Quality Of Satellite Images Research Articles

Glacial relief of the central part of the Kola Region

Improvement in quality of digital elevation models and satellite images of the Earth’s surface led to a tendency to interpret them without sufficient confirmation by geological research methods. At the same time, the geological data is critical for the interpretation of genesis of accumulative glacial landforms and regional landscape reconstruction during the last glaciation. The article provides a classification and geologic structure of the glacial relief of one of the key areas in the Kola region. New data were obtained using morphometric analysis of relief, geological, structural analysis of glacial landforms, petrographic analysis of coarse glacial deposits, and the study of lake sediments. Two bands of glacial accumulative relief were identified in the study area. The first band forms a parallel ridge relief on the southern slope of the Lovozero Tundra. It represents the formations of a lateral moraine formed at the edge of a glacier moving from the west to the east along the slope. Also a hummocky-ridge relief along the slopes of the Lovozero, Panskie, and Fedorova Tundras that consist of terminal moraines is included in this band. The moraines are composed of dislocated limno- and fluvioglacial deposits, dump and ablative moraines. The second band is formed by three subparallel chains of ridge-hummocky relief. They include folded and imbricated-thrust glaciotectonically deformed deposits. Fluvioglacial deposits are developed on the distal slope of the outer chain. Both bands of glacial relief are associated with formation of marginal landforms during two stages of glacial retreats. Analysis of deglaciation models of the last ice sheet in the Kola and adjacent regions and data on the position of known marginal glacial formations made it possible to compare the stages with the final episodes of the Luga (Karelian) and Neva (Syamozero) Stages. The information obtained reveals more details about the stages of development of the last ice sheet and the deglaciation pattern of the Kola region in the Late Glacial.

FPGA-Based Satellite Vision Systems using Verilog HDL

Abstract: It is believed that the most effective way to collect information about the Earth's surface is through high- quality satellite images. Extracting a feature from an image is really difficult because you have to choose the best image segmentation methods and combine many strategies to find the Region in the most effective manner. This study makes recommendations for the classification techniques for objects in the satellite. On high-resolution satellite images, applying image processing methods. The methods used to define region mostly focus on urban, agricultural, and forest regions. There are several methods for extracting these traits. Using a Grey Level Cooccurrence Matrix is the most used method. It is employed to unveil specific characteristics regarding the spatial arrangement of gray levels in the texture image. The Grey Level Co-occurrence Matrix (GLCM) captures statistical details of neighboring pixels in an image, enabling the computation of textural features that enhance the comprehension of visual content. This research presents a VLSI implementation aimed at extracting four texture characteristics from the grey level co-occurrence matrix. Verilog was employed to model the hardware, with MATLAB used for software simulation. The simulation utilized the Verilog HDL language from the XILINX tool, and the implementation was executed on the SPARTAN FPGA board.

Improving Digital Satellite Image for security purposes

Satellite imagery is employed in many different fields of study. These pictures have serious quality problems. Image enhancement algorithms, however, can improve it in terms of contrast, brightness, feature elimination from noise contents, etc. These algorithms present and analyse the picture's properties by sharpening, focusing, or smoothing the image. Therefore, the specific application determines the goal of picture enhancement. This paper briefly overviews picture enhancement methods that produce optimal and progressive outcomes for satellite images used for secured remote sensing. To do this, various image enhancement techniques are used, which are widely used today to improve image quality across various image processing applications. Some commonly used image enhancement techniques include spatial filtering, contrast stretching, and histogram equalisation. These techniques aim to enhance the visual quality of satellite images by adjusting brightness and contrast and reducing noise. These methods can also improve the interpretability of the images for remote sensing purposes. The enhancement of satellite images finds use in several fields, particularly security. It is essential for security applications, including threat detection, border control, and surveillance. Security professionals may more effectively analyse and understand data to spot any dangers or questionable activity by boosting the visual details and general quality. Keywords: Satellite image analysis; mean filter; secured application; SVM; wavelet transformation

