Nighttime Images Research Articles

VIIRS Night-Time Light Image in the Spatial Structure Identification of Banyuwangi Regency

A spatial structure is a hierarchical arrangement of settlement centers and a network system of infrastructure and facilities that support the community’s socio-economic activities. Spatial structure is important because the city’s rapid growth requires regional service center planning. In general, the method for determining the spatial structure uses the Centrality Index (IS) with a variable number of facilities, infrastructure, and people to obtain a hierarchical arrangement of regional service centers. However, this method is often constrained by data limitations, so in this study remote sensing technology was used through night light imagery in identifying potential centers. This study used the Visible and Infrared Imaging Radiometer Suite (VIIRS) sensor, one of the sensors carried by the Suomi-NPP (National Polar-orbiting Operational Environmental Satellite System Preparatory Project) satellite. Identification is done by overlaying the night light image with the existing spatial structure to produce potential centers. Based on the analysis results, eight classifications are based on the light emission interval class, where the higher the class, the higher the light emission value, which indicates the potential for a new center. The Pesanggaran sub-district is in class eight, the Tegaldimo sub-district is in class six, and the Banyuwangi sub-district is in class one.

Satellite Time-Series Analysis for Thermal Anomaly Detection in the Naples Urban Area, Italy

Naples is the most densely populated Italian city (7744 inhabitants per km2). It is located in a particular geological context: the presence of Mt Vesuvius characterizes the eastern part, and the western part is characterized by the presence of the Phlegrean Fields, making Naples a high-geothermal-gradient region. This endogenous heat, combined with the anthropogenic heat due to intense urbanization, has defined Naples as an ideal location for Surface Urban Heat Island (SUHI) analysis. SUHI analysis was effectuated by acquiring the Land Surface Temperature (LST) over Naples municipality by processing Landsat 8 (L8) Thermal Infrared Sensor (TIRS) images in the 2013–2023 time series by employing Google Earth Engine (GEE). In GEE, two different approaches have been followed to analyze thermal images, starting from the Statistical Mono Window (SMW) algorithm, which computes the LST based on the brightness temperature (Tb), the emissivity value, and the atmospheric correction coefficients. The first one is used for the LST retrieval from daytime images; here, the emissivity component is derived using, firstly, the Normalized Difference Vegetation Index (NDVI) and then the Vegetation Cover Method (VCM), defining the Land Surface Emissivity (LSɛ), which considers solar radiation as the main source of energy. The second approach is used for the LST retrieval from nighttime images, where the emissivity is directly estimated from the Advance Spaceborne Thermal Emission Radiometer database (ASTER-GED), as, during nighttime without solar radiation, the main source of energy is the energy emitted by the Earth’s surface. From these two different algorithms, 123 usable daytime and nighttime LST images were downloaded from GEE and analyzed in Quantum GIS (QGIS). The results show that the SUHI is more concentrated in the eastern part, characterized by intense urbanization, as shown by the Corine Land Cover (CLC). At the same time, lower SUHI intensity is detected in the western part, defined by the Land Cover (LC) vegetated class. Also, in the analysis, we highlighted 40 spots (10 hotspots and 10 coldspots, both for daytime and nighttime collection) that present positive or negative temperature peaks for all the time series. Due to the huge amount of data, this work considered only the five representative spots that were most representative for SUHI analysis and determination of thermal anomalies in the urban environment.

Estimating the relationship between ethnic inequality, conflict and voter turnout in Africa using geocoded data

To what extent is voter turnout influenced by ethnic inequality? We combine ethnolinguistic maps on sub-national locations of ethnic groups with night-time light satellite images to construct a dataset of ethnic inequality across sub-national locations in 24 African countries. Using election data from the constituency-level elections archive, we show that ethnic inequality is negatively associated with voter turnout. This relationship holds even after we control for endogeneity and geographic characteristics. We also construct a dataset of election conflict across the sub-national areas. We find that an increase in ethnic inequality is associated with an increase in conflict, which reduces voter turnout.

