Satellite Image Fusion Research Articles

Location Related Signals with Satellite Image Fusion Method Using Visual Image Integration Method

Location Related Signals with Satellite Image Fusion Method Using Visual Image Integration Method

Pre-Processing of Panchromatic Images to Improve Object Detection in Pansharpened Images.

In recent years, many techniques of fusion of multi-sensors satellite images have been developed. This article focuses on examining and improvement the usability of pansharpened images for object detection, especially when fusing data with a high GSD ratio. A methodology to improve an interpretative ability of pansharpening results is based on pre-processing of the panchromatic image using Logarithmic-Laplace filtration. The proposed approach was used to examine several different pansharpening methods and data sets with different spatial resolution ratios, i.e., from 1:4 to 1:60. The obtained results showed that the proposed approach significantly improves an object detection of fused images, especially for imagery data with a high-resolution ratio. The interpretative ability was assessed using qualitative method (based on image segmentation) and quantitative method (using an indicator based on the Speeded Up Robust Features (SURF) detector). In the case of combining data acquired with the same sensor the interpretative potential had improved by a dozen or so per cent. However, for data with a high resolution ratio, the improvement was several dozen, or even several hundred per cents, in the case of images blurred after pansharpening by the classic method (with original panchromatic image). Image segmentation showed that it is possible to recognize narrow objects that were originally blurred and difficult to identify. In addition, for panchromatic images acquired by WorldView-2, the proposed approach improved not only object detection but also the spectral quality of the fused image.

Comparison of Five Spatio-Temporal Satellite Image Fusion Models over Landscapes with Various Spatial Heterogeneity and Temporal Variation

In recent years, many spatial and temporal satellite image fusion (STIF) methods have been developed to solve the problems of trade-off between spatial and temporal resolution of satellite sensors. This study, for the first time, conducted both scene-level and local-level comparison of five state-of-art STIF methods from four categories over landscapes with various spatial heterogeneity and temporal variation. The five STIF methods include the spatial and temporal adaptive reflectance fusion model (STARFM) and Fit-FC model from the weight function-based category, an unmixing-based data fusion (UBDF) method from the unmixing-based category, the one-pair learning method from the learning-based category, and the Flexible Spatiotemporal DAta Fusion (FSDAF) method from hybrid category. The relationship between the performances of the STIF methods and scene-level and local-level landscape heterogeneity index (LHI) and temporal variation index (TVI) were analyzed. Our results showed that (1) the FSDAF model was most robust regardless of variations in LHI and TVI at both scene level and local level, while it was less computationally efficient than the other models except for one-pair learning; (2) Fit-FC had the highest computing efficiency. It was accurate in predicting reflectance but less accurate than FSDAF and one-pair learning in capturing image structures; (3) One-pair learning had advantages in prediction of large-area land cover change with the capability of preserving image structures. However, it was the least computational efficient model; (4) STARFM was good at predicting phenological change, while it was not suitable for applications of land cover type change; (5) UBDF is not recommended for cases with strong temporal changes or abrupt changes. These findings could provide guidelines for users to select appropriate STIF method for their own applications.

Effect of fusing Sentinel-2 and WorldView-4 imagery on the various vegetation indices

Satellite image fusion techniques have been used for more than two decades. Development of satellite sensors for very high-resolution satellite imagery monitoring contributed to the development of new image fusion techniques. We examine the quality of Ehlers, Brovey transform, modified intensity–hue–saturation (M-IHS), and high-filter resolution merge fusion methods on vegetation indices values combining high-resolution WorldView-4 and low-resolution Sentinel-2 imagery. For image fusion, four bands—blue, green, red, and near-infrared—were used. The effect of fusing Sentinel-2 and WorldView-4 imagery is tested on the various vegetation indices [normalized difference vegetation index (NDVI), blue normalized difference vegetation index (BNDVI), and green normalized difference vegetation index (GNDVI)]. M-IHS fusion showed the best result, with the least difference between indices calculated based on the original Sentinel-2 and fused bands. Difference between vegetation indices calculated using original Sentinel-2 and M-IHS fused bands for 30.08.2015, 30.09.2016, and 30.09.2017 is between −0.041 to 0.037. The second part of this research is an evaluation of three different vegetation classes (grassland, mixed vegetation, and forest) on three different years and three different months within a year. Difference between NDVI, GNDVI, and BNDVI calculated based on the original Sentinel-2, and M-IHS fused bands are ranging from −0.043 to 0.103, −0.041 to 0.050, and −0.052 to 0.030 for grassland, mixed vegetation, and forest, respectively. On the other hand, the difference between vegetation indices for grassland, mixed vegetation, and forest are from −0.110 to 0.050, −0.041 to 0.038, and −0.052 to 0.038, when observing August, September, and October of 2017, respectively.