Numerical study of satellite image quality in the presence of Seidel aberrations

AbstractWe present estimated images of a hypothetical satellite system in the presence of Seidel aberrations. We exemplified the effects of defocus, spherical, coma, astigmatism, field curvature, and distortion aberrations. Our analysis methods allow the investigation of aberration effects individually and in any combined form. Thereby, we introduce a potentially rich analysis dimension for the imaging chain, image processing, and image quality analyses. For a number of degrees of aberrations, we provide image estimates, show and quantify how individual (field‐dependent) aberrations would introduce degradations on the satellite images. We got correlation coefficient values for defocus, spherical, coma, astigmatism, field curvature, and distortion aberrations as 0.69, 0.71, 0.66, 0.79, 0.76, and 0.60, respectively. Thereby, we identfied the significance order of Seidel aberrations for this system by using image simulations and image similarity measures.

Automatically detect crosswalks from satellite view images – A deep learning approach with ground truth verification

Like roadway information is to motor vehicle safety, pedestrian facility information (e.g., sidewalk presence) is crucial towards improving the safety of these vulnerable road users. Yet unlike widely accessible roadway data, pedestrian facility data is unavailable for most state agencies. Without this information, data-driven problem identification, countermeasure analysis, project evaluation, and performance management will be heavily impeded. Thus, urgent need for this data was recognized by state departments of transportation (DOTs). To address this need, we developed an automated approach to automatically detect crosswalks from satellite view images. The most advanced deep learning methodology, transfer learning with a Convolutional Neural Network (CNN) was used to handle real-world images. During the prediction process, a satellite image of a roadway pavement was analyzed by the satellite view model to predict the presence of a crosswalk. Then, the street view image of the same target was detected by the integrated street view model as a ground truth check. A total of 18,361 images from Bing Maps in satellite view and street view were used to train and test the deep learning model. As a result, the satellite view model itself achieved 98.43% accuracy using testing data from the same region. When dealing with data from another region, using the satellite view detection with ground truth checking increased the accuracy by 49%. It is obvious that by integrating the ground truth checking model into the satellite view crosswalk detection, we can obtain a more robust model which can handle highly occluded, low quality satellite images.

Satellite Image Enhancement Using Histogram Equalization

Image enhancement is an indispensable technique in improving the quality, brightness, contrast and clarity of satellite images. The object that appears in images and variation caused by shadow, occlusion, camouflage in satellite images are the fundamental challenges posed by image enhancement techniques. The aim of this research work was to enhance satellite images of Sambisa using histogram equalization technique. MATLAB 2021 was used to implement the experiment. The results show that histogram equalization method has an excellent processing effect and it improved the brightness, contrast and clarity of the images as compared original images and the enhanced images.

Denoising Diffusion Probabilistic Feature-Based Network for Cloud Removal in Sentinel-2 Imagery

Cloud contamination is a common issue that severely reduces the quality of optical satellite images in remote sensing fields. With the rapid development of deep learning technology, cloud contamination is expected to be addressed. In this paper, we propose Denoising Diffusion Probabilistic Model-Cloud Removal (DDPM-CR), a novel cloud removal network that can effectively remove both thin and thick clouds in optical image scenes. Our network leverages the denoising diffusion probabilistic model (DDPM) architecture to integrate both clouded optical and auxiliary SAR images as input to extract DDPM features, providing significant information for missing information retrieval. Additionally, we propose a cloud removal head adopting the DDPM features with an attention mechanism at multiple scales to remove clouds. To achieve better network performance, we propose a cloud-oriented loss that considers both high- and low-frequency image information as well as cloud regions in the training procedure. Our ablation and comparative experiments demonstrate that the DDPM-CR network outperforms other methods under various cloud conditions, achieving better visual effects and accuracy metrics (MAE = 0.0229, RMSE = 0.0268, PSNR = 31.7712, and SSIM = 0.9033). These results suggest that the DDPM-CR network is a promising solution for retrieving missing information in either thin or thick cloud-covered regions, especially when using auxiliary information such as SAR data.