Lighting characteristics of public space in urban functional areas based on SDGSAT-1 glimmer imagery:A case study in Beijing, China

The artificial light sources in urban areas constitute the primary source of stable nighttime illumination, which can be utilized to reflect the nighttime human activity and the conditions of public space lighting. The previous studies have focused on the evaluation of lighting environments in specific scenarios and the public's perception of these lighting environments. There has been limited analysis of the characteristics of public space lighting and its influencing factors in detail across different urban functional areas. This study primarily utilizes high-resolution SDGSAT-1 glimmer imagery and data from field measurements of lighting attributes to analyze the characteristics of public space lighting and its influencing factors in various urban functional areas. Furthermore, it establishes a quantitative relationship between measured illuminance and glimmer imagery to assess public space lighting levels. The results show that: (1) Various urban functional areas exhibit different illuminance levels, but they all maintain good illuminance uniformity. Additionally, to reduce the impact of the blue light spectrum on public visual perception, urban public space lighting primarily utilizes warm color temperatures with low color rendering properties. However, nighttime public space lighting cannot always achieve a balance between public comfort and high color rendering; (2) In the main urban area of Beijing, 98.5% of illuminance values fall within the range of 0 to 35 lx, indicating that most areas have good public lighting conditions. However, cases of over-illumination are also observed; (3) The building height has a positive correlation with public space lighting, while vegetation density exhibits a negative correlation. This study explored the characteristics of public space lighting in different urban functional areas and conducted a large-scale analysis of urban public lighting conditions using high-resolution nighttime images, providing evidence for the design and optimization of public space lighting in cities and better serving urban sustainable development, and contributing to the achievement of UN Sustainable Development Goal 11.7: By 2030, provide universal access to safe, inclusive, accessible, green and sustainable public spaces, particularly for women, children, older persons and persons with disabilities.

Development of Multi Modal Image Fusion Techniques in Night Vision Technology Using Deep Learning

This study is aimed at exploring new development in nighttime image processing based on nighttime photography/night vision. We use various learning methodologies based on neural networks to develop multisensor data processing techniques. The ability of Convolutional Neural Networks (CNN) to enhance image quality and clarity under conditions of low light or at night is the focus of our research. To do this A large database has been created, containing 3200 images both taken from the internet as well as our own. This data is preprocessed, normalized and features expanded using some common methods of data augmentation in order to optimize model performance. We compared our proposed CNN model with existing literature through a series of experiments. Performance was measured using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM). The Results are now in. They showed that our model can improve various metrics with higher scores. And they show the real-time monitoring capabilities in urban streets for our model. This research brings multisensor image fusion based on night-adjacent techniques forward from night vision technology and provides insight into practical and potential future directions for using deep learning techniques to improve safety and security in many different environments.

Alignment and fusion for adaptive domain nighttime semantic segmentation

In the field of autonomous driving technology, both daytime and nighttime scenes are common. However, due to the poor illumination and difficulty in manual annotation of nighttime images, semantic segmentation of nighttime scenes is more challenging compared to daytime scenes. Therefore, achieving significant progress in nighttime semantic segmentation would greatly enhance the effectiveness of the application of autonomous driving scenarios. In this work, we investigate the problem of domain-adaptive nighttime semantic segmentation (DANSS). The problem aims to learn semantic segmentation in nighttime scenes by leveraging a labeled Cityscapes dataset and unlabeled but roughly aligned day-night image pairs. To address this, we propose an Align and Fusion Network (AAFnet), a network for adaptive domain nighttime semantic segmentation. AAFnet utilizes a novel DAFormer as the backbone to separately compute features for dynamic and static objects during the training process. It also employs methods such as small category sampling and image blending to improve learning effectiveness. Additionally, we propose utilizing local image patches to enhance the results during the testing process. Experimental results demonstrate a significant improvement of 10.4% compared to the previous method, DANNet. Extensive experiments conducted on the Dark Zurich dataset and Nightdriving dataset validate the effectiveness of the proposed approach. Our method outperforms previous backbones and achieves top-ranking results.

