Panchromatic Sensor Research Articles

Joint Panchromatic and Multispectral Geometric Calibration Method for the DS-1 Satellite

The DS-1 satellite was launched successfully on 3 June 2021 from the Taiyuan Satellite Launch Center. The satellite is equipped with a 1 m panchromatic and a 4 m multispectral sensor, providing high-resolution and wide-field optical remote sensing imaging capabilities. For satellites equipped with panchromatic and multispectral sensors, conventional geometric processing methods in the past involved separate calibration for the panchromatic sensor and the multispectral sensor. This method produced distinct internal and external calibration parameters in the respective bands, and also resulted in nonlinear geometric misalignments between the panchromatic and multispectral images due to satellite chattering and other factors. To better capitalize on the high spatial resolution of panchromatic imagery and the superior spectral resolution of multispectral imagery, it is necessary to perform registration on the calibrated panchromatic and multispectral images. When registering separately calibrated panchromatic and multispectral images, poor consistency between panchromatic and multispectral images leads to a small number of corresponding points, resulting in poor accuracy and registration effects. To address this issue, we propose a joint panchromatic and multispectral calibration method to register the panchromatic and multispectral images. Before geometric calibration, it is necessary to perform corresponding points matching. When matching, the small interval between the panchromatic and multispectral Charge-Coupled Devices (CCDs) results in a small intersection angle of the corresponding points between the panchromatic and multispectral images. As a result of this, the consistency between the spectral bands significantly improves, and the corresponding points match to have a more uniform distribution and a wider coverage. The technique enhances the consistent registration accuracy of both the panchromatic and multispectral bands. Experiments demonstrate that the joint calibration method yields a registration accuracy of panchromatic and multispectral bands exceeding 0.3 pixels.

Lyzenga Algorithm for Shallow Water Mapping Using Multispectral Sentinel-2 Imageries in Gili Noko Waters

Bathymetric survey is an essential approach for gathering marine geospatial information. The use of an echosounder can provide highly accurate bathymetric data, yet it is expensive and time-consuming. To achieve an accurate chart of shallow water areas is challenging as they are difficult to access by large survey vessels due to their draft limitation. Bathymetric survey can also be carried out by using an Unmanned Surface Vehicles; however, it is restricted to small and calm areas. Thus, a Satellite Derived Bathymetry (SDB) come as a solution to take the place of bathymetric survey using an echosounder, especially in a shallow water with many natural or man-made obstacles. This study utilises Sentinel-2 image data, an optical satellite imagery to determine depth values of shallow water area using SDB approach. Several algorithms have been developed to collect depth values using the SDB. One of the algorithms is Lyzenga algorithm which aims to simplify the process of extracting water depths by comparing water reflectance factors using three bands of panchromatic sensor imagery. This study applied the Lyzenga algorithm to determine water depths using multiple linear regression and it is validated using an in-situ data. The RMSE and MAE values is 1.711m and 1.254m, respectively whilst the correlation coefficient is 0.946.

Quantifying the Variability of Ground Light Sources and Their Relationships with Spaceborne Observations of Night Lights Using Multidirectional and Multispectral Measurements.

With the transition to LED lighting technology, multispectral night-time sensors are needed to quantify the changing nightscapes, given the limitations of the panchromatic sensors. Our objective was to quantify the contribution of lighting sources as measured on the ground and examine their correspondence with night-time brightness and color as measured from space. We conducted ground-based measurements of night-time brightness using the multidirectional (top, rear, right, front, left) and multispectral LANcube v2, which was mounted on the roof of a car, over 458 km of roads in central Israel and in Brisbane, Australia. For spaceborne measurements, we used the SDGSAT-1 multispectral Glimmer sensor. We found that spaceborne measurements of apparent radiance were best explained when including all ground-based directional measurements, with greater explanatory power for highways (R2 = 0.725) than for urban roads (R2 = 0.556). Incoming light in the five directions varied between road classes and land use. In most cases, the variability in night-time brightness and color was greater for urban road sections than for highways. We conclude that due to the spectral mixture of lighting sources, at a medium spatial resolution, the impact of the transition to LED lighting may be more easily recognized from space over highways than in dense urban settings.

