this agreement panchromatic and multispectral data of the Chinese ZY-3 satellite are pushed to the server at the University of Vienna for use in education and research. So far, nearly 500 GB of data have been uploaded to the server. This technical note briefly introduces the ZY-3 system and illustrates the implementation of the agreement by the first China-Sat Workshop and several case studies. Some of them are already completed, others are still ongoing. They include a geometric accuracy validation of ZY-3 data, an animated visualization of image quick views on a spherical display to demonstrate the time series of the image coverage for Austria and Laos, and the use of ZY-3 data to study the spread of bark beetle in the province of Lower Austria. An accuracy study of DTMs from ZY-3 stereo data, as well as a land cover analysis and comparison of Austria with ZY-3 and other sensors are still ongoing.

A Multispectral and Multiangle 3-D Convolutional Neural Network for the Classification of ZY-3 Satellite Images Over Urban Areas

The recent availability of high-resolution multiview ZY-3 satellite images, with angular information, can provide an opportunity to capture 3-D structural features for classification. In high-resolution image classification over urban areas, objects with diverse vertical structures make urban landscape more heterogeneous in 3-D space and consequently can make the classification challenging. In this article, a novel multiangle gray-level cooccurrence tensor feature is proposed based on the multiview bands of the ZY-3 imagery, namely, GLCM^MA–T. The GLCM^MA–T feature captures the distributions of the gray-level spatial variation under different viewing angles, which can depict the 3-D textures and structures of urban objects. The spectral and GLCM^MA–T tensor features are interpreted by two 3-D convolutional neural network (CNN) streams and then concatenated as the input to the fully connected layer. This novel multispectral and multiangle 3-D convolutional neural network (M²-3-DCNN) combines the spectral and angular information, and the fused feature has the potential to provide a comprehensive description of urban objects with complex vertical structures. The experimental results on ZY-3 multiview images from four test areas indicate that the proposed method can significantly improve the classification accuracy when compared with several state-of-the-art multiangle features and deep-learning-based image classification methods.

Estimates of Forest Canopy Height Using a Combination of ICESat-2/ATLAS Data and Stereo-Photogrammetry

Forest canopy height is an indispensable forest vertical structure parameter for understanding the carbon cycle and forest ecosystem services. A variety of studies based on spaceborne Lidar, such as ICESat, ICESat-2 and airborne Lidar, were conducted to estimate forest canopy height at multiple scales. However, while a few studies have been conducted based on ICESat-2 simulated data from airborne Lidar data, few studies have analyzed ATL08 and ATL03 products derived from the ATLAS sensor onboard ICESat-2 for regional vegetation canopy height mapping. It is necessary and promising to explore how data obtained by ICESat-2 can be applied to estimate forest canopy height. This study proposes a new means to estimate forest canopy height, defined as the mean height of trees within a given forest area, using a combination of ICESat-2 ATL08 and ATL03 data and ZY-3 satellite stereo images. Five procedures were used to estimate the forest canopy height of the city of Nanning in China: (1) Processing ground photons in a 30 m × 30 m grid; (2) Extracting a digital surface model (DSM) using ZY-3 stereo images; (3) Calculating a discontinuous canopy height model (CHM) dataset; (4) Validating the DSM and ground photon height using GEDI data; (5) Estimating the regional wall-to-wall forest canopy height product based on the backpropagation artificial neural network (BP-ANN) model and Landsat 8 vegetation indices and independent accuracy assessments with field measured plots. The validation shows a root mean square error (RMSE) of 3.34 m to 3.47 m and a coefficient of determination R2 = 0.51. The new method shows promise and can be used for large-scale forest canopy height mapping at various resolutions or in combination with other data, such as SAR images. Finally, this study analyzes resolutions and how to filter effective data when ATL08 data are directly used to generate regional or global vegetation height products, which will be the focus of future research.