Night Time Image Enhancement

Night time image enhancement plays a crucial role in various applications such as surveillance, autonomous driving, and photography. However, capturing high-quality images in low-light conditions remains challenging due to limited visibility and increased noise levels. In this project, we propose a novel approach for enhancing nighttime images using MIRNet, a state-of-the-art deep learning architecture specifically designed for low-light image enhancement tasks. We collect a dataset of low-light images paired with their corresponding well-exposed counterparts and train the MIRNet model to learn the mapping between the two modalities. The architecture of MIRNet incorporates convolutional layers with residual connections to effectively capture low-light image features and generate visually pleasing enhancements. We evaluate the performance of our approach on a diverse range of nighttime scenes and compare the results against existing methods. Our experiments demonstrate that MIRNet produces superior results in enhancing nighttime images, significantly improving visibility, reducing noise, and preserving image details. The proposed approach holds promise for real-world applications where high-quality nighttime imagery is essential for decision-making and visual analysis. Keywords: Night time image enhancement, MIRNet, Deep learning, Low-light imaging, Image Processing,Convolutional neural networks (CNNs),Residual connections, Supervised learning, Dataset preparation,Imagequalityimprovement,Noisereduction,Visibilityenhancement,Surveillance,Autonomousdriving,Photography.

Spatiotemporal Analysis of Carbon Emissions Based on Night-time Light Data in Western Provinces of China

This study focuses on the fusion and calibration of DMSP–OLS and NPP–VIIRS night-time light data, sourced from the National Geophysical Data Centre in the United States. A long-time series night-time light dataset, spanning from 1999 to 2019, for the western provinces of China is created. In conjunction with carbon emission statistics from these 11 western provinces, an estimation model is constructed to analyse the changes in the spatiotemporal pattern of carbon emissions within the region, revealing the characteristics of carbon emissions caused by human night-time economic activities in western China, and provides theoretical reference for the formulation of energy conservation and emission reduction policies. Results show that: – A strong correlation coefficient of 0.9067 exists between carbon emissions and the total digital number (DN) value of night-time light in the western provinces, with a negligible average relative error, and this result indicates the effectiveness of the estimation model; – The study reveals an increasing trend in carbon emissions across all 11 provinces from 1999 to 2019, and this growth forms a radial expansion pattern centred around the provincial capitals of Sichuan, Shaanxi, and Inner Mongolia; – By integrating night-time light images and calculated carbon emissions through the estimation model, a significantly positive spatial correlation of carbon emissions is discernible. This outcome presents as a high carbon agglomeration area in the Inner Mongolia Autonomous Region and a low carbon agglomeration area in Qinghai Province. On the basis of these findings, the study proposes transformation measures to promote low carbon emissions in China’s western provinces. These practical suggestions provide a point of reference for China as it aims to meet its “carbon neutrality” and “peak carbon emissions” targets.

DDFL: Dual-Domain Feature Learning for nighttime semantic segmentation

Nighttime semantic segmentation has been playing a critical role in intelligent transportation, building safety and urban management. However, nighttime scenes present some challenges such as complex structures, multiple light sources, uneven lighting and blurry image noise, which severely degrade the segmentation quality of nighttime images. To address these challenges, we propose a Dual-Domain Feature Learning (DDFL) model for nighttime semantic segmentation. Our approach introduces three innovative ideas. First, we establish an exposure correction module to address the impact of lighting differences on the model’s learning, so as to maximally restore the pixel distortion and blurry areas caused by artificial light in nighttime scenes. Second, we incorporate frequency domain information into the nighttime segmentation task to give the model stronger discrimination ability. Finally, we introduce a dual-domain fusion module to complement the information of learning from the spatial and frequency domains in a cross-fusion manner, enabling the network to perceive semantic information while preserving details. The proposed model was experimentally tested on the Nightcity, Nightcity+ and BDD100k datasets. Our results demonstrate that our model outperforms mainstream models, achieving mIoU scores of 56.73%, 57.41% and 28.97%, respectively, under different lighting, image exposure levels, and resolutions. These results show that our model is capable of segmenting nighttime scenes efficiently in a high-quality way.