Assessment of the effectiveness of spatiotemporal fusion of multi-source satellite images for cotton yield estimation

Deficiencies in the spatiotemporal resolution of remote sensing (RS) images limit crop yield estimation at the farm and field scale. These deficiencies may be alleviated by fusion of high spatial and temporal resolution images such as MODIS and Landsat. In this study, a new daily MODIS NDVI product (reconstructed MODIS) was generated from 16-day composite images using the Extreme Model, which integrates the NDVI value with the corresponding specific date information at each pixel. The Flexible Spatiotemporal Data Fusion (FSDAF) model was then used to create two fused, high-resolution time-series products (fused MODIS and fused reconstructed MODIS) in order to enhance the spatial and temporal effectiveness of satellite images for field-scale applications. Three yield estimation models were then built using time-series data of Landsat NDVI, predicted NDVI from fused MODIS, and predicted NDVI from fused reconstructed MODIS. The methodology was tested on a farm field over the cotton growing season in the San Joaquin Valley of California. Results showed that: (1) the time trend of NDVI over the growing season for the fused reconstructed MODIS was more similar to that of Landsat than were either of MODIS or fused MODIS, indicating that the specific date of MODIS pixels is important for time-series analysis; (2) the NDVI from fused reconstructed MODIS provided the best correlation with Landsat NDVI, with R2 and RMSE values 15% higher than for fused MODIS; (3) correlation between cotton yield and all three datasets at the pixel level was statistically significant for all image dates, and (4) the accuracy of the cotton yield estimation model using predicted NDVI from fused reconstructed MODIS (R2 = 0.79; RMSE = 488.01) was higher than with fused MODIS (R2 = 0.77; RMSE = 513.96) and only slightly lower than with Landsat (R2 = 0.84, RMSE = 463.12). This study improved the accuracy of MODIS-based yield estimation using fusion images, and the results can be applied to improve vegetation monitoring and quantitative modeling using MODIS NDVI at the field scale.

Qualitative assessment of geostatistical and non-geostatistical fusion techniques: a case study on landsat 8 images

Broadly satellite images are available in two categories (1) high spectral resolution but less spatial resolution (2) high spatial resolution but less spectral resolution. But in certain applications, images with high spatial as well as high spectral resolution are required. To meet such kind of requirement, Image fusion is widely accepted and increasingly being used. In this study satellite image fusion is done using geostatistical methods (cokriging, regression kriging) and non-geostatistical methods (intensity hue saturation, principal component analysis). The study is focused on performing qualitative assessment of selected image fusion techniques. In this study, the primary variable is RGB bands of Landsat 8 Operational Land Imager (OLI) and the panchromatic band is chosen as the second variable. The output of these selected methods is compared to access spectral and spatial quality. Spectral quality is accessed by finding the correlation between the primary variable and the output, however spatial quality is accessed via texture analysis method named entropy. Overall assessment of loss of correlation, luminance distortion, and contrast distortion is done using Image quality index. Correlation index of regression kriging and PCA are comparable whereas entropy and image quality index of fused output is highest in case of regression kriging. Hence regression kriging can be concluded as the best fusion technique out of the compared techniques.

Satellite image fusion to detect changing surface permeability and emerging urban heat islands in a fast-growing city.

Rapid and extensive urbanization has adversely impacted humans and ecological entities in the recent decades through a decrease in surface permeability and the emergence of Urban Heat Islands (UHI). While detailed and continuous assessments of surface permeability and UHI are crucial for urban planning and management of landuse zones, they mostly involve time consuming and expensive field studies and single sensor derived large scale aerial and satellite imageries. We demonstrated the advantage of fusing imageries from multiple sensors for landuse and landcover (LULC) change assessments as well as for assessing surface permeability and temperature and UHI emergence in a fast growing city, i.e. Tirunelveli, Tamilnadu, India. IRS-LISSIII and Landsat-7 ETM+ imageries were fused for 2007 and 2017, and classified using a Rotation Forest (RF) algorithm. Surface permeability and temperature were then quantified using Soil-Adjusted Vegetation Index (SAVI) and Land Surface Temperature (LST) index, respectively. Finally, we assessed the relationship between SAVI and LST for entire Tirunelveli as well as for each LULC zone, and also detected UHI emergence hot spots using a SAVI-LST combined metric. Our fused images exhibited higher classification accuracies, i.e. overall kappa coefficient values, than non-fused images. We observed an overall increase in the coverage of urban (dry, real estate plots and built-up) areas, while a decrease for vegetated (cropland and forest) areas in Tirunelveli between 2007 and 2017. The SAVI values indicated an extensive decrease in surface permeability for Tirunelveli overall and also for almost all LULC zones. The LST values showed an overall increase of surface temperature in Tirunelveli with the highest increase for urban built-up areas between 2007 and 2017. LST also exhibited a strong negative association with SAVI. Southeastern built-up areas in Tirunelveli were depicted as a potential UHI hotspot, with a caution for the Western riparian zone for UHI emergence in 2017. Our results provide important metrics for surface permeability, temperature and UHI monitoring, and inform urban and zonal planning authorities about the advantages of satellite image fusion.

