Modified IHS Research Articles

Hybrid modelling approach for water body change detection at Chalan Beel area in northern Bangladesh

Water is a strategic resource for both socio-economic development and human life. The current study has been carried out for spatio-temporal change detection of surface water bodies during winter period using hybrid modelling approach. The study area has fallen in the northern part of Bangladesh and is locally called Chalan Beel with 5 million of in habitants, a prominent intensive crop production, surface and groundwater irrigation, high evapotranspiration, and water scarcity. For the detection of water body changes, satellite images of 1999 and 2011 were used, and the following image fusion techniques were applied: (a) Gram-Schmidt (GS), (b) modified intensity hue saturation (IHS), (c) high-pass filter (HPF), and (d) wavelet. Landsat 7/ETM + panchromatic (PAN) band of 15 m × 15 m resolution in 1999 and Landsat 5/TM multispectral (MS) bands of 30 m × 30 m resolution in 2011 were allied each other to generate high-resolution image that contains information of two different years. The fused images were classified to extract the water bodies using four classification methods: (a) artificial neural network (ANN), (b) support vector machine (SVM) and (c) maximum likelihood (ML). To analyze the quality of the fused images, statistical calculation (quantitatively) and Laplacian edge detection (qualitatively) were used. To validate the fused image classification results, the multispectral images from 1999 and 2011 were again individually classified using principal component analysis (PCA), normalized difference water index (NDWI), and image differencing (ID) processes and compared with the previous classification. Surprisingly, the results showed that two-third of the areas dried up in 10 years.

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.

An effective approach to selecting the appropriate pan-sharpening method in digital change detection of natural ecosystems

In an image fusion process, the spatial resolution of a multispectral image is improved by a panchromatic band. However, due to the spatial and spectral resolution differences between these two data sets, the enhanced image may have two distortions, spatial and spectral. Therefore, to evaluate the efficiency of the pan-sharpening method, the status of these two types of distortions is examined. Unfortunately, there is still no developed acceptance index that can thoroughly investigate the quality of the pansharpened image; moreover, most of the proposed methods for reviewing the quality of output images have been developed with an emphasis on the residential area. Accordingly, to assess the quality of the pansharpened image in this study, we evaluated highly effective conventional methods, such as visual examinations, quantitative evaluation and impact analysis regarding the change detection process of mangrove forests. Finally, we suggested a simple yet efficient approach for such research in natural ecosystems. In the proposed method, based on the nature of the ecosystem, a spectral vegetation index is applied to the pansharpened images; the spectral quality of the images is further evaluated based on two parameters, 1) the areas under the curves of the histogram of the spectral vegetation index in the natural ecosystem region and 2) its centroid. The spatial quality of the pansharpened images is evaluated through implementing of two transects perpendicular to each other in the images of the spectral vegetation index, and creating a spatial deviation on them. With expert reviews and visual evaluation of the pansharpened images, the proposed method, especially in natural ecosystems, has more advantages as regards assessing the quality of the fused images. Based on the evaluations, among 11 methods of pansharpening, including Ehlers Fusion, FuzeGO, Gram-Schmidt, HPF, HCS, PCA, Modified IHS, Brovey Transform, Projective Resolution Merge, Wavelet IHS, and Wavelet PCA; the HPF method the Brovey Transform and Modified IHS methods respectively showed the best performance in the digital change detection of Mangrove forests.

Comparison of various pan-sharpening methods using Quickbird-2 and Landsat-8 imagery

Pan-sharpening is the process of transferring the spatial resolution of panchromatic (PAN) image to a multispectral (MS) image for producing a single image with high spatial detail and rich spectral information. In this study, PAN and MS imagery of Quickbird-2 and Landsat-8 are fused separately, using ten different pan-sharpening methods such as principal component analysis (PCA), modified-intensity hue saturation (M-IHS), multiplicative, brovey transform (BT), wavelet-principal component analysis (W-PCA), hyperspectral color space (HCS), high-pass filter (HPF), Gram-Schmidt (GS), Fuze Go, and non-subsampled contourlet transform (NSCT). The effectiveness of these techniques is assessed and compared by qualitative analysis and 14 quantitative analysis methods including bias, correlation coefficient (CC), difference in variance (DIV), relative dimensionless global error in synthesis (ERGAS), universal image quality index (Q), relative average spectral error (RASE), root mean square error (RMSE), structural similarity index method (SSIM), signal-to-noise ratio (SNR), peak SNR (PSNR), spatial correlation coefficient (SCC), image entropy (E), and gradient and quality with no reference image (QNR). The results of both analysis types show that the Fuze Go and NSCT produced the best fused image with high spatial detail and rich spectral information followed by the HPF and GS.