MFHOD: Multi-modal image fusion method based on the higher-order degradation model

The task of multimodal image fusion aims to preserve the respective advantages of each modality, such as the detailed texture information from visible light images and the salient target information from infrared images. However, images in real environments are often not perfect high-quality images but are affected by various degradation factors such as noise, blur, and glare. Existing unsupervised multimodal image fusion algorithms use undegraded source images as input to fusion networks and generate high-quality fusion images through complex fusion strategies. This single linear learning approach from high-quality to high-quality cannot be applied to the fusion task of degraded multimodal images in real environments. To address this issue, this paper proposes an end-to-end multimodal image fusion network based on a high-order local degradation model (MFHOD). Firstly, inspired by the idea of probabilistic degradation, we propose a high-order local random degradation model (HODM), which inputs the source multimodal images into the degradation model to obtain degraded images before feeding them into the network. Secondly, we design a simple and efficient dual-branch feature extraction encoder to extract deep features from images. Then, from the perspectives of image pixels, brightness, and gradients, we propose an improved composite loss composed of multiple loss functions to constrain network training. Finally, we propose a L2-norm fusion strategy for preserving brightness information in low-light nighttime images. Our MFHOD demonstrates good performance on infrared and visible light image datasets as well as medical image datasets. Experimental results show that MFHOD can effectively suppress the effects of glare, noise, and smoke in adverse environments, and also improve the quality of fusion images in low-light and nighttime environments.

White balancing based improved nighttime image dehazing

White balancing based improved nighttime image dehazing

DNIT: Enhancing Day-Night Image-to-Image Translation through Fine-Grained Feature Handling (Student Abstract)

Existing image-to-image translation methods perform less satisfactorily in the "day-night" domain due to insufficient scene feature study. To address this problem, we propose DNIT, which performs fine-grained handling of features by a nighttime image preprocessing (NIP) module and an edge fusion detection (EFD) module. The NIP module enhances brightness while minimizing noise, facilitating extracting content and style features. Meanwhile, the EFD module utilizes two types of edge images as additional constraints to optimize the generator. Experimental results show that we can generate more realistic and higher-quality images compared to other methods, proving the effectiveness of our DNIT.

Seasonal variation in atmospheric optical depth (AOD) and thermal inertia (TI) inter-relationship over Martian Gale crater

Investigating the relationship between thermal inertia (TI) and aerosol optical depth (AOD) is significant in giving insights into the seasonality of dust deposition and lifting phenomenon. The present study focuses on establishing a relationship of AOD with TI and different particle sizes over different Martian seasons. Two different Martian landforms (exposed rock and sand dunes) have been used to establish these relationships. TI layer was generated using THEMIS nighttime images for different seasons, whereas Curiosity Rover measured AOD values and Mars Climate database (MCD) visible column dust optical depth were used to derive rover equivalent AOD. An inverse relation was observed between AOD and TI for exposed rock and sand dune regions for all the seasons with low to moderate coefficient of determination (R2). A similar inverse trend was observed between rover equivalent AOD and particle size with R2 values ranging from 0.8 in the case of sand dunes (winter) to 0.93 in exposed rock (autumn). The results were further compared within the AOD obtained from orbiter image (HRSC) derived using Shadow method for spring season (Shaheen et al., 2022). The same inverse relation was found within TI having good R2 values of 0.61 for exposed rock and 0.76 for the sand dunes. Error estimation using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Normalized Mean Square Error (NMSE), Fractional Bias (FB), Index of agreement errors was carried out for TI vs. AOD and particle size vs. AOD. Excellent statistical significance was obtained for AOD and particle size, in the case of sand dunes it was 0.96 for autumn and 0.99 in case of exposed rock for spring season, respectively.

Nighttime color constancy using robust gray pixels.

