scholarly journals ALGORITHM AND APPLICATION OF GCP-INDEPENDENT BLOCK ADJUSTMENT FOR SUPER LARGE-SCALE DOMESTIC HIGH RESOLUTION OPTICAL SATELLITE IMAGERY

2018 ◽  
Vol XLII-3 ◽  
pp. 1607-1615
Author(s):  
Y. S. Sun ◽  
L. Zhang ◽  
B. Xu ◽  
Y. Zhang
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Large Scale ◽  
Satellite Images ◽  
Satellite Image ◽  
Optimization Method ◽  
Adjustment Process ◽  
Large Area ◽  
Rank Defect ◽  
And Performance

The accurate positioning of optical satellite image without control is the precondition for remote sensing application and small/medium scale mapping in large abroad areas or with large-scale images. In this paper, aiming at the geometric features of optical satellite image, based on a widely used optimization method of constraint problem which is called Alternating Direction Method of Multipliers (ADMM) and RFM least-squares block adjustment, we propose a GCP independent block adjustment method for the large-scale domestic high resolution optical satellite image – GISIBA (GCP-Independent Satellite Imagery Block Adjustment), which is easy to parallelize and highly efficient. In this method, the virtual "average" control points are built to solve the rank defect problem and qualitative and quantitative analysis in block adjustment without control. The test results prove that the horizontal and vertical accuracy of multi-covered and multi-temporal satellite images are better than 10 m and 6 m. Meanwhile the mosaic problem of the adjacent areas in large area DOM production can be solved if the public geographic information data is introduced as horizontal and vertical constraints in the block adjustment process. Finally, through the experiments by using GF-1 and ZY-3 satellite images over several typical test areas, the reliability, accuracy and performance of our developed procedure will be presented and studied in this paper.

scholarly journals Automatic 3-D Building Model Reconstruction from Very High Resolution Stereo Satellite Imagery

2019 ◽  
Vol 11 (14) ◽  
pp. 1660
Author(s):  
Partovi ◽  
Fraundorfer ◽  
Bahmanyar ◽  
Huang ◽  
Reinartz
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Large Scale ◽  
Missing Values ◽  
Model Reconstruction ◽  
Large Area ◽  
Building Model ◽  
Building Models ◽  
D Model ◽  
Very High

Recent advances in the availability of very high-resolution (VHR) satellite data together withefficient data acquisition and large area coverage have led to an upward trend in their applicationsfor automatic 3-D building model reconstruction which require large-scale and frequent updates,such as disaster monitoring and urban management. Digital Surface Models (DSMs) generatedfrom stereo satellite imagery suffer from mismatches, missing values, or blunders, resulting inrough building shape representations. To handle 3-D building model reconstruction using suchlow-quality DSMs, we propose a novel automatic multistage hybrid method using DSMs togetherwith orthorectified panchromatic (PAN) and pansharpened data (PS) of multispectral (MS) satelliteimagery. The algorithm consists of multiple steps including building boundary extraction anddecomposition, image-based roof type classification, and initial roof parameter computation whichare prior knowledge for the 3-D model fitting step. To fit 3-D models to the normalized DSM(nDSM) and to select the best one, a parameter optimization method based on exhaustive searchis used sequentially in 2-D and 3-D. Finally, the neighboring building models in a building blockare intersected to reconstruct the 3-D model of connecting roofs. All corresponding experimentsare conducted on a dataset including four different areas of Munich city containing 208 buildingswith different degrees of complexity. The results are evaluated both qualitatively and quantitatively.According to the results, the proposed approach can reliably reconstruct 3-D building models, eventhe complex ones with several inner yards and multiple orientations. Furthermore, the proposedapproach provides a high level of automation by limiting the number of primitive roof types and byperforming automatic parameter initialization.

scholarly journals STATE OF THE ART IN DIGITAL SURFACE MODELLING FROM MULTI-VIEW HIGH-RESOLUTION SATELLITE IMAGES

2020 ◽  
Vol V-2-2020 ◽  
pp. 351-356 ◽  
Author(s):  
Y. Han ◽  
S. Wang ◽  
D. Gong ◽  
Y. Wang ◽  
Y. Wang ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Satellite Images ◽  
Satellite Image ◽  
Ground Truth ◽  
Semantic Interpretation ◽  
Surface Model ◽  
Three Solutions ◽  
Surface Modelling ◽  
High Resolution Satellite Images