THE OVERSEAS GEOMETRIC ACCURACY VALIDATION OF ZY-3 SATELLITES IMAGES

Abstract. With China’s first stereo imaging satellite ZY-3’s successful launch and two satellites networking operation, space remote sensing becomes an important means of data acquisition for survey and mapping, and geographic information updating in China.The updating period of 4D products in China is shortened by one third, the geographic information updating capability was improved more than 2 times.After more than eight years of stable and continued earth observation, ZY-3 statellites data coverage has gradually achieved more than half of the earth land which enables the overseas service for global geospatial application. Focusing on the Chinese domestic mapping applications and services, the data of ZY-3 and other domestic land observation satellites can be real-time pushed and distributed across China through the satellite cloud service platform, and worldwide for facilitating the "Belt and Road" initiative, which is promoted through the GEOSS as well for global application.This paper presents the overseas geometric accuracy evaluation and validation of ZY-3 satellites images, which was carried out systematically for the first time abroad and complements with China’s mapping evaluation practices to provide a comprehensive technical guidelines for global mapping application. The direct location accuracy of ZY-3 satellites sensor corrected image products were further evaluated and verified cooperating with the University of Vienna, Austria. The location deviations in mountain area and in flat area were analyzed and compared to check the deviation pattern and validate their consistency with the results in China.Utilizing China’s ZY-3 satellites images ranging from 2012–2018, the DSM was generated automatically based on a semiglobal optimization method (Yue et al. 2016) using self-developed software SDP without ground control points but using SRTM data as elevation reference to improve the geometric accuracy. The result shows the elevation accuracy of DSM is 3.19 m (RMSE), satisfying the local requirement for 10m interval contour mapping.The planar accuracy of DOM generated without ground control points utilizing ZY-3 satellites images within 2012–2015 is 2.46 m (RMSE). With 128 orthorectified aerial photo slots provided by NGD of Laos as reference, the planar accuracy of the DOM generated utilizing ZY-3 satellites images from 2016–2018 is 1.65 m (RMSE), which can satisfy the local 1:25000 planar accuracy requirement and overlay with the local 1:2000 transportation data exactly. The overseas geometric accuracy evaluation and validation of ZY-3 satellites images provides a systematic and practical guidance for future global applications by complementing with china’s experience, which would be beneficial to overseas users in acknowledging the usage of ZY-3 satellites data and help to promote it’s global applications.

BLOCK ADJUSTMENT OF MULTISPECTRAL IMAGES WITHOUT GCP AIDED BY STEREO TLC IMAGES FOR ZY-3 SATELLITE

Abstract. Multispectral images are the main data source of optical satellite remote sensing application. Consistent geometric accuracy is the basis of image registration and fusion. But the multispectral images usually collected by the nadir imaging camera are of weak geometric intersection, which will lead to the rank defect of the adjustment equation when performing block adjustment (BA) directly, making the solution unstable. Thus, a planar BA aided by additional digital surface model (DSM) which can overcome this weak geometry is often used to improve the geometric accuracy and consistency of regional planar images. However, the inevitable elevation error and the indispensable ground control points (GCPs) make the method limited in practical application. In this paper, a new method aiming to the BA of planer multispectral images of ZY-3 satellite without use of GCPs is presented. This method introduces the constraint of stereo images to assist the BA of planar images. By configuring appropriate weights of different observations, the integrated optimization of positioning models of planar and stereo images can be achieved together. The effectiveness of the method was verified by the planar multispectral images and stereo three linear camera (TLC) images collected by ZY-3 satellite. The satisfactory results indicated the rationality and effectiveness of the presented method.

A JITTER DETECTION METHOD BASED ON THE INTEGRATION IMAGING MODEL

Abstract. Satellite jitter is a random error source which leads to image degradation. This paper proposes a method to detect the time-variant jitter using multispectral images. In the method, multispectral images are adopted for their large overlap to obtain the parallax map. The imaging process is analyzed in details, and an integration imaging model is constructed, which takes fully into account the time-variant jitter property and builds the relationship between object space with image space. Besides, multispectral images of ZY-3 satellite were used for experiments, and results show that the presented method obtains the jitter curve with the error of amplitude, frequency and phase not more than 0.0591 px, 0.0006 Hz and 0.007 rad, respectively. Results demonstrate the performance of the presented method in jitter detection.

CDnetV2: CNN-Based Cloud Detection for Remote Sensing Imagery With Cloud-Snow Coexistence

Cloud detection is a crucial preprocessing step for optical satellite remote sensing (RS) images. This article focuses on the cloud detection for RS imagery with cloud-snow coexistence and the utilization of the satellite thumbnails that lose considerable amount of high resolution and spectrum information of original RS images to extract cloud mask efficiently. To tackle this problem, we propose a novel cloud detection neural network with an encoder-decoder structure, named CDnetV2, as a series work on cloud detection. Compared with our previous CDnetV1, CDnetV2 contains two novel modules, that is, adaptive feature fusing model (AFFM) and high-level semantic information guidance flows (HSIGFs). AFFM is used to fuse multilevel feature maps by three submodules: channel attention fusion model (CAFM), spatial attention fusion model (SAFM), and channel attention refinement model (CARM). HSIGFs are designed to make feature layers at decoder of CDnetV2 be aware of the locations of the cloud objects. The high-level semantic information of HSIGFs is extracted by a proposed high-level feature fusing model (HFFM). By being equipped with these two proposed key modules, AFFM and HSIGFs, CDnetV2 is able to fully utilize features extracted from encoder layers and yield accurate cloud detection results. Experimental results on the ZY-3 satellite thumbnail data set demonstrate that the proposed CDnetV2 achieves accurate detection accuracy and outperforms several state-of-the-art methods.