$M^3\text{Fusion}$: A Deep Learning Architecture for Multiscale Multimodal Multitemporal Satellite Data Fusion

Modern Earth Observation systems provide remote sensing data at different temporal and spatial resolutions. Among all the available spatial mission, today the Sentinel-2 program supplies high temporal (every five days) and high spatial resolution (HSR) (10 m) images that can be useful to monitor land cover dynamics. On the other hand, very HSR (VHSR) imagery is still essential to figure out land cover mapping characterized by fine spatial patterns. Understanding how to jointly leverage these complementary sources in an efficient way when dealing with land cover mapping is a current challenge in remote sensing. With the aim of providing land cover mapping through the fusion of multitemporal HSR and VHSR satellite images, we propose a suitable end-to-end deep learning framework, namely $M^3\text{Fusion}$ , which is able to simultaneously leverage the temporal knowledge contained in time series data as well as the fine spatial information available in VHSR images. Experiments carried out on the Reunion Island study area confirm the quality of our proposal considering both quantitative and qualitative aspects.

Roof Shape Classification from LiDAR and Satellite Image Data Fusion Using Supervised Learning.

Geographic information systems (GIS) provide accurate maps of terrain, roads, waterways, and building footprints and heights. Aircraft, particularly small unmanned aircraft systems (UAS), can exploit this and additional information such as building roof structure to improve navigation accuracy and safely perform contingency landings particularly in urban regions. However, building roof structure is not fully provided in maps. This paper proposes a method to automatically label building roof shape from publicly available GIS data. Satellite imagery and airborne LiDAR data are processed and manually labeled to create a diverse annotated roof image dataset for small to large urban cities. Multiple convolutional neural network (CNN) architectures are trained and tested, with the best performing networks providing a condensed feature set for support vector machine and decision tree classifiers. Satellite image and LiDAR data fusion is shown to provide greater classification accuracy than using either data type alone. Model confidence thresholds are adjusted leading to significant increases in models precision. Networks trained from roof data in Witten, Germany and Manhattan (New York City) are evaluated on independent data from these cities and Ann Arbor, Michigan.

Multisensor Satellite Image Fusion and Networking for All-Weather Environmental Monitoring

Given the advancements of remote sensing technology, large volumes of remotely sensed images with different spatial, temporal, and spectral resolutions are available. To better monitor and understand the changing Earth's environment, fusion of remotely sensed images with different spatial, temporal, and spectral resolutions is critical for distinctive feature retrieval, interpretation, mapping, and decision analysis. A suite of methods have been developed to fuse multisensor satellite images for different purposes in the past few decades. This paper provides a thorough review of contemporary and classic image fusion methods and presents a summary of their phenomenological applications, with challenges and perspectives, for environmental systems analysis. Cross-mission satellite image fusion, networking, and missing value pixel reconstruction for environmental monitoring are described, and their complex integration is illustrated with a case study of Lake Nicaragua that elucidates the state-of-the-art remote sensing technologies for advancing water quality management.

Una metodología para la caracterización del uso del suelo mediante imágenes de media resolución espacial

Introduction: The characterization of land uses represents one of the essential inputs for the management of natural resources at different scales. Objective: To develop a methodology to characterize land use in the upper creek basin from the Azul stream (Buenos Aires, Argentina), through the fusion of satellite images with a medium spatial resolution. Materials and methods: A time-series of 23 images was used from the Normalized Difference Vegetation Index (NDVI) of the MODIS-Terra satellite (product MOD13Q1) for the period May 2015 - May 2016. Landsat 8 images were used to discriminate some categories difficult to classify with NDVI-MODIS. The final cover map was validated regarding verification points independent to the classification process; its accuracy was evaluated by means of the Kappa statistic. Results and discussion: The NDVI time series allowed to recognize the phenological patterns of the covers and land use of greater representativeness in the region. Seven land cover were discriminated; the agricultural uses represented 81.5 % of the surface, double-crop wheat-soya (soybean in Argentina) system predominated (39.4 %). The overall accuracy of the final map was high (88.9 %, Kappa coefficient = 0.86). Conclusion: The methodology used has the advantage of being quick and replicable, to characterize the land uses of a given region and to evaluate its potential changes over time.

