Multispectral Bands Research Articles

Combination of visible and thermal remotely sensed data for enhancement of Land Cover Classification by using satellite imagery

On a large scale, the land cover classification has been investigated throughout the world in remote sensing for different kinds of applications such as water resources, agricultural, environmental, as well as ecological and hydrological applications. In order enhance accuracy of the classification results, Landsat and multispectral bands are used to study the numerous classification methods. Remote sensing thermal data provides valuable information in order to examine the effectiveness of applying the thermal bands to extract useful land cover thematic maps. In this research, Landsat-8 satellite data captured by Operational Land Imager (OLI) and the Thermal Infrared (TIRS) Sensors, with using remotely sensing data and Geographic Information System (GIS) analysis with using ground truth data collect from fieldwork in same time of imagery capturing by using infrared thermometer camera. In 2018, single date Landsat-8 image of the study area in Iraq was captured in winter. This image is used to estimate Land Surface Temperature (LST) by split window algorithm and performing Land Cover (LC) classification after image noise removal by using supervised classification algorithms Support Vector Machine (SVM) with multi-spectral and thermal bands combinations to find out which one has more accuracy. Result shows the effective and efficiency of the proposed method compared by traditional classification methods. The overall accuracy and Kappa coefficient are 94.25%, 64.43% and 0.93, 0.63, respectively.

Performance of chlorophyll prediction indices for Eragrostis tef at Sentinel-2 MSI and Landsat-8 OLI spectral resolutions

As remotely sensed data becomes more readily available around the world, satellites such as Landsat-8 and Sentinel-2 have great potential to support precision agriculture. Sensors with high spectral and spatial resolutions are particularly optimal for limited land resource farmers to improve land management. The objective of this short communication is to assess the performance of Sentinel-2 and Landsat-8 multispectral bands for chlorophyll prediction using indices that were originally developed using imaging spectroscopy/hyperspectral data. Remotely sensed chlorophyll content measures are often utilized as a proxy of plant health. Performance of a group of chlorophyll prediction indices is tested for tef (Eragrostis tef), an endemic grass crop native to Ethiopia that forms a major component of Ethiopian diets and is grown by limited land resource farmers. Hyperspectral reflectance data captured in situ at the canopy level were convolved into bands approximating Landsat-8 and Sentinel-2 sensors, and a suite of chlorophyll prediction indices were computed and regressed against chlorophyll content. Results show that simple pigment indices employing wavelengths corresponding to the blue and ultra-blue bands performed best for predicting chlorophyll. The red-edge index computed using the Sentinel-2 bands also performed well. These findings suggest that publicly available, multispectral imagery can potentially substitute for hyperspectral data in chlorophyll prediction indices, thereby improving the accessibility of precision agriculture methods.

A Novel Image Compression Based Method for Multispectral Fingerprint Biometric System

Abstract A biometric system is a pattern recognition tool for efficient human recognition based upon biological and behavioral traits. The fingerprint is one of the most promising and widely used biometric with almost 48% market share. The images for fingerprint systems may be captured in multispectral bands which needs special techniques for efficient working of the overall system. Multispectral Fingerprint biometric system has recently become quite popular as it provides high security and recognition. Multispectral images are of high dimensions, the high dimensionality of multispectral data requires robust compression technique. In this Paper a novel approach using Huffman coding and wavelet decomposition for multispectral fingerprint biometric system has been presented. The technique promises high security to templates in database as well as compressed templates. The compressed templates results in faster matching during authentication phase of the fingerprint biometric system. Moreover, the presented technique results in low revocability but high security to mitigate the effect of masquerade attacks.

A novel fire index-based burned area change detection approach using Landsat-8 OLI data

ABSTRACT Change detection from multi-temporal remote sensing images is an effective way to identify the burned areas after forest fires. However, the complex image scenario and the similar spectral signatures in multispectral bands may lead to many false positive errors, which make it difficult to exact the burned areas accurately. In this paper, a novel-burned area change detection approach is proposed. It is designed based on a new Normalized Burn Ratio-SWIR (NBRSWIR) index and an automatic thresholding algorithm. The effectiveness of the proposed approach is validated on three Landsat-8 data sets presenting various fire disaster events worldwide. Compared to eight index-based detection methods that developed in the literature, the proposed approach has the best performance in terms of class separability (2.49, 1.74 and 2.06) and accuracy (98.93%, 98.57% and 99.51%) in detecting the burned areas. Simultaneously, it can also better suppress the complex irrelevant changes in the background.