High-resolution urban land-cover mapping and landscape analysis of the 42 major cities in China using ZY-3 satellite images

Detailed and precise urban land-cover maps are crucial for urban-related studies. However, there are limited ways of mapping high-resolution urban land cover over large areas. In this paper, we propose an operational framework to map urban land cover on the basis of Ziyuan-3 satellite images. Based on this framework, we produced the first high-resolution (2 m) urban land-cover map (Hi-ULCM) covering the 42 major cities of China. The overall accuracy of the Hi-ULCM dataset is 88.55%, of which 14 cities have an overall accuracy of over 90%. Most of the producer’s accuracies and user’s accuracies of the land-cover classes exceed 85%. We further conducted a landscape pattern analysis in the 42 cities based on Hi-ULCM. In terms of the comparison between the 42 cities in China, we found that the difference in the land-cover composition of urban areas is related to the climatic characteristics and urbanization levels, e.g., cities with warm climates generally have higher proportions of green spaces. It is also interesting to find that cities with higher urbanization levels are more habitable, in general. From the landscape viewpoint, the geometric complexity of the landscape increases with the urbanization level. Compared with the existing medium-resolution land-cover/use datasets (at a 30-m resolution), Hi-ULCM represents a significant advance in accurately depicting the detailed land-cover footprint within the urban areas of China, and will be of great use for studies of urban ecosystems.

A Deep Siamese Network with Hybrid Convolutional Feature Extraction Module for Change Detection Based on Multi-sensor Remote Sensing Images

Information extraction from multi-sensor remote sensing images has increasingly attracted attention with the development of remote sensing sensors. In this study, a supervised change detection method, based on the deep Siamese convolutional network with hybrid convolutional feature extraction module (OB-DSCNH), has been proposed using multi-sensor images. The proposed architecture, which is based on dilated convolution, can extract the deep change features effectively, and the character of “network in network” increases the depth and width of the network while keeping the computational budget constant. The change decision model is utilized to detect changes through the difference of extracted features. Finally, a change detection map is obtained via an uncertainty analysis, which combines the multi-resolution segmentation, with the output from the Siamese network. To validate the effectiveness of the proposed approach, we conducted experiments on multispectral images collected by the ZY-3 and GF-2 satellites. Experimental results demonstrate that our proposed method achieves comparable and better performance than mainstream methods in multi-sensor images change detection.

星载激光遥感林业应用发展研究

Forestry observation is an important direction of the development of space-borne LiDAR. So far, foreign space laser remote sensing payloads have been developed. The experimental LiDAR carried by Chinese ZY-3(02) satellite is a big breakthrough in earth observation of space-borne LiDAR in China, and the oncoming multi-beam LiDAR of Chinese CE-1 satellite will be applied to forestry remote sensing. The vertical structure of forest was acquired by ICESAT-1/GLAS waveform, while point cloud of forest was obtained by ICESAT-2/ATLAS photons. The space-borne LiDAR forestry remote sensing gradually entered the practical stage, beyond the experimental stage. During this period, technical system, configuration, and techniques had improved. In this paper, the applications and technique developments of main forestry remote sensing space-borne LiDAR in use were analyzed, and the characteristics and development trend were summarized, based on the study of detection principle, technical system, observation condition, etc. Space-borne forestry observation LiDAR payloads should be designed according to scientific mission, application requirements and technical characteristics, using the advantages of space-borne LiDAR, and based on which the development trend and research focus of space-borne forestry observation technology and application were prospected.

Block Adjustment of Large-scale Domestic Optical Satellite Remote Sensing Imagery without GCP in Antarctic

Aiming to the difficult area of surveying and mapping, such as Antarctic, it is difficult to guarantee favourable observation condition for traditional mapping methods. Large-scale block adjustment (BA) without ground control point (GCP) is carried out by using domestic high-resolution satellite remote sensing imagery. The key technologies of BA model construction based on virtual control point (VCP), gross error detection and elimination, and robust and fast method of large-scale BA are studied in this paper. On this basis, the experimental analysis and validation of 13pairs (39 images) of ZY-3 satellite imagery in Antarctic are carried out. The results show that the sparse matrix technology can effectively reduce the memory requirement. The combined matrix block and GPU parallel technology can solve the problem of large-scale BA computational efficiency. In addition, after BA, the maximum residual is 3.920 pixel, the root mean square error (RMSE) is 0.169 pixel in the X (flight) direction, the maximum residual error is 5.933 pixel, and the RMSE is 0.191 pixel in the Y (scan) direction. The proposed method has certain accuracy and stability in large-scale BA without GCP using high-resolution satellite remote sensing imagery in Antarctica. The relative positioning accuracy can reach sub-pixel level, which can meet the requirements of cartographic splicing.