Color constancy is a basic step for achieving stable color perception in both biological visual systems and the image signal processing (ISP) pipeline of cameras. So far, there have been numerous computational models of color constancy that focus on scenes under normal light conditions but are less concerned with nighttime scenes. Compared with daytime scenes, nighttime scenes usually suffer from relatively higher-level noise and insufficient lighting, which usually degrade the performance of color constancy methods designed for scenes under normal light. In addition, there is a lack of nighttime color constancy datasets, limiting the development of relevant methods. In this paper, based on the gray-pixel-based color constancy methods, we propose a robust gray pixel (RGP) detection method by carefully designing the computation of illuminant-invariant measures (IIMs) from a given color-biased nighttime image. In addition, to evaluate the proposed method, a new dataset that contains 513 nighttime images and corresponding ground-truth illuminants was collected. We believe this dataset is a useful supplement to the field of color constancy. Finally, experimental results show that the proposed method achieves superior performance to statistics-based methods. In addition, the proposed method was also compared with recent deep-learning methods for nighttime color constancy, and the results show the method's advantages in cross-validation among different datasets.

Latent domain knowledge distillation for nighttime semantic segmentation

Despite significant progress made in image semantic segmentation, most research has primarily focused on daytime scenes. Semantic segmentation of nighttime images is equally critical for autonomous driving; however, this task poses more challenges due to inadequate lighting and difficulties associated with obtaining accurate manual annotations. In this paper, we propose a novel method called Latent Domain Knowledge Distillation (LDKD) for accurate nighttime semantic segmentation. Within a teacher-student framework, our LDKD combines domain alignment and knowledge distillation in a mutually reinforcing manner. First, we introduce a bidirectional photometric alignment module capable of generating daytime-like and nighttime-like latent images in real-time. This module effectively bridges the appearance gap between the source domain (daytime) and the target domain (nighttime). Second, we enhance collaboration between the teacher and student networks. The student network unifies image-level and feature-level domain alignment, while the teacher network generates reliable pseudo-labels by distilling knowledge from the latent domain. Furthermore, to account for potential noise in pseudo-labels, we propose a noise-tolerant learning method aimed at mitigating the risks associated with overreliance on pseudo-labels during model training. Extensive experiments on Dark Zurich and ACDC datasets show that our LDKD achieves state-of-the-art performance, demonstrating the effectiveness of our method for nighttime semantic segmentation.

Progressive Bidirectional Feature Extraction and Enhancement Network for Quality Evaluation of Night-Time Images

Blind image quality assessment (BIQA) has received increasing attention in the past decades. However, it still remains inadequately researched on BIQA for night-time images suffering from the diverse authentic degradations. Since the intrinsic content degradations of night-time images are highly related to the illumination, how to use the connection between content and illumination to enhance the feature representation ability is the key issue in designing BIQA methods for night-time images. In this paper, we first construct an ultra-high-definition night-time image dataset (UHD-NID) with high image resolution and abundant parameter settings. UHD-NID contains 1600 images with a high resolution of 5616×3744, and each group of images contains ten exposure levels. Then, we conduct subjective assessment and analyze the subjective data to obtain a mean opinion score to each image in UHD-NID. To enhance the feature representation ability in content and illumination, we propose a progressive bidirectional feature extraction and enhancement network (PBFEE-Net). In addition, we use a decomposition network to decompose the input image into the reflectance and illumination, which can facilitate the ability of feature extraction to some extent. The experimental results show that our proposed method achieves superior performance in evaluating the quality of night-time images. The dataset and code will be released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/NBUsjl/UHD-NID</uri> .