Optimization of Band Selection in Multispectral and Narrow-Band Imaging: An Analytical Approach.

The contrast ratio has been proposed for quantification of image quality of subsurface inhomogeneities in the skin. Based on the two-flux Kubelka-Munk model, we developed an analytical approach which links the contrast ratio with optical tissue parameters. We obtained an explicit analytical solution for the dependence of maximal contrast ratio on optical tissue parameters. Then, we linked the minimally observable contrast ratio (cmin) with the bit depth of the camera, d: cmin=1/(2d-1). Based on this analysis we were able to derive an explicit expression, which links camera properties with the minimally detectable changes in optical tissue parameters (both scattering and absorption). The proposed analytical model can be used for rapid assessment and optimization of multispectral and narrow band imaging techniques and for estimation of the accuracy of imaging techniques. The developed model confirms the utility of the contrast ratio for tissue imaging.

Retrieval of Land Surface Temperature from Earth Observation Satellites for Gas Flaring Sites in the Niger Delta, Nigeria

This research investigates the recording of Land Surface Temperature (LST) by Earth Observation (EO) Satellites for four gas flaring sites in Rivers State, Nigeria. Six Landsat 5 Thematic Mapper (TM) and Eleven Landsat 7 Enhanced Thematic Mapper Plus (ETM+) from 17 January 1986 to 08 March 2013 with < 5% cloud contamination were considered. All the sites are located within a single Landsat scene (Path 188, Row 057). Dark Object Subtraction (DOS) method and Atmospheric Correction Parameter (ATMCORR) Calculator were used to obtain atmospheric correction effects parameters for multispectral and thermal bands [Upwelling radiance (Lu), downwelling radiance (Ld) and transmittance (τ)] of Landsat data respectively. The emissivity (e) for each site is estimated by using standard values for determined land surface cover from Look Up Table (LUT). The correction obtained from DOS method was applied to the computed reflectance to get the atmospherically corrected reflectance that was used for the classification of land cover. The Lu, Ld and τ obtained were applied to the calibrated at-sensor radiance band 6 (high gain) data to compute the surface-leaving radiance (Lλ) with the evalues obtained for each site. The Planck equation was inverted using the calibration constants to derive LST. Six range of LST values were retrieved for each flaring site, with Bonny Liquefied Natural Gas (LNG) Plant recorded the highest LST (345.0 K) and Umudioga Flow Station with the lowest (293.0 K). LST retrieved from both sensors for the flare hotspots are the highest values compared to other locations within the processing sites, which was clearly shown through Geospatial Information System (GIS) spatial analysis and the transects plots. Furthermore, the closer is the distance to the flare, the higher is the temperature and vice versa. Based on these results, it can be concluded that satellite based sensors, such as Landsat TM and ETM+, have the ability to record LST at gas flaring sites in the Niger Delta.

Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data

Land cover mapping of intensive cropping areas facilitates an enhanced regional response to biosecurity threats and to natural disasters such as drought and flooding. Such maps also provide information for natural resource planning and analysis of the temporal and spatial trends in crop distribution and gross production. In this work, 10 meter resolution land cover maps were generated over a 6200 km2 area of the Riverina region in New South Wales (NSW), Australia, with a focus on locating the most important perennial crops in the region. The maps discriminated between 12 classes, including nine perennial crop classes. A satellite image time series (SITS) of freely available Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery was used. A segmentation technique grouped spectrally similar adjacent pixels together, to enable object-based image analysis (OBIA). K-means unsupervised clustering was used to filter training points and classify some map areas, which improved supervised classification of the remaining areas. The support vector machine (SVM) supervised classifier with radial basis function (RBF) kernel gave the best results among several algorithms trialled. The accuracies of maps generated using several combinations of the multispectral and radar bands were compared to assess the relative value of each combination. An object-based post classification refinement step was developed, enabling optimization of the tradeoff between producers’ accuracy and users’ accuracy. Accuracy was assessed against randomly sampled segments, and the final map achieved an overall count-based accuracy of 84.8% and area-weighted accuracy of 90.9%. Producers’ accuracies for the perennial crop classes ranged from 78 to 100%, and users’ accuracies ranged from 63 to 100%. This work develops methods to generate detailed and large-scale maps that accurately discriminate between many perennial crops and can be updated frequently.