Multimodal Sensors Image Fusion for Higher Resolution Remote Sensing Pan Sharpening

Panchromatic (PAN) sensors and multispectral (MS) sensors are usually applied to remote sensing tasks. Pan sharpening is an effective image fusion method based on multimodal sensors. In pan sharpening, high-resolution (HR) PAN images from PAN sensors and corresponding low-resolution MS images from MS sensors are used to obtain the HR MS images. Normally, the ideal pan-sharpening image is a fused MS image with the same resolution as the PAN image. We hope to obtain the pan-sharpening images with higher resolution, which will not only preserve spatial structure information and spectral information from PAN images and MS images but also achieve super-resolution reconstruction. For this purpose, a novel pan-sharpening framework, called multilevel and multiscale fusion network (MLMSFN), is constructed. To our knowledge, our framework is the first work to achieve beyond the limit of existing image resolution in pan sharpening. We evaluate the effectiveness of our designed method, and the experiments testify that our method can obtain higher resolution pan-sharpening images.

Analyzing spatial variability in night-time lights using a high spatial resolution color Jilin-1 image – Jerusalem as a case study

In recent decades, there has been an increase in artificial lighting in the world due to urbanization and the revolution of LED lighting. Artificial lighting is an indicator of human activity, but can adversely affect natural ecosystems and people due to negative impacts of light pollution. Space-borne and airborne imagery as well as ground-based measurements enable to measure the intensity and spectra of artificial lights. One of the challenges in remote sensing of night-time lights is how to ground truth night-time imagery acquired by satellites, and how much do space-borne measurements represent the brightness as perceived by organisms. Most of the studies on night-time lights to-date were done using panchromatic sensors at large spatial extents, which did not allow to examine intra-urban variation in night light intensity and spectra. The aim of this study was to test the capability of the new Chinese satellite Jilin-1, which is the first commercial satellite to offer multispectral night-light imagery at a spatial resolution below 1 m, to characterize the night-time properties of urban areas. We examined the correspondence between light intensities as measured from different sensors at different spatial resolutions: two Jilin-1 images of the Jerusalem metropolitan area (0.89 m), VIIRS/DNB (500 m), Loujia-1 (130 m), unmanned aerial vehicle (UAV) color image (0.05 m) and hemispherical color photographs taken by a calibrated ground DSLR (digital single-lens reflex camera). In all the comparisons between different remote sensing tools, as the spatial resolution coarsened, the Pearson correlation coefficient increased, reaching r > 0.5 (after resampling to 100 m). Stronger correlations were found for the red band, and weaker correlations were found for the blue band, probably due to atmospheric scattering. By identifying specific objects such as buildings and lightings, we found good correspondence (R2=0.51) between Jilin-1 and the ground-based measurements of night-time brightness. We further examined the variability of night lights within different land use types and within different ethnic/religion composition of statistical areas. We found that residential areas of Orthodox Jews were characterized with the highest brightness at night compared with residential areas of Arabs in the West Bank that had the lowest brightness. At the statistical zone level (n = 299), more than 50% of the variability in night-time brightness, was explained by land cover properties (NDVI), infrastructure (roads and built volume) and the ethnic/religious composition. In addition, we found that the spectral ratio index which was based on the red and green bands, enabled to better distinguish between land use classes, than the spectral ratio index which was based on the green and blue bands. The availability of night-time multi-spectral imagery at fine spatial resolution now enables to study urban land-use and spatial inequality, and to better understand the factors explaining night-time brightness.

Model-Based Reduced-Rank Pansharpening

Observation of the Earth using satellites mounted with optical sensors is an important application of remote sensing. Owing to physical constraints, multispectral (MS) sensors acquire images of lower spatial resolution than a single-band panchromatic (PAN) sensor that acquires images of the same scene. Pansharpening fuses the MS and PAN images to obtain an MS image with the same spatial resolution as the PAN image. In this letter, we propose to expand a method, initially developed for Sentinel-2 single-sensor sharpening, for pansharpening. The expanded method is based on solving a non-convex MS acquisition model using optimization methods based on cyclic decent and manifold optimization. The tuning parameters of the method are chosen using Bayesian optimization with reduced-scale evaluation. The proposed method is compared with a number of established pansharpening methods and is validated using both synthetic and real data sets.