Abstract. Data from the optical satellite imaging sensors running 24/7, is collecting in embarrassing abundance nowadays. Besides more suitable for large-scale mapping, multi-view high-resolution satellite images (HRSI) are cheaper when comparing to Light Detection And Ranging (LiDAR) data and aerial remotely sensed images, which are more accessible sources for digital surface modelling and updating. Digital Surface Model (DSM) generation is one of the most critical steps for mapping, 3D modelling, and semantic interpretation. Computing DSM from this dataset is relatively new, and several solutions exist in the market, both commercial and open-source solutions, the performances of these solutions have not yet been comprehensively analyzed. Although some works and challenges have focused on the DSM generation pipeline and the geometric accuracy of the generated DSM, the evaluations, however, do not consider the latest solutions as the fast development in this domain. In this work, we discussed the pipeline of the considered both commercial and opensource solutions, assessed the accuracy of the multi-view satellite image-based DSMs generation methods with LiDAR-derived DSM as the ground truth. Three solutions, including Satellite Stereo Pipeline (S2P), PCI Geomatica, and Agisoft Metashape, are evaluated on a WorldView-3 multi-view satellite dataset both quantitatively and qualitatively with the LiDAR ground truth. Our comparison and findings are presented in the experimental section.

scholarly journals A Large-Scale Dataset and Deep Learning Model for Detecting and Counting Olive Trees in Satellite Imagery

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Amr Abozeid ◽  
Rayan Alanazi ◽  
Ahmed Elhadad ◽  
Ahmed I. Taloba ◽  
Rasha M. Abd El-Aziz
Keyword(s):  
Deep Learning ◽  
Satellite Imagery ◽  
Large Scale ◽  
Estimation Error ◽  
Satellite Image ◽  
Olive Tree ◽  
Learning Model ◽  
Large Area ◽  
Olive Trees ◽  
Deep Learning Model

Since the Pre-Roman era, olive trees have a significant economic and cultural value. In 2019, the Al-Jouf region, in the north of the Kingdom of Saudi Arabia, gained a global presence by entering the Guinness World Records, with the largest number of olive trees in the world. Olive tree detecting and counting from a given satellite image are a significant and difficult computer vision problem. Because olive farms are spread out over a large area, manually counting the trees is impossible. Moreover, accurate automatic detection and counting of olive trees in satellite images have many challenges such as scale variations, weather changes, perspective distortions, and orientation changes. Another problem is the lack of a standard database of olive trees available for deep learning applications. To address these problems, we first build a large-scale olive dataset dedicated to deep learning research and applications. The dataset consists of 230 RGB images collected over the territory of Al-Jouf, KSA. We then propose an efficient deep learning model (SwinTUnet) for detecting and counting olive trees from satellite imagery. The proposed SwinTUnet is a Unet-like network which consists of an encoder, a decoder, and skip connections. Swin Transformer block is the fundamental unit of SwinTUnet to learn local and global semantic information. The results of an experimental study on the proposed dataset show that the SwinTUnet model outperforms the related studies in terms of overall detection with a 0.94% estimation error.

scholarly journals Penerapan Algoritma LZ77 dan Algoritma Difference Coding Untuk Memperkecil Ukuran pada Citra Satelite Geoeye

2020 ◽  
Vol 7 (2) ◽  
pp. 225
Author(s):  
Amanda Pratama ◽  
Muhammad Syahrizal
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Satellite Images ◽  
Satellite Image ◽  
Level Of Detail ◽  
The Other ◽  
Earth Surface ◽  
Surface Image

Geoeye satellite imagery is one of the satellite images that has a high resolution and is gaining in popularity in recent years. As one of the global satellite imagery, it is clear that this satellite image offers something that can amaze anyone of its users, this satellite image offers an earth surface image with a level of detail that can be considered extraordinary, and the level of accuracy after compared to satellite imagery that has high resolution the other

Mapping and Monitoring of the Land Use/Cover Changes in the Wider Area of ltanos, Crete, Using Very High Resolution EO Imagery With Specific Interest in Archaeological Sites

2019 ◽  
Author(s):  
Sawyer Reid stippa ◽  
George Petropoulos ◽  
Leonidas Toulios ◽  
Prashant K. Srivastava
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Satellite Images ◽  
Archaeological Site ◽  
Digital Photography ◽  
Archaeological Sites ◽  
Large Area ◽  
High Resolution Imagery ◽  
Site Mapping ◽  
Rule Sets