ANALYZING THE EFFECTS OF LAND COVER CHANGE ON SURFACE TEMPERATURE IN MOUNT MAKILING FOREST RESERVE (MMFR) AND ITS NEIGHBORING MUNICIPALITIES USING LANDSAT DATA

Abstract. Land Surface Temperature (LST) is said to be affected by frequent changes in the land cover. Over the years, the immediate environs of Mount Makiling Forest Reserve (MMFR) have experienced such kind of change due to rapid economic growth of the area that also led to the expansion of urban centers. The study utilized Landsat imageries to determine the possible effects of land cover change on surface temperature using the integration of remote sensing and GIS technologies. Initially, the multispectral bands were radiometrically corrected using Dark Object Subtraction (DOS) while the thermal bands were corrected using Land Surface Emissivity (LSE). After these corrections were applied, the images were classified using supervised image classification technique where seven land cover types have been identified. The classified images were then validated using 200 reference data and this revealed an overall accuracy of 87.5% and 86.0% for the May 2003 and July 2015 images, respectively. Results showed that changes in land cover resulted to a significant change in Land Surface Temperature (LST). The LST in 2003 (16.49°C – 40.44°C) was found higher than that of 2015 which was observed between 13.35°C and 33.83°C only. The reason behind this is the increase in green spaces from 2003 to 2015. Among the major land cover types, forest lands exhibited the lowest mean surface temperature for both years having 27.27°C in 2003 and 21.35°C in 2015 while built-up areas had the highest surface temperature having 32.60°C in 2003 and 26.00°C in 2015.

Simulation of land use dynamics and impact on land surface temperature using satellite data

Increasing of global average surface temperature naturally leading to major problems as global warming, which has typically attracted the interest of multinational organizations, civil society groups and scientists in the world. The main aim of this research is to properly investigate the impact of urban development via land-use dynamics on the future micro-climate of Beijing by predicting land surface temperature (LST) distribution and simulating land-use dynamics. Land use/land cover (LULC) and LST were traditionally produced using multispectral and thermal bands. Land use dynamics were calculated using a statistical model. LST of each LULC was mapped to assess temporal changes of LST responding to LULC changes in the study area for 20 years over three periods (1997–2005, 2005–2017 and 1997–2017). Prediction of LST was employed using single and multiple linear regression models until 2027. The results show that the average LST increased from 22.06 to 35.85 °C, from 20.98 to 37.04 °C, and from 17.00 to 38.29 °C in 1997, 2005 and 2017, respectively. This can be explained by the population growth and change in LULC. In addition, the comprehensive index of land use degree showed significant changes in land use of the area caused by several human activities. The annual rate of the urban area showed a remarkable increase as a direct result of urban sprawl. Urbanization may impact negatively on vegetation, causing it to decrease progressively to 2017. Furthermore, the results of the prediction models show that the Urban Index and Modification of Normalized Difference Water Index are highly influential to the forecasting of LST. The study has also demonstrated that remote sensing and GIS techniques are useful tools for assessing spatial and temporal changes in LULC dynamic and its impacts on LST.

A Joint Sparsity Approach to Soil Detection Using Expanded Bands of WV-2 Images

Soil can be used as a damage indicator of landslides and flooding, which expose soil from vegetation canopy. It can also be used as an indirect indicator of illegal tunnel digging activity. This letter presents a sparsity-based approach to soil detection using multispectral satellite images, where both original and synthetic bands have been used. Spatial and spectral information has then been jointly used in soil detection. Extensive experiments clearly demonstrated the feasibility of our approach.

Comparison of multi-seasonal Landsat 8, Sentinel-2 and hyperspectral images for mapping forest alliances in Northern California