Hyper-Sharpening Based on Spectral Modulation

Hyperspectral (HS) image sharpening (namely hyper sharpening) with an auxiliary sensor, such as multispectral (MS) or panchromatic sensor, has attracted a great deal of attention for the past decade. A number of hyper-sharpening techniques, aiming at enhancing the spatial resolution of HS images, have been developed and demonstrated their effectiveness especially on synthetic or simulated data. Nevertheless, since the differences between different remote sensing systems or imaging conditions are complicate, it results in a serious spectral distortion when applying on real remote sensing data acquired by different sensors under different acquisition times or conditions. Unfortunately, very few works have considered this issue. In this paper, a new hyper-sharpening framework based on spectral modulation is proposed to better preserve spectral information when fusing with MS data acquired by a different sensor. The goal of this paper is to generate an adjusted MS image that would have been observed under the same imaging conditions with the corresponding HS sensor. Two approaches, originating in MS pan-sharpening field, are introduced as examples under this framework, namely high-pass details injection model and band-dependent spatial-detail model. Experiments on three HS and MS datasets acquired by different platforms demonstrate that the proposed framework is beneficial to the spectral fidelity of the fused image compared with some state-of-the-art hyper-sharpening techniques. Meanwhile, it is also easy to implement and has a certain advantage in enhancing the spatial details.

Influencing Factors and Outcomes of Health Information Seeking Behavior According to Platform: Focusing on Socioeconomic Factors, Digital Media Literacy, Social Support, and Life Satisfaction

This study presents an approach to synthesize hyperspectral images of lower resolution at a higher resolution using the high resolution images acquired from a sensor of commercial satellites. The proposed method was applied to the reconstruction of EO-1 Hyperion images using the images acquired from IKONOS sensor. Based on the FitPAN-Mod pansharpening technique (Lee, 2008b), the hyperspectral images of 30m resolution were reconstructed at 1m resolution of IKONOS panchromatic image. In this study, the synthesized hyperspectral images of 50 bands, whose wavelengths range in the wavelength of panchromatic sensor, were generated from the three stages of high resolution reconstruction using FitPAN-Mod. The experimental results show that the proposed method effectively integrates the spatial detail of the panchromatic modality as well as the spectral detail of the hyperspectral one into the synthesized image. It indicates the proposed method has a potential as a technique to produce alternative images for the images that would have been observed from a hyperspectral sensor at the high resolution of commercial satellite images.

Guided Image Filtering-Based Pan-Sharpening Method: A Case Study of GaoFen-2 Imagery

GaoFen-2 (GF-2) is a civilian optical satellite self-developed by China equipped with both multispectral and panchromatic sensors, and is the first satellite in China with a resolution below 1 m. Because the pan-sharpening methods on GF-2 imagery have not been a focus of previous works, we propose a novel pan-sharpening method based on guided image filtering and compare the performance to state-of-the-art methods on GF-2 images. Guided image filtering was introduced to decompose and transfer the details and structures from the original panchromatic and multispectral bands. Thereafter, an adaptive model that considers the local spectral relationship was designed to properly inject spatial information back into the original spectral bands. Four pairs of GF-2 images acquired from urban, water body, cropland, and forest areas were selected for the experiments. Both quantitative and visual inspections were used for the assessment. The experimental results demonstrated that for GF-2 imagery acquired over different scenes, the proposed approach consistently achieves high spectral fidelity and enhances spatial details, thereby benefitting the potential classification procedures.