Archaeological site mapping is important for both understanding the history as well as protecting them from excavation during the developmental activities. As archaeological sites generally spread over a large area, use of high spatial resolution remote sensing imagery is becoming increasingly applicable in the world. The main objective of this study was to map the land cover of the Itanos area of Crete and of its changes, with specific focus on the detection of the landscape’s archaeological features. Six satellite images were acquired from the Pleiades and WorldView-2 satellites over a period of 3 years. In addition, digital photography of two known archaeological sites was used for validation. An Object Based Image Analysis (OBIA) classification was subsequently developed using the five acquired satellite images. Two rule-sets were created, one using the standard four bands which both satellites have and another for the two WorldView-2 images their four extra bands included. Validation of the thematic maps produced from the classification scenarios confirmed a difference in accuracy amongst the five images. Comparing the results of a 4-band rule-set versus the 8-band showed a slight increase in classification accuracy using extra bands. The resultant classifications showed a good level of accuracy exceeding 70%. Yet, separating the archaeological sites from the open spaces with little or no vegetation proved challenging. This was mainly due to the high spectral similarity between rocks and the archaeological ruins. The satellite data spatial resolution allowed for the accuracy in defining larger archaeological sites, but still was a difficulty in distinguishing smaller areas of interest. The digital photography data provided a very good 3D representation for the archaeological sites, assisting as well in validating the satellite-derived classification maps. All in all, our study provided further evidence that use of high resolution imagery may allow for archaeological sites to be located, but only where they are of a suitable size archaeological features.

scholarly journals Rapid Single Image-Based DTM Estimation from ExoMars TGO CaSSIS Images Using Generative Adversarial U-Nets

2021 ◽  
Vol 13 (15) ◽  
pp. 2877
Author(s):  
Yu Tao ◽  
Siting Xiong ◽  
Susan J. Conway ◽  
Jan-Peter Muller ◽  
Anthony Guimpier ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Imaging System ◽  
Landing Site ◽  
Parameter Tuning ◽  
Processing Parameters ◽  
Input Image ◽  
Large Area ◽  
Laser Altimeter ◽  
Imaging Science

The lack of adequate stereo coverage and where available, lengthy processing time, various artefacts, and unsatisfactory quality and complexity of automating the selection of the best set of processing parameters, have long been big barriers for large-area planetary 3D mapping. In this paper, we propose a deep learning-based solution, called MADNet (Multi-scale generative Adversarial u-net with Dense convolutional and up-projection blocks), that avoids or resolves all of the above issues. We demonstrate the wide applicability of this technique with the ExoMars Trace Gas Orbiter Colour and Stereo Surface Imaging System (CaSSIS) 4.6 m/pixel images on Mars. Only a single input image and a coarse global 3D reference are required, without knowing any camera models or imaging parameters, to produce high-quality and high-resolution full-strip Digital Terrain Models (DTMs) in a few seconds. In this paper, we discuss technical details of the MADNet system and provide detailed comparisons and assessments of the results. The resultant MADNet 8 m/pixel CaSSIS DTMs are qualitatively very similar to the 1 m/pixel HiRISE DTMs. The resultant MADNet CaSSIS DTMs display excellent agreement with nested Mars Reconnaissance Orbiter Context Camera (CTX), Mars Express’s High-Resolution Stereo Camera (HRSC), and Mars Orbiter Laser Altimeter (MOLA) DTMs at large-scale, and meanwhile, show fairly good correlation with the High-Resolution Imaging Science Experiment (HiRISE) DTMs for fine-scale details. In addition, we show how MADNet outperforms traditional photogrammetric methods, both on speed and quality, for other datasets like HRSC, CTX, and HiRISE, without any parameter tuning or re-training of the model. We demonstrate the results for Oxia Planum (the landing site of the European Space Agency’s Rosalind Franklin ExoMars rover 2023) and a couple of sites of high scientific interest.

scholarly journals Efficient and large-area all vacuum-deposited perovskite light-emitting diodes via spatial confinement

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Peipei Du ◽  
Jinghui Li ◽  
Liang Wang ◽  
Liang Sun ◽  
Xi Wang ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
High Efficiency ◽  
Light Emitting Diodes ◽  
Scale Production ◽  
Nonradiative Recombination ◽  
Large Area ◽  
Spatial Confinement ◽  
Light Emitting ◽  
Large Scale Production

AbstractWith rapid advances of perovskite light-emitting diodes (PeLEDs), the large-scale fabrication of patterned PeLEDs towards display panels is of increasing importance. However, most state-of-the-art PeLEDs are fabricated by solution-processed techniques, which are difficult to simultaneously achieve high-resolution pixels and large-scale production. To this end, we construct efficient CsPbBr3 PeLEDs employing a vacuum deposition technique, which has been demonstrated as the most successful route for commercial organic LED displays. By carefully controlling the strength of the spatial confinement in CsPbBr3 film, its radiative recombination is greatly enhanced while the nonradiative recombination is suppressed. As a result, the external quantum efficiency (EQE) of thermally evaporated PeLED reaches 8.0%, a record for vacuum processed PeLEDs. Benefitting from the excellent uniformity and scalability of the thermal evaporation, we demonstrate PeLED with a functional area up to 40.2 cm2 and a peak EQE of 7.1%, representing one of the most efficient large-area PeLEDs. We further achieve high-resolution patterned perovskite film with 100 μm pixels using fine metal masks, laying the foundation for potential display applications. We believe the strategy of confinement strength regulation in thermally evaporated perovskites provides an effective way to process high-efficiency and large-area PeLEDs towards commercial display panels.