Satellite Image Registration and Image Fusion by using Principle Component Analysis

This aims to fused image registration and image fusion used to spatial resolution images by principle component analysis method. Digital image processing requires either the full image or a part of image. It will be processed from the user’s point of view like the radius of object. Wavelet technique will improve the spatial resolution to produce spectral degradation in output image. To overcome the spectral degradation, PCA fusion method can be used. PCA uses curve which represent edges and extraction of the detailed information from the image.PAN and MS images are used by individual acquired low frequency approximate component and high frequency detail components in this PCA. To evaluate the image fusion accuracy, Peak Signal to Noise Ratio and Root Mean Square Error are used. The advantages of using digital image processing are preservation of original data accuracy, flexibility and repeatability.

Performance assessment of neuro fuzzy based image fusion of satellite images

Performance assessment of neuro fuzzy based image fusion of satellite images

A robust adaptive spatial and temporal image fusion model for complex land surface changes

Spatial and temporal satellite image fusion (STIF) has provided a feasible alternative for generating imagery with both high spatial and temporal resolution, thus expanding the applications of existing satellite sensors. However, a critical challenge confronting the further development of STIF is to systematically and robustly address complex land surface changes, which include land cover changes without shape changes (e.g., crop rotation) and land cover changes with shape changes (e.g., urban expansion), in addition to conventional land surface changes (e.g., phenological changes of vegetation). This paper presents the Robust Adaptive Spatial and Temporal Fusion Model (RASTFM) to tackle this challenge with one prior pair of MODIS-Landsat images. In RASTFM, land surface changes are reorganized into non-shape changes (including phenological changes and land cover changes without shape changes) and shape changes (i.e., land cover changes with shape changes), which are handled differently. However, both non-shape changes and shape changes are predicted through a Non-Local Linear Regression (NL-LR) of the subject pixel's similar neighbors. A regression based high-pass modulation is also performed as a post-processing step to improve both the spatial details and spectral fidelity of the predicted Landsat image. Unlike other STIF models (e.g., the Spatial and Temporal Adaptive Reflectance Fusion Model, STARFM), RASTFM can find similar neighboring pixels more precisely through a non-local searching strategy and derives the weights of similar neighbors more rigorously via a linear regression model. As both non-shape and shape changes are treated based on the regression of similar neighboring pixels, the land surface changes are processed in a unified manner. Experiments that use one simulated and three actual MODIS-Landsat datasets featured by different types of land surface changes were conducted to demonstrate the performance of RASTFM. Comparisons with the state-of-the-art STIF models, including weighted function, unmixing and dictionary-learning methods, show that NL-LR based RASTFM can capture the land surface changes in various landscapes more accurately and robustly in a unified manner, thereby facilitating the continuous and detailed monitoring of complex and diverse land surface dynamics.

Intensity-hue-saturation renovation and local deviation methods for satellite image fusion metric assessment

Intensity-hue-saturation (IHS) method is an exceptional mixing moving towards with computational capability and spatial classification holding. An isolated identifying picture blend count in light of HIS change and neighbourhood grouping and its interaction with little computational flightiness are projected. Visual impact and total examination happens as expected demonstrate that the proposed crucial estimation out plays out the standard picture mix procedures in the spectre space with the spatial excellence like that of un-obliterated wavelet change related course of action. The proposed changed procedure can secure the relative spatial assurance of the solidified picture with the IHS-related blend computation and the improved spooky excellence. In this paper, we develop our past results for picture mix estimations by including the strategies to satellite images against night vision and single-metric biomedical picture blend examination. Multispectral outlines consolidate satellite descriptions for arrive cover examination. In this manuscript, we explored grouping of estimations which are used to assess the execution of the entwined picture things. In this manuscript we assemble the distinctive estimations into multiple categories and show a utilisation of the estimations to satellite descriptions with an entropy-dependant picture mix technique.