Improving UAV imaging quality by optical sensor fusion: an initial study

ABSTRACTThe most frequent application of unmanned aerial vehicle (UAVs) is to collect optical colour images from an area of interest. Thus, high spatial resolution colour images with high amount of signal to noise ratio (SNR) are of great importance in UAV applications. Currently, most UAVs use single sensor colour filter array (CFA) cameras for image collection, within which the Bayer-pattern sensors are the most frequently used ones. Due to the limitations of the CFAs, the quality (in terms of spatial resolution, SNR, and sharpness) of UAV colour images is not optimal. In this article, a sensor fusion solution is proposed to improve the quality of UAV imaging. In the proposed solution, a high-resolution colour (HRC) Bayer-pattern sensor is replaced by a dual camera set containing a panchromatic (Pan) sensor, with the same pixel size and a Bayer-pattern colour (or a four-band multi-spectral) sensor with larger pixel size; the resulting images of the dual camera set are then fused. The enlarged pixel size of the colour sensor provides a higher SNR for the cost of lower spatial resolution. However, the accompanied Pan sensor provides single band images with high SNR and high spatial resolution. Fusing the images of the dual camera set generates colour (or MS) images with high spatial resolution, SNR, and sharpness compensating for the major problems of the Bayer-pattern filters.This replacement solution is initially tested in a laboratory experiment. The results of quality assessments show that the SNR is increased by 2–3 times, the sharpness is improved by around 2 times, and the spatial resolution is increased up to the level of the pan images, while the colour errors remained almost as low as the original colour images. In addition, image classification capability of the images is examined using two methods: Support Vector Machine (SVM) and Maximum Likelihood (ML). The results of image classification also confirmed around 20–40% increase in accuracy. Therefore, the proposed sensor fusion can be a good alternative for UAV colour sensors.

Application of DIROEF Algorithm for Noncollinear Multiple CCD Array Stitching of the Chinese Mapping Satellite 1-02

The Chinese Mapping Satellite 1-02 (MS1-02) was launched on May 6, 2012, and can provide wide-swath remote sensing images through stitching together subimages acquired by a panchromatic sensor (PAN). However, the limited stitching precision caused by the particular configuration of the PAN reduces the geometric quality of images, which restricts their application. In this letter, we propose a new image-space stitching approach based on the differential recursion optimal estimation filter for PAN images and describe it in detail. To validate the correctness and advantages of our method, experiments were run on several sets of matching points with different characteristics and images with different geographic conditions. The results show that images with seamless visual effect and subpixel-level stitching precision can be obtained.

Robust Approach for Recovery of Rigorous Sensor Model Using Rational Function Model

The replacement of the rational function model (RFM) by the rigorous sensor model (RSM) has been studied extensively and verified with many types of sensors and remote-sensing applications. However, relatively less research has been conducted on recovering RSM from RFM, and the few relating techniques can only be applied in specific circumstances. This paper proposes a novel linear method to obtain the position, attitude, and interior orientation (IO) elements of satellites based on the orientation information of the rays implied by the RFM. Instead of resection, forward intersection is used to solve for position, and an equivalent body coordinate system is introduced to overcome the strong correlation between the attitude and IO. The orientation information of the rays implied by the RFM is used to calculate the IO pixel by pixel. Experiments using the Ziyuan 3 panchromatic nadir sensor show that this method can recover the exterior orientation and IO elements effectively.

CMOS IMAGING SENSOR TECHNOLOGY FOR AERIAL MAPPING CAMERAS

Abstract. In June 2015 Leica Geosystems launched the first large format aerial mapping camera using CMOS sensor technology, the Leica DMC III. This paper describes the motivation to change from CCD sensor technology to CMOS for the development of this new aerial mapping camera. In 2002 the DMC first generation was developed by Z/I Imaging. It was the first large format digital frame sensor designed for mapping applications. In 2009 Z/I Imaging designed the DMC II which was the first digital aerial mapping camera using a single ultra large CCD sensor to avoid stitching of smaller CCDs. The DMC III is now the third generation of large format frame sensor developed by Z/I Imaging and Leica Geosystems for the DMC camera family. It is an evolution of the DMC II using the same system design with one large monolithic PAN sensor and four multi spectral camera heads for R,G, B and NIR. For the first time a 391 Megapixel large CMOS sensor had been used as PAN chromatic sensor, which is an industry record. Along with CMOS technology goes a range of technical benefits. The dynamic range of the CMOS sensor is approx. twice the range of a comparable CCD sensor and the signal to noise ratio is significantly better than with CCDs. Finally results from the first DMC III customer installations and test flights will be presented and compared with other CCD based aerial sensors.