GF-1/6 Satellite Pixel-by-Pixel Quality Tagging Algorithm

The Landsat and Sentinel series satellites contain their own quality tagging data products, marking the source image pixel by pixel with several specific semantic categories. These data products generally contain categories such as cloud, cloud shadow, land, water body, and snow. Due to the lack of mid-wave and thermal infrared bands, the accuracy of traditional cloud detection algorithm is unstable when facing Chinese Gaofen-1/6 (GF-1/6) data. Moreover, it is challenging to distinguish clouds from snow. In order to produce GF-1/6 satellite pixel-by-pixel quality tagging data products, this paper builds a training sample set of more than 100,000 image pairs, primarily using Sentinel-2 satellite data. Then, we adopt the Swin Transformer model with a self-attention mechanism for GF-1/6 satellite image quality tagging. Experiments show that the model’s overall accuracy reaches the level of Fmask v4.6 with more than 10,000 training samples, and the model can distinguish between cloud and snow correctly. Our GF-1/6 quality tagging algorithm can meet the requirements of the “Analysis Ready Data (ARD) Technology Research for Domestic Satellite” project.

Cyanobacteria Index as a Tool for the Satellite Detection of Cyanobacteria Blooms in the Baltic Sea

Cyanobacteria blooms in the Baltic Sea have been studied for years due to their toxicity, which negatively affects all biota, along with the influence of these floating colonies on surface fluxes. However, mapping these blooms is still a challenge since their high dynamics, wide coverage, and specific radiometric footprint hinder atmospheric correction and negatively affect the quality of satellite images. In this study, we assessed the use of an alternative approach called the cyanobacteria index (CI), which is based on the reflectance spectral shape and which does not require comprehensive atmospheric correction. We demonstrated a close relationship between the positive CI values, indicating the presence of blooms, and the concentration of phycocyanin, the marker pigment of filamentous cyanobacteria in the Baltic Sea. We proved that the CI index could efficiently identify cyanobacteria-dominated blooms where colonies floated near the surface. Therefore, this index represents a valuable complement to the previous monitoring methods, suitable for extreme bloom events. The analysis of a time series of satellite images obtained between 2002 and 2018 using the CI index revealed the elongation of the bloom season, which may have been a consequence of the water temperature remaining within the cyanobacteria’s tolerance range for a longer time.

Assessment of micro-vibrations effect on the quality of remote sensing satellites images

ABSTRACT Recently, there is a growing interest in analysing the degrading effect of satellite micro-vibrations due to the rapid growth in satellite technologies and the urgent need to precisely extract a huge amount of information from satellite images. Different kinds of micro-vibration have a notable effect on the quality of satellite images. The main objective of this paper is to demonstrate and analyse the effect of all types of micro-vibration on the quality of images acquired by high-resolution satellites. An algorithm to simulate micro-vibrations is proposed. A very high-resolution satellite image from the Pleiades-neo satellite is selected as an example to be used in addressing the degrading effects of micro-vibrations. In this paper, the modulation transfer function (MTF) is used as a major function to model the degradation that has been conducted. Also, several quality metrics are used to quantitatively assess the degradation. The key result of this paper is the significant effect of micro-vibrations on the quality of remote sensing satellite images which is attributed to the main influential parameters. These parameters like blur diameter, vibration displacement, number of Time Delay and Integration (TDI) stages of the camera, and the ratio of the integration time to the vibration period.

Large-scale individual building extraction from open-source satellite imagery via super-resolution-based instance segmentation approach

Building footprint is a primary dataset of an urban geographic information system (GIS) database. Therefore, it is essential to establish a robust and automated framework for large-scale building extraction. However, the characteristic of remote sensing images complicates the application of the instance segmentation method based on the Mask R-CNN model, which ought to be improved toward extracting and fusing multi-scale features. Moreover, open-source satellite image datasets with wider spatial coverage and temporal resolution than high-resolution images may exhibit different coloration and resolution. This study proposes a large-scale building extraction framework based on super-resolution (SR) and instance segmentation using a relatively lower-resolution (>0.6 m) open-sourced dataset. The framework comprises four steps: color normalization and image super-resolution, scene classification, building extraction, and scene mosaicking. We took Hyogo Prefecture, Japan (19,187 km2) as a test area and extracted 1,726,006 (29.12 km2) of the 3,301,488 buildings (32.46 km2), where the number of buildings and footprint area increased by 3.0 % and 5.0 % respectively. The result indicated that the color normalization and image super-resolution could improve the visual quality of open-source satellite images and contribute to building extraction accuracy. Moreover, the improved Mask R-CNN based on Multi-Path Vision Transformer (MPViT) backbone achieved F1 scores of 0.71, 0.70, 0.81, and 0.67 for non-built-up, rural, suburban, and urban areas, respectively, which is better than those of the baseline model and other mainstream instance segmentation approaches. This study demonstrates the potential of acquiring acceptable building footprint maps from open-source satellite images, which has significant practical implications.