Intensity-hue-saturation renovation and local deviation methods for satellite image fusion metric assessment

Intensity-hue-saturation (IHS) method is an exceptional mixing moving towards with computational capability and spatial classification holding. An isolated identifying picture blend count in light of HIS change and neighbourhood grouping and its interaction with little computational flightiness are projected. Visual impact and total examination happens as expected demonstrate that the proposed crucial estimation out plays out the standard picture mix procedures in the spectre space with the spatial excellence like that of un-obliterated wavelet change related course of action. The proposed changed procedure can secure the relative spatial assurance of the solidified picture with the IHS-related blend computation and the improved spooky excellence. In this paper, we develop our past results for picture mix estimations by including the strategies to satellite images against night vision and single-metric biomedical picture blend examination. Multispectral outlines consolidate satellite descriptions for arrive cover examination. In this manuscript, we explored grouping of estimations which are used to assess the execution of the entwined picture things. In this manuscript we assemble the distinctive estimations into multiple categories and show a utilisation of the estimations to satellite descriptions with an entropy-dependant picture mix technique.

THERMAL AND VISIBLE SATELLITE IMAGE FUSION USING WAVELET IN REMOTE SENSING AND SATELLITE IMAGE PROCESSING

Abstract. Multimodal remote sensing approach is based on merging different data in different portions of electromagnetic radiation that improves the accuracy in satellite image processing and interpretations. Remote Sensing Visible and thermal infrared bands independently contain valuable spatial and spectral information. Visible bands make enough information spatially and thermal makes more different radiometric and spectral information than visible. However low spatial resolution is the most important limitation in thermal infrared bands. Using satellite image fusion, it is possible to merge them as a single thermal image that contains high spectral and spatial information at the same time. The aim of this study is a performance assessment of thermal and visible image fusion quantitatively and qualitatively with wavelet transform and different filters. In this research, wavelet algorithm (Haar) and different decomposition filters (mean.linear,ma,min and rand) for thermal and panchromatic bands of Landast8 Satellite were applied as shortwave and longwave fusion method . Finally, quality assessment has been done with quantitative and qualitative approaches. Quantitative parameters such as Entropy, Standard Deviation, Cross Correlation, Q Factor and Mutual Information were used. For thermal and visible image fusion accuracy assessment, all parameters (quantitative and qualitative) must be analysed with respect to each other. Among all relevant statistical factors, correlation has the most meaningful result and similarity to the qualitative assessment. Results showed that mean and linear filters make better fused images against the other filters in Haar algorithm. Linear and mean filters have same performance and there is not any difference between their qualitative and quantitative results.

Comparison of Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Stationary Wavelet Transform (SWT) based Satellite Image Fusion Techniques

Comparison of Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Stationary Wavelet Transform (SWT) based Satellite Image Fusion Techniques

Pixel-level image fusion techniques in remote sensing: a review

In the recent years remote sensing image fusion of satellite images has become a popular tool for analyzing different features presented on satellite images. We have analyzed only pixel-level image fusion techniques in this paper, which integrates a low-resolution multispectral (MS) image and high-resolution panchromatic (PAN) image to produce a more informative image than any of the single image. Generally, a PAN image is having a better spatial resolution, while the MS image is having a better spectral resolution than PAN image, due to this trade-off between MS and PAN image resolutions, it could be difficult to preserve spectral and spatial resolution in a single image without a fusion technique. In this paper, we have reviewed most popular and recent image fusion techniques and implemented them on Cartosat-1, RESOURCESAT-2, LANDSAT-8 data set. Results obtained from each method by visual analysis and quantitative measures indicated that UNB fusion algorithm outperforms other techniques used in this paper.

Surface water areas significantly impacted 2014 dengue outbreaks in Guangzhou, China

Dengue transmission in urban areas is strongly influenced by a range of biological and environmental factors, yet the key drivers still need further exploration. To better understand mechanisms of environment–mosquito–urban dengue transmission, we propose an empirical model parameterized and cross-validated from a unique dataset including viral gene sequences, vector dynamics and human dengue cases in Guangzhou, China, together with a 36-year urban environmental change maps investigated by spatiotemporal satellite image fusion. The dengue epidemics in Guangzhou are highly episodic and were not associated with annual rainfall over time. Our results indicate that urban environmental changes, especially variations in surface area covered by water in urban areas, can substantially alter the virus population and dengue transmission. The recent severe dengue outbreaks in Guangzhou may be due to the surge in an artificial lake construction, which could increase infection force between vector (mainly Aedes albopictus) and host when urban water area significantly increased. Impacts of urban environmental change on dengue dynamics may not have been thoroughly investigated in the past studies and more work needs to be done to better understand the consequences of urbanization processes in our changing world.