CMOS IMAGING SENSOR TECHNOLOGY FOR AERIAL MAPPING CAMERAS

In June 2015 Leica Geosystems launched the first large format aerial mapping camera using CMOS sensor technology, the Leica DMC III. This paper describes the motivation to change from CCD sensor technology to CMOS for the development of this new aerial mapping camera. <br><br> In 2002 the DMC first generation was developed by Z/I Imaging. It was the first large format digital frame sensor designed for mapping applications. In 2009 Z/I Imaging designed the DMC II which was the first digital aerial mapping camera using a single ultra large CCD sensor to avoid stitching of smaller CCDs. The DMC III is now the third generation of large format frame sensor developed by Z/I Imaging and Leica Geosystems for the DMC camera family. It is an evolution of the DMC II using the same system design with one large monolithic PAN sensor and four multi spectral camera heads for R,G, B and NIR. For the first time a 391 Megapixel large CMOS sensor had been used as PAN chromatic sensor, which is an industry record. Along with CMOS technology goes a range of technical benefits. The dynamic range of the CMOS sensor is approx. twice the range of a comparable CCD sensor and the signal to noise ratio is significantly better than with CCDs. <br><br> Finally results from the first DMC III customer installations and test flights will be presented and compared with other CCD based aerial sensors.

LEICA DMC III CALIBRATION AND GEOMETRIC SENSOR ACCURACY

As an evolution of the successful DMC II digital camera series, Leica Geosystems has introduced the Leica DMC III digital aerial camera using, for the first time in the industry, a large-format CMOS sensor as a panchromatic high-resolution camera head. This paper describes the Leica DMC III calibration and its quality assurance and quality control (QA/QC) procedures. It will explain how calibration was implemented within the production process for the Leica DMC III camera. Based on many years of experience with the DMC and DMC II camera series, it is know that the sensor flatness has a huge influence on the final achievable results. The Leica DMC III panchromatic CMOS sensor with its 100.3mm x 56.9mm size shows remaining errors in a range of 0.1 to 0.2μm for the root mean square and shows maximum values not higher that 1.0μm. The Leica DMC III is calibrated based on a 5cm Ground Sample Distance (GSD) grid pattern flight and evaluated with three different flying heights at 5cm, 8cm and 11cm GSD. The geometric QA/QC has been performed using the calibration field area, as well as using an independent test field. The geometric performance and accuracy is unique and gives ground accuracies far better than the flown GSD.

Correction of Distortions in YG-12 High-Resolution Panchromatic Images

Design deficiencies and hardware limitations cause a number of issues with the images acquired by Chinese satellites launched before 2012, such as YG-12. The geometric quality of the images recorded by YG-12 cannot match its high resolution because of serious time-synchronization errors and interior distortions. To improve the geometric quality of YG-12 images, this paper proposes a method of interior calibration for the YG-12 panchromatic sensor. In addition, an innovative method is proposed to eliminate time-synchronization errors using parallel observations of the panchromatic sensor onboard YG-12. The experimental results indicate the interior parameters of the panchromatic sensor are determined with an accuracy of better than 0.32 pixels, and seamless mosaic images can be obtained after the elimination of distortions. Furthermore, the positioning accuracy with relatively few ground control points is shown to be better than 1.5 pixels.

Transformation From Hyperspectral Radiance Data to Data of Other Sensors Based on Spectral Superresolution