The current era of earth observation now provides constellations of open-access, multispectral satellite imagery with medium spatial resolution, greatly increasing the frequency of cloud-free data for analysis. The Landsat satellites have a long historical record, while the newer Sentinel-2 (S2) satellites offer higher temporal, spatial and spectral resolution. The goal of this study was to evaluate the relative benefits of single- and multi-seasonal multispectral satellite data for discriminating detailed forest alliances, as defined by the U.S. National Vegetation Classification system, in a Mediterranean-climate landscape (Sonoma County, California). Results were compared to a companion analysis of simulated hyperspectral satellite data (HyspIRI) for the same study site and reference data (Clark et al., 2018). Experiments used real and simulated S2 and Landsat 8 (L8) data. Simulated S2 and L8 were from HyspIRI images, thereby focusing results on differences in spectral resolution rather than other confounding factors. The Support Vector Machine (SVM) classifier was used in a hierarchical classification of land-cover (Level 1), followed by alliances (Level 2) in forest pixels, and included summer-only and multi-seasonal sets of predictor variables (bands, indices and bands plus indices). Both real and simulated multi-seasonal multispectral variables significantly improved overall accuracy (OA) by 0.2–1.6% for Level 1 tree/no tree classifications and 3.6–25.8% for Level 2 forest alliances. Classifiers with S2 variables tended to be more accurate than L8 variables, particularly for S2, which had 0.4–2.1% and 5.1–11.8% significantly higher OA than L8 for Level 1 tree/no tree and Level 2 forest alliances, respectively. Combining multispectral bands and indices or using just bands was generally more accurate than relying on just indices for classification. Simulated HyspIRI variables from past research had significantly greater accuracy than real L8 and S2 variables, with an average OA increase of 8.2–12.6%. A final alliance-level map used for a deeper analysis used simulated multi-seasonal S2 bands and indices, which had an overall accuracy of 74.3% (Kappa = 0.70). The accuracy of this classification was only 1.6% significantly lower than the best HyspIRI-based classification, which used multi-seasonal metrics (Clark et al., 2018), and there were alliances where the S2-based classifier was more accurate. Within the context of these analyses and study area, S2 spectral-temporal data demonstrated a strong capability for mapping global forest alliances, or similar detailed floristic associations, at medium spatial resolutions (10–30 m).

Is it possible to discern Striga weed (Striga hermonthica) infestation levels in maize agro-ecological systems using in-situ spectroscopy?

The invasion by Striga in most cereal crop fields in Africa has posed a significant threat to food security and has caused substantial socioeconomic losses. Hyperspectral remote sensing is an effective means to discriminate plant species, providing possibilities to track such weed invasions and improve precision agriculture. However, essential baseline information using remotely sensed data is missing, specifically for the Striga weed in Africa. In this study, we investigated the spectral uniqueness of Striga compared to other co-occurring maize crops and weeds. We used the in-situ FieldSpec® Handheld 2™ analytical spectral device (ASD), hyperspectral data and their respective narrow-band indices in the visible and near infrared (VNIR) region of the electromagnetic spectrum (EMS) and four machine learning discriminant algorithms (i.e. random forest: RF, linear discriminant analysis: LDA, gradient boosting: GB and support vector machines: SVM) to discriminate among different levels of Striga (Striga hermonthica) infestations in maize fields in western Kenya. We also tested the utility of Sentinel-2 waveband configurations to map and discriminate Striga infestation in heterogenous cereal crop fields. The in-situ hyperspectral reflectance data were resampled to the spectral waveband configurations of Sentinel-2 using published spectral response functions. We sampled and detected seven Striga infestation classes based on three flowering Striga classes (low, moderate and high) against two background endmembers (soil and a mixture of maize and other co-occurring weeds). A guided regularized random forest (GRRF) algorithm was used to select the most relevant hyperspectral wavebands and vegetation indices (VIs) as well as for the resampled Sentinel-2 multispectral wavebands for Striga infestation discrimination. The performance of the four discriminant algorithms was compared using classification accuracy assessment metrics. We were able to positively discriminate Striga from the two background endmembers i.e. soil and co-occurring vegetation (maize and co-occurring weeds) based on the few GRRF selected hyperspectral vegetation indices and the GRRF selected resampled Sentinel-2 multispectral bands. RF outperformed all the other discriminant methods and produced the highest overall accuracy of 91% and 85%, using the hyperspectral and resampled Sentinel-2 multispectral wavebands, respectively, across the four different discriminant models tested in this study. The class with the highest detection accuracy across all the four discriminant algorithms, was the “exclusively maize and other co-occurring weeds” (>70%). The GRRF reduced the dimensionality of the hyperspectral data and selected only 9 most relevant wavebands out of 750 wavebands, 6 VIs out of 15 and 6 out of 10 resampled Sentinel-2 multispectral wavebands for discriminating among the Striga and co-occurring classes. Resampled Sentinel-2 multispectral wavebands 3 (green) and 4 (red) were the most crucial for Striga detection. The use of the most relevant hyperspectral features (i.e. wavebands and VIs) significantly (p ≤ 0.05) increased the overall classification accuracy and Kappa scores (±5% and ±0.2, respectively) in all the machine learning discriminant models. Our results show the potential of hyperspectral, resampled Sentinel-2 multispectral datasets and machine learning discriminant algorithms as a tool to accurately discern Striga in heterogenous maize agro-ecological systems.