Long-term analysis of the urban heat island effect using multisource Landsat images considering inter-class differences in land surface temperature products

It is imperative to quantitatively analyze the long-term temporal and spatial characteristics of the urban heat island (UHI) effect on cities for applications, such as urban expansion and environmental protection. Owing to the high spatial resolution and availability of long time-series data, remote sensing images from Landsat satellites are widely used for land surface temperature (LST) retrieval. However, limited by the satellite revisit cycle and image quality, the use of multisource Landsat images in a long-term study of the UHI effect is inevitable. Nonetheless, owing to the differences among multisource sensors, such as Landsat-7 and Landsat-8, there may be apparent deviations in the LST results retrieved from different sensor data, which are obtained from the same area and under similar circumstances. Consequently, it is necessary to build a relationship between the LST results generated from multisource Landsat sensors for future research on the UHI effect.In this study, Shenzhen city was studied to explore the fitting relationship between the corresponding LST products from Landsat-7 and Landsat-8 images obtained from adjacent dates with similar climatic conditions. Furthermore, factors affecting the fitting models, such as land cover types, seasonal and inter-annual differences, were analyzed. The constructed fitting model had a strong relationship with land cover types but a relatively weak relationship with seasonal and inter-annual differences; this indicates that a pseudo Landsat-8-based LST product can be generated from a Landsat-7-based LST product using a model fitted by a Landsat-7/8 pair obtained from adjacent years (or different seasons). Finally, by considering the consistency between LST products from multisource Landsat images, the spatiotemporal variations in the UHI effect in Shenzhen can be accurately explored using long time-series data.

Primary drivers of multidecadal spatial and temporal patterns of shoreline change derived from optical satellite imagery

Understanding and predicting shoreline change along sandy coasts requires continuous (in both time and space) long-term (decades) shoreline data at good spatial (e.g. 100 s of metres) and temporal (e.g. months) resolution. Publicly available satellite imagery can now provide such time series. However, satellite-derived shorelines (SDS) are associated with uncertainties, particularly at high-energy meso-macrotidal coasts, which challenge the assessment of long-term trends and interannual variability. In this paper we address the 1984–2020 time- and space-evolution of 269 km of high-energy meso-macrotidal sandy coast in southwest France using uncertain (no tide and runup correction) SDS data. The shoreline trends are validated with field data collected over the period 2008–2019. Over 1984–2020, the shoreline eroded by 0.55 m/yr with maximum erosion (accretion) reaching 15.61 m/yr (6.94 m/yr), with the largest changes observed along coasts adjacent to the inlet and estuary mouths. We show that, away from the presence of ebb-tide deltas and swash bars affecting offshore wave transformation and nearshore circulation, the long-term shoreline trend is well explained by the gradients in longshore drift computed from a regional wave hindcast and an empirical longshore transport formula. By averaging the yearly SDS along the entire coastline, we find that interannual shoreline variability is well correlated with the winter West Europe Pressure Anomaly (WEPA), which outscores the other conventional teleconnection pattern indices. WEPA even explains >80 % of the space-averaged shoreline variability over the recent period 2014–2020 when more and higher quality satellite images are available. A more local assessment of the links between climate indices and shoreline response shows that correlation with all climate indices dramatically drops downdrift of the large-scale estuary mouths and inlets. This suggests that along this 10–20 km stretch of downdrift coast, shoreline response is controlled factors internal to the estuary mouth/inlet system. The rest of the coast is mostly controlled by factors external to the system, which are primarily the variability in winter-mean wave height correlated to winter WEPA index. Overall, we demonstrate that an adapted space-averaging of uncorrected (noisy) SDS dataset can allow addressing the time- and space variability of shoreline change and their primary drivers including large-scale climate patterns of atmospheric variability. We also advocate that such SDS analysis can be performed along any coastline in the world in order to guide future model development and application.