Hyperspectral radiance spectra are the sensor's response, through its spectral response functions (SRFs), to the at-sensor radiance field. As the SRFs vary with sensors, hyperspectral radiance data need to be transformed for cross-calibration with another sensor or data simulation of a future sensor. In fact, the hyperspectral radiance data are composed of average radiance in the sensor's passbands and bear a spectral smoothing effect. Current data transformation methods do not fully consider this spectral response effect in hyperspectral data, and it restricts the accuracy of the transformation and, hence, the accuracy of the cross-calibration and data simulation. The spectral response effect is modeled as a correlation-sampling process instead of convolution sampling to fit all the hyperspectral, multispectral, and panchromatic sensors. An iterative method is proposed to get a superresolution spectrum from the hyperspectral radiance spectrum according to the response model. Data of another sensor are simulated by responding to the superresolution spectrum. Validation experiments are performed using MODTRAN4-simulated spectrum, coincident Earth Observing-1 Hyperion and Advanced Land Imager (ALI) radiance data, and nearly coincident Hyperion and Landsat-7 Enhanced Thematic Mapper Plus data. For transformations from the MODTRAN4-simulated spectrum to the hyperspectral spectrum of different spectral resolutions, the proposed transformation method achieves the least root mean square relative error (RMSRE) compared with the linear interpolation, the cubic spline interpolation, and the deconvolution-sampling method. For transformation from the Hyperion data to the ALI multispectral and panchromatic data, the RMSRE is less than 9% and 4%, respectively.

Coseismic Horizontal Offsets and Fault-Trace Mapping Using Phase Correlation of IRS Satellite Images: The 1999 Izmit (Turkey) Earthquake

On August 17, 1999, a strong earthquake (Mw ? 7.4) occurred along the western sector of the North Anatolian Fault system in Turkey. The epicenter was located near the city of Izmit, 50 km east of Istanbul. Previous works determined the coseismic surface displacements by satellite synthetic aperture radar (SAR) interferometry (InSAR) and satellite optical-image correlation. In 1999, the highest spatial resolution orbiting camera was the panchromatic sensor (PAN), a 5.8-m pixel sensor (SPOT 2 was a 10-m pixel sensor) onboard the Indian Remote Sensing (IRS) satellite. We propose to apply a new phase-correlation method to PAN images to study the coseismic rupture due to the Izmit earthquake. The phase-correlation method does not need phase unwrapping and was proved to be robust under a wide variety of circumstances. Image correlometry deals with the quantification of the subpixel offsets over the whole image, allowing displacement measurement with an accuracy that is proportional to the pixel size. We measured the near-field deformations exploiting two geometrically corrected IRS images with similar look angles. A quality check of the derived offset map was performed by comparison with GPS benchmarks and SPOT offsets. The results show that IRS PAN images can be correlated to derive coseismic slip offsets due to a large earthquake (and to map its fault trace).

Scaling-up Transformation of Multisensor Images with Multiple Resolutions

For scaling up low resolution multispectral images (LRMIs) with high resolution panchromatic image (HRPI), intensity-hue-saturation (IHS) can produce satisfactory spatial enhancement but usually introduces spectral distortion in the fused high resolution multispectral images (HRMIs). In this paper, to minimize this problem, we present a generalized intensity modulation (GIM) by extending the IHS transform to an arbitrary number of LRMIs, which uses the information of the spectral response functions (SRFs) of the multispectral and panchromatic sensors. Before modulation, the generalized intensity is enhanced by injecting details extracted from the HRPI by means of empirical mode decomposition. After the enhanced generalized intensity is substituted for the old one, the HRMIs are obtained through the GIM. Quickbird images are used to illustrate the superiority of this proposed method. Extensive comparison results based on visual analysis and Wald’s protocol demonstrate that the proposed method is more encouraging for scaling up the LRMIs with the HRPI spectrally and spatially than the tested fusion methods.

Detection of Ground Control Points using the SITOGEOGIS tool to orthorectify Landsat 7 ETM + images

Images from the Landsat 7 spacecraft have been widely used in various environmental applications because they are comparatively inexpensive with respect to other satellite data, in addition the panchromatic sensor provides high resolution with a wide coverage area. We present here a orthorectification procedure of Landsat images using the SITOGEO-GIS tool for the GCPs acquisition. Appling this method eighty-five Landsat 7 ETM+ images, cover ing the entire region of Italy, were orthorectified to the UTM-WGS 84 Geodetic Reference System and organized into a searchable database. Our Landsat images were then compared with the data downloadable from Global Land Cover Facility (GLCF) showing a planimetric discrepancies less than 2 pixels. Moreover this work shows that the best target for GCPs collection is the human-made structures.