Multicomponent Driven Consistency Priors for Simultaneous Decomposition and Pansharpening

Pansharpening is also known as the fusion of a low-resolution (LR) multispectral (MS) image and a high-resolution (HR) panchromatic (Pan) image of the same scene, which is an effective way to improve the spatial resolution of the LR MS image so as to obtain an HR MS image (i.e., pansharpened MS image). In this article, we propose a novel multicomponent consistency priors driven variational model for simultaneous decomposition and pansharpening (SDP) in a unified optimization framework. Specifically, the proposed SDP model particularly decomposes the Pan and MS images into cartoon, structure and texture components, and fully exploits the multicomponent consistency priors on these cartoon, structure and texture components of the Pan and MS images. Thus, the proposed SDP model can suitably characterize the different properties of these multicomponents so that these multicomponents can be well preserved. Moreover, the proposed SDP model is actually a band-coupled model, which can fully preserve the intrinsic structural correlation among the MS bands, because the MS image is actually a spatial-spectral strongly correlated cube. Then, an efficient iterative algorithm based on the forward-backward splitting technique is designed to solve the proposed SDP model. Finally, we compare the proposed SDP method with some state-of-the-art methods on various satellite datasets, and the experimental results demonstrate the effectiveness of the proposed method, which can perform higher spectral and spatial qualities than the other methods.

Random Forest Classifier for Extracting Water bodies from Pansharpened Image to Detect Surface Water Changes

Change detection from time series multispectral Landsat imagery has been an active research in remote sensing for several years to monitor the ecosystem, environment, climate and so on. This study is focused on detecting the changes in surface water by the integration of fusion and image classification techniques in multi-temporal multispectral Landsat images. The panchromatic band and the multispectral band of Landsat OLI and TM images respectively, were fused using undecimated wavelet transform to get the pan-sharpened image. Then classification techniques like Maximum Likelihood, Support Vector Machine, Artificial Neural Network and Random Forest were employed for extracting the water pixels and changed pixels. The performances of these classification techniques were analyzed based on metrics such as overall error, commission error, precision, recall, overall accuracy, kappa coefficients and the results show that the application of random forest classifier on pansharpened image outperforms in extracting the water pixels and also in highlighting the changes with maximum accuracy.

Random Forest Classifier for Extracting Water bodies from Pansharpened Image to Detect Surface Water Changes

Change detection from time series multispectral Landsat imagery has been an active research in remote sensing for several years to monitor the ecosystem, environment, climate and so on. This study is focused on detecting the changes in surface water by the integration of fusion and image classification techniques in multi-temporal multispectral Landsat images. The panchromatic band and the multispectral band of Landsat OLI and TM images respectively, were fused using undecimated wavelet transform to get the pan-sharpened image. Then classification techniques like Maximum Likelihood, Support Vector Machine, Artificial Neural Network and Random Forest were employed for extracting the water pixels and changed pixels. The performances of these classification techniques were analyzed based on metrics such as overall error, commission error, precision, recall, overall accuracy, kappa coefficients and the results show that the application of random forest classifier on pansharpened image outperforms in extracting the water pixels and also in highlighting the changes with maximum accuracy

Metaheuristic pansharpening based on symbiotic organisms search optimization

This study proposed a metaheuristic pansharpening (MP) method, which performs in a Synthetic Variable Ratio (SVR)-like manner. The proposed method introduced the Symbiotic Organisms Search (SOS) algorithm, an advanced nature-based optimization algorithm, to estimate a weight for each multispectral (MS) band to achieve the optimum intensity. The SVR pansharpening formula was used as the objective function and the Root Mean Square Error (RMSE) metric was used as the fitness function of the SOS algorithm to optimize the intensity. The spectral and spatial quality of the results of the MP method were qualitatively and quantitatively compared against those of 15 widely-used pansharpening methods in 5 test sites in Turkey with different land cover features. The experiments aimed to spatially enhance WorldView-2 MS images by using WorldView-2 panchromatic (PAN) bands and a UAV-derived PAN orthophoto. It was also aimed to sharpen IKONOS MS images by using a QuickBird pansharpened image and an IKONOS PAN band. The MATLAB software was used to implement the proposed method and to compute the spatial and spectral quality metrics. The spatial quality of each pansharpened image was evaluated at full-scale, whereas the spectral quality of each pansharpened image was evaluated at both full-scale and reduced-scale. A scoring strategy based on giving a performance score with respect to the spatial and spectral quality metrics was used to ensure a fair comparison among the pansharpening methods used. The results demonstrated that, out of 16 pansharpening methods, the MP method achieved the highest overall spectral and spatial quality scores of 15.5 and 15.5, respectively. The proposed method was found to perform successfully with both singlesensor and multisensor input images. It was also concluded that the proposed method is able to deal with high spatial resolution ratio between the input images.