Multi-temporal satellite image correction based on CNN features and adaptive contrast enhancement

In recent years, identifying changes in multi-temporal images in terms of land use and land cover has been significant in a variety of applications, including urban planning. Due to weather and environmental effects, optical remote sensing has limitations in obtaining images where the image quality may be degraded. It’s because the images being registered are taken at various times, viewpoints, and types of sensors. In this article, the pre-processing methods, which include radiometric correction and geometric correction, are introduced to enhance the quality of satellite images and identify correct spatial alignment. For radiometric correction, adaptive contrast enhancement is done by combining histogram- and non-linear transfer function-based approaches in CIELAB color space. A comparison study is done to see how the new method compared to other methods. For geometric correction, the features from two images are extracted using Convolutional Neural Network to match and align them. The introduced approach for radiometric correction gave the best average rank of BRISQUE scores and RMSE of contrast scores, and the geometric correction can align two images together with an average accuracy of improvement of 91.78 percent. The findings of this research will provide the preliminary step for any change detection activities.

BRIGHTEARTH: PIPELINE FOR ON-THE-FLY 3D RECONSTRUCTION OF URBAN AND RURAL SCENES FROM ONE SATELLITE IMAGE

Abstract. With the growth of the availability and quality of satellite images, automatic 3D reconstruction from optical satellite images remains a popular research topic. Numerous applications, such as telecommunications and defence, directly benefit from the use of 3D models of both urban and rural scenes. While most of the state-of-the-art methods use stereo pairs for 3D reconstruction, such pairs are not immediately available anywhere in the world. In this paper, we propose an automatic pipeline for very-large-scale 3D reconstruction of urban and rural scenes from one high-resolution satellite image. Convolutional neural networks are trained to extract key semantic information. The extracted information is then converted into GIS vector format, and enriched by both terrain and object height information. The final classification step is applied, yielding a 16-class 3D map. The presented pipeline is operational and available for commercial purposes under the BrightEarth trademark.

Design and Development of Decorrelation Stretch Technique for Enhancing the Quality of Satellite Images with Improved MSE and UIQI in Comparison with Wiener Filter

Image enhancement is the process of increasing the quality of an image by enhancing certain features of an image and the resultant image is more suitable than the input image. This research is aimed at developing more efficient image enhancement techniques in the spatial domain. Materials and methods: Image enhancement using decorrelation stretch technique (N=30) is proposed in this work, and this method is compared with Wiener filter (N=30). The sample size used for both groups is 30. Results: The quality enhanced image is measured using Mean Square Error (MSE) and Universal Image Quality Index measure (UIQI). High UIQI and low MSE indicates the better enhanced image. The parameters of MSE (db) and UIQI are having higher values than the parameters of Wiener filter. The mean MSE value of decorrelation is 4.2715 db. Mean MSE of wiener filter is 32.0476 db. The mean UIQI value of decorrelation is 0.5640 db and that of Wiener filter is 0.5766 db. Conclusion: Image enhancement using decorrelation stretch technique is significantly performed better than Wiener filtering based on the experimental results and independent sample T test results using IBM-SPSS software.

Instance segmentation scheme for roofs in rural areas based on Mask R-CNN

Rooftop detection has numerous applications such as change detection in human settlements, land encroachments, planning routes to rural areas and estimation of solar generation potential of cities. Detecting the number, type and shape of building roofs form part of preliminary procedures to perform a variety of tasks for making decisions. Assessment of rooftops in rural areas is an important task in the estimation of potential solar generation and sizing of solar PV systems which has proven to be very challenging due to the different quality, lighting conditions and resolution of aerial and satellite images. In this research, we implement a mask RCNN algorithm using TensorFlow Object Detection API to detect the rooftop of buildings in a typical rural settlement. The average precision and recall values (@ IoU = 0.5:0.05:0.95) of the trained model were 85% and 88.2% respectively. The results of the experiment show that the approach can effectively and accurately detect and segment rural rooftops from high-resolution aerial images.