Resolving pre-collapse slope motion at the February 2021 Chamoli rock-ice avalanche via feature tracking of optical satellite imagery

2021 ◽  
Author(s):  
Maximillian Van Wyk de Vries ◽  
Shashank Bhushan ◽  
David Shean ◽  
Etienne Berthier ◽  
César Deschamps-Berger ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Feature Tracking ◽  
Satellite Image ◽  
Image Feature ◽  
Sampling Distance ◽  
Velocity Changes ◽  
The Stability ◽  
Slide Area ◽  
Ice Avalanche

<p>On the 7<sup>th</sup> of February 2021, a large rock-ice avalanche triggered a debris flow in Chamoli district, Uttarakhand, India, resulting in over 200 dead or missing and widespread infrastructure damage. The rock-ice avalanche originated from a steep, glacierized north-facing slope with a history of instability, most recently a 2016 ice avalanche. In this work, we assess whether the slope exhibited any precursory displacement prior to collapse. We evaluate monthly slope motion over the 2015 and 2021 period through feature tracking of high-resolution optical satellite imagery from Sentinel-2 (10 m Ground Sampling Distance) and PlanetScope (3-4 m Ground Sampling Distance). Assessing slope displacement of the underlying rock is complicated by the presence of glaciers over a portion of the collapse area, which display surface displacements due to internal ice deformation. We overcome this through tracking the motion over ice-free portions of the slide area, and evaluating the spatial pattern of velocity changes in glaciated areas. Preliminary results show that the rock-ice avalanche bloc slipped over 10 m in the 5 years prior to collapse, with particularly rapid slip occurring in the summer of 2017 and 2018. These results provide insight into the precursory conditions of the deadly rock-ice avalanche, and highlight the potential of high-resolution optical satellite image feature tracking for monitoring the stability of high-risk slopes.</p>

scholarly journals Vehicle Detection and Classification from High Resolution Satellite Images

2014 ◽  
Vol II-1 ◽  
pp. 1-8 ◽  
Author(s):  
L. Abraham ◽  
M. Sasikumar
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Satellite Images ◽  
Weather Forecasting ◽  
Vehicle Detection ◽  
Satellite Imaging ◽  
Suitable Alternative ◽  
High Resolution Satellite Images ◽  
Technological Developments ◽  
High Resolution Satellite Imagery

In the past decades satellite imagery has been used successfully for weather forecasting, geographical and geological applications. Low resolution satellite images are sufficient for these sorts of applications. But the technological developments in the field of satellite imaging provide high resolution sensors which expands its field of application. Thus the High Resolution Satellite Imagery (HRSI) proved to be a suitable alternative to aerial photogrammetric data to provide a new data source for object detection. Since the traffic rates in developing countries are enormously increasing, vehicle detection from satellite data will be a better choice for automating such systems. In this work, a novel technique for vehicle detection from the images obtained from high resolution sensors is proposed. Though we are using high resolution images, vehicles are seen only as tiny spots, difficult to distinguish from the background. But we are able to obtain a detection rate not less than 0.9. Thereafter we classify the detected vehicles into cars and trucks and find the count of them.

scholarly journals Segmentation of Satellite Imagery of Amedi Site Using Chan–Vese Model with Saliency Estimation

2020 ◽  
Vol 1 (4) ◽  
pp. 125-134
Author(s):  
Pawan Rachee
Keyword(s):  
Satellite Imagery ◽  
Satellite Images ◽  
Object Segmentation ◽  
Satellite Image ◽  
Bilateral Filter ◽  
Histogram Equalization ◽  
Saliency Map ◽  
Enhancement Technique ◽  
Map Segmentation ◽  
Quantitative Manner

The images that have been taken from space satellites are described by satellite imagery. The presence of the earth's surface is detected by remote sensing. Normally the source of the satellite image is barely seen, because many points in the sky are obscured with cloud shadows. Therefore, one of the most important and ubiquitous tasks in image analysis is segmentation. Segmentation is the method of dividing a image into a collection of specific regions that vary in some essential qualitative or quantitative manner. In this paper we will focus on a method for segmenting images that was developed   Three different methods to detect the location of the satellite images have been studied, implemented, and tested; these are based on Chan-Vese and saliency map segmentation, and multi-resolution segmentation to obtain a proper object segmentation. In this study, the combination of the proposed segmentation automatic detection and image enhancement technique has been performed to reduce the noise of the original image. In addition, the Bilateral filter, and histogram equalization are used in these proposed techniques. Experimental results demonstrate that the suggested method can precisely extract the objective of Amedi site from the satellite images with difficult backgrounds and overlapping regions.

Sign in / Sign up

Export Citation Format

Share Document