A Novel Multispectral, Panchromatic and SAR Data Fusion for Land Classification

Multisensor data fusion is addressed in this article for land classification purposes in a semiarid environment. A novel algorithm based on multispectral, panchromatic and synthetic aperture radar (SAR) data is here presented. The proposed multisensory data fusion approach relies on the generalized intensity-hue-saturation (G-IHS) transform and the A trous wavelet transform (ATWT). The fusion product is obtained by modulating the high features details of the panchromatic ATWT with the SAR texture and by replacing the high-pass details of the G-IHS Intensity component with this panchromatic-SAR modulation. After the fusion product is derived, a classification is performed by using a standard maximum likelihood classifier. The proposed algorithm is tested over a meaningful case study acquired over the Maspalomas Special Natural Reserve (Spain) and processing data from WorldView-2 (for both multispectral and panchromatic channels) and TerraSAR-X (for the SAR channel) missions. Results show a fine preservation of the spectral information contained in each multispectral band. Sharpened details are observed over built-up areas and a smoothing texture is perceived over homogeneous areas (lakes, sea, bare soil, and roads) due to the SAR-panchromatic modulation. This leads to a better overall classification accuracy of the fused image compared to outcomes obtained with a single sensor, resulting 7% and 2% more accurate than multispectral and pan-sharpening classification, respectively.

Sub-urban land classification using GF-2 images and support vector machine method

Remote sensing classification is an important part in the process of extracting effective image information and research the foundation of land cover change. While traditional remote sensing image classification methods have some problems on low accuracy and uncertainty, machine learning algorithms are gradually applied to remote sensing classification. In this paper, support vector machines (SVM) method with high training speed and low computation burden is adopted to classify land cover based on GF-2 image, which is the domestic optical remote sensing satellite with high spatial resolution. The results show that: The overall classification accuracy by SVM is achieved 72.59% and the coefficient of Kappa is 0.65. The classification map is highly consistent with the original image, especially higher classification accuracy of cropland and tree. Partial regions were misclassified as shadow that didn’t reflect the real land objects. As a whole, there is favorable classification quality using SVM method and GF-2 multispectral bands.

Convolutional Sparse Representation of Injected Details for Pansharpening

In this letter, we address the pansharpening problem, which focuses on constructing a high-resolution (HR) multispectral (MS) image from a low-resolution (LR) MS and an HR panchromatic (Pan) image. The accuracy of pansharpening method based on sparse representation (SR) mainly depends on the construction of dictionary and the learning of sparse coefficients, while the details injection (DI)-based pansharpening method sharpens the MS bands by adding the proper spatial details from Pan. The combination of SR and DI has been put forward as the pansharpening method based on SR of injected details (SR-D). However, limited to the patch-based manner, pansharpening with traditional SR model faces two disadvantages, i.e., limited ability in detail preservation and high sensitivity to misregistration. In this letter, we replace the traditional SR model with convolutional SR (CSR) as a global SR model in the SR-D method and propose a new pansharpening method called CSR of injected details (CSR-D) to overcome the above-mentioned two drawbacks. Experimental results on the IKONOS and WorldView2 data sets show that the proposed method can achieve remarkable spectral and spatial quality on both reduced scale and full scale.

A genetic algorithm solution to the gram-schmidt image fusion

ABSTRACTThere is no such thing as ‘the best image fusion method’ in terms of both spectral and spatial fidelity. This fact encourages the researchers to develop more advanced approaches in order to optimally transfer the spatial details without distorting the colour content. Component substitution (CS)-based image fusion methods have been proven to produce sharper images but suffer from colour distortion. The aim of this study was to modify the CS-based Gram-Schmidt (GS) fusion method with the aid of the Genetic Algorithm (GA) to further improve its colour preservation performance. The GA was used to estimate a weight for each multispectral (MS) band. The obtained band weights were used to generate a low-resolution panchromatic (PAN) band, which plays a significant role in the performance of the GS method. The performance of the proposed approach was compared not only against the conventional GS, but also against widely-used CS-based, multiresolution analysis (MRA)-based and colour-based (CB) image fusion methods. The results indicated that the proposed GA-based approach produced spectrally and spatially superior results compared to the other methods used.