Illumination Distillation Framework for Nighttime Person Re-Identification and a New Benchmark

Nighttime person Re-ID (person re-identification in the nighttime) is a very important and challenging task for visual surveillance but it has not been thoroughly investigated. Under the low illumination condition, the performance of person Re-ID methods usually sharply deteriorates. To address the low illumination challenge in nighttime person Re-ID, this paper proposes an Illumination Distillation Framework (IDF), which utilizes illumination enhancement and illumination distillation schemes to promote the learning of Re-ID models. Specifically, IDF consists of a master branch, an illumination enhancement branch, and an illumination distillation module. The master branch is used to extract the features from a nighttime image. The illumination enhancement branch first estimates an enhanced image from the nighttime image using a nonlinear curve mapping method and then extracts the enhanced features. However, nighttime and enhanced features usually contain data noise due to unstable lighting conditions and enhancement failures. To fully exploit the complementary benefits of nighttime and enhanced features while suppressing data noise, we propose an illumination distillation module. In particular, the illumination distillation module fuses the features from two branches through a bottleneck fusion model and then uses the fused features to guide the learning of both branches in a distillation manner. In addition, we build a real-world nighttime person Re-ID dataset, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Night600</i> , which contains 600 identities captured from different viewpoints and nighttime illumination conditions under complex outdoor environments. Experimental results demonstrate that our IDF can achieve state-of-the-art performance on two nighttime person Re-ID datasets ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Night600</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Knight</i> ). We will release our code and dataset at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Alexadlu/IDF</uri> .

NiteDR: Nighttime Image De-Raining with Cross-View Sensor Cooperative Learning for Dynamic Driving Scenes

NiteDR: Nighttime Image De-Raining with Cross-View Sensor Cooperative Learning for Dynamic Driving Scenes

ILLUMINATION ENHANCEMENT OF NIGHTTIME IMAGES USING A REGULATED SINGLE SCALE RETINEX ALGORITHM

People are active during the nighttime and capture many photos. Due to the low-light nature of the environment, captured images tend to appear dimmed with imbalanced illumination, low contrast, noise, and a lack of vibrant colors. For this purpose, this paper presents a direct and practical approach to improving the lighting of night images based on the single-scale retinex model and using image processing methods and other statistical approaches. The proposed algorithm begins by converting the image from the RGB to the HSI model. Then, it enhances only the I channel while preserving the H and S channels. Thus, processing on the I channel begins with estimating the illumination version of the image and calculating the logarithms of both the illuminated and original image, like the SSR model. Then, it subtracts these two images using a subtraction of logarithmic image processing. Subsequently, the cumulative distribution function of the Gumble probability distribution is applied, and the resulting output is further treated using a logarithmic transformation method. That produces the processed I channel, which combines with the conserved H and S channels to give the HSI image. Ultimately, it converts the image to RGB format. The proposed algorithm is applied to two datasets of nighttime images, compares their performance to seven different contemporary algorithms, and evaluates the results of the compares using three specific metrics of quality evaluation based on the results generated, deducing that the suggested algorithm enhanced the brightness of night images and surpassed other algorithms in terms of execution time, and image assessment methods. Additionally, it exceeded them in all metrics used.

Optimising camera and flight settings for ultrafine resolution mapping of artificial night-time lights using an unoccupied aerial system

Light pollution from artificial night-time lights (ANTLs) is a global health, economic, and environmental issue. Remote sensing and unoccupied aerial systems (UASs) provide efficient and cost-effective ways to study ANTL spatial patterns and dynamics over large areas. With ultrahigh-resolution images that can identify individual light sources, UAS offers more detailed image than satellite imagery. However, standardisation and optimisation of camera and flight settings during the acquisition ANTL UAS images is lacking. The aim of this paper is to determine the camera and flight settings to capture high-quality ANTL using a DJI Matrice 300 RTK aircraft with a Zenmuse P1 camera. It emphasises the importance of selecting appropriate camera settings for high-quality ANTL images, which can benefit future research. Results show significant image quality gains when camera and flight settings are chosen appropriately in relation to the lighting conditions. We present three experiments demonstrating a range of camera settings, and we provide practical recommendations for high-quality night-time image collection. The optimal camera settings were determined to be an exposure time of 0.0166 s, ISO of 25600, and aperture of 2.97. This experiment produced outstanding results, with 85% of images having a blur extent below 0.40.