Distortion Disentanglement and Knowledge Distillation for Satellite Image Restoration

Satellite images are typically subject to multiple distortions. Different factors affect the quality of satellite images, including changes in atmosphere, surface reflectance, sun illumination, viewing geometries etc., limiting its application to downstream tasks. In supervised networks, the availability of paired datasets is a strong assumption. Consequently, many unsupervised algorithms have been proposed to address this problem. These methods synthetically generate a large dataset of degraded images using image formation models. A neural network is then trained with an adversarial loss to discriminate between images from distorted and clean domains. However, these methods yield suboptimal performance when tested on real images that do not necessarily conform to the generation mechanism. Also, they require a large amount of training data and are rendered unsuitable when only a few images are available. We propose a distortion disentanglement and knowledge distillation framework for satellite image restoration to address these important issues. Our algorithm requires only two images: the distorted satellite image to be restored and a reference image with similar semantics. Specifically, we first propose a mechanism to disentangle distortion. This enables us to generate images with varying degrees of distortion using the disentangled distortion and the reference image. We then propose the use of knowledge distillation to train a restoration network using the generated image pairs. As a final step, the distorted image is passed through the restoration network to get the final output. Ablation studies show that our proposed mechanism successfully disentangles distortion. Exhaustive experiments on different timestamps of Google-Earth images and publicly available datasets, LEVIR-CD and SZTAKI, show that our proposed mechanism can tackle a variety of distortions and outperforms existing state-of-the-art restoration methods visually as well as on quantitative metrics.

Road Identification Through Efficient Edge Segmentation Based on Morphological Operations

Road identification from high-precision images is important to programmed mapping, urban planning, and updating geographic information system (GIS) databases. Manual identification of roads is slow, costly, and prone to errors. Therefore, it is a hot topic among remote sensing experts to develop programmed techniques for road identification from satellite images. The main challenge lies in the variation of width and surface contents between roads. This paper presents a road identification and extraction strategy for satellite images. The strategy, denoted as ESMIRMO, recognizes roads in satellite images through edge segmentation, using morphological operations. Specifically, morphological operations were employed to enhance the quality of the original image, laying a good basis for accurate road detection. Next, edge segmentation was adopted to detect the road in the original image accurately. After that, the proposed strategy was compared with traditional methods. The comparison shows that our strategy could identify roads from satellite images more accurately than traditional methods, and overcome many of their limitations. Overall, our strategy manages to enhance the quality of satellite images, pinpoint roads in the enhanced images, and provide drivers and repairers with real-time information on road conditions.

Remote sensing estimation of the flood storage capacity of basin-scale lakes and reservoirs at high spatial and temporal resolutions

The flood storage of lakes and reservoirs plays an important role in flood regulation and control in floodplains. However, the flood storage capacity of lakes and reservoirs is ineffectively quantified at the basin scale due to the limited access to in-situ data and poor quality of optical satellite images in flooding seasons. To address this, taking a typical floodplain basin (the Poyang Lake basin) in the Yangtze as a study case, radar satellite data combined with measured bathymetry and digital elevation model data were utilized to reconstruct the time series of the water inundation area and water storage change of all lakes and reservoirs larger than 1 km2 during the once-in-a-generation flood event that occurred in 2020 (termed as the 2020 flood event hereafter). Results show that the flood storage capacity of Poyang Lake can reach the maximum at 12.18 Gt, and that for other lakes and reservoirs within the basin is approximately 2.95 Gt. It indicates a total flood-storage capacity of 15.13 Gt for the basin-scale lakes and reservoirs, approximately accounting for 45.02% of the terrestrial water storage change of the basin. The storage capacity of Poyang Lake was approximately four times larger than the entirety of other lakes and reservoirs in the basin despite that its maximum water inundation area is in the proportion of 2.58 times other water bodies. This finding indicates that the Poyang Lake provided the dominant contribution to flood storage among all the lakes and reservoirs in the basin. This study introduced a remote sensing approach to quantify the flood storage capacity of basin-scale lakes and reservoirs at high spatial and temporal resolutions during the flood event, which could fill the insufficiently-quantified knowledge about dynamics of lakes and reservoirs in areas lacking full-covered in-situ data records. This study also helps to offer a quantitative basis to improve flood forecasting and control for the public authority, stakeholders, and decision-makers.