An assessment of pan-sharpening algorithms for mapping mangrove ecosystems: a hybrid approach

ABSTRACTMapping mangrove (littoral and swamps) ecosystems is challenging due to the qualitative and quantitative nature of the surrounding water and mudflats. However, accurate assessment of mangroves is required to determine carbon credits. This research study explores five pan-sharpening algorithms with the aim of determining the best algorithm to map mangrove ecosystems from very high resolution satellite images. In this research, a multidimensional evaluation was employed to pinpoint the best algorithm from among five advanced algorithms, i.e. Ehler’s transformation (ET), modified intensity hue saturation (MIHS), wavelet transformation (WT), optimized high pass filter addition (OHPFA), and subtractive resolution merge (SRM). These approaches involve the calculation of spectral root mean square error (RMSE), Sobel filter RMSE and correlation coefficient (r). OHPFA and SRM provided good results during this assessment. Object-based image analysis was incorporated to further assess the best technique between these two approaches for assessing mangrove tree canopy by calculating under and over segmentation. The SRM algorithm provides the best results with a kappa coefficient (κ) of 0.875 and an accuracy of 92.3% when compared with ground data. This research is very useful in various applications such as calculation of crown projection area using high resolution satellite images for estimation of blue carbon in mangrove trees.

Evaluation of pan-sharpening methods for spatial and spectral quality

Many pan-sharpening methods have been proposed to fuse the high spectral and low spatial resolution of multispectral (MS) image with the high spatial resolution of panchromatic (PAN) image to produce a multispectral image with improved spatial resolution. In this study, the effectiveness of pan-sharpening methods such as principal component analysis (PCA), brovey transform (BT), modified intensity hue saturation (M-IHS), multiplicative, wavelet-intensity-hue-saturation (W-IHS), wavelet principal component analysis (W-PCA), hyperspectral colour space (HCS), high-pass filter (HPF), gram-schmidt (GS), subtractive resolution merge (SRM), Fuze Go and Ehlers was assessed and compared by fusing the PAN and MS imagery of Quickbird-2. The qualities of the pan-sharpening methods were evaluated by both visual and quantitative analyses with respect to spatial and spectral fidelity. In quantitative analysis, the spectral indices such as spectral angle mapper (SAM), relative dimensionless global error in synthesis (ERGAS), structural similarity index method (SSIM), relative average spectral error (RASE), correlation coefficient (CC) and universal image quality index (Q) were used. The spatial indices such as spatial correlation coefficient (SCC), gradient and image entropy (E) were used. The result of both analyses revealed that the Ehlers and Fuze Go methods performed better than the other methods. The Ehlers method was superior by retaining the colour information, and Fuze Go best enhanced the spatial details in the fused image.

Fusion of very high-resolution UAV images with criteria-based image fusion algorithm

Image fusion is the combination of spectral characteristics of a low-resolution image and spatial features of a higher resolution image to produce the spatially enhanced image. Different fusion techniques have been introduced in the literature. Fusion algorithms tend to yield good results when using single sensor images as input data sets. Fusion process becomes more challenging in case multisensor images are used. The spatial resolution ratio of the input images is another key factor which makes fusion process more challenging. A fusion algorithm is considered successful if it achieves to increase the spatial details without damaging the colour content of the low-resolution image. In this study, criteria-based fusion algorithm, introduced for the fusion of images with four bands, has been further modified for the fusion of a WorldView-2 MS (1.85 m) and an orthophoto panchromatic image (10 cm) produced with the images taken from an unmanned aerial vehicle (UAV). The spectral and spatial quality of the fused image produced with this algorithm has been evaluated qualitatively and quantitatively, and compared with those of advanced fusion methods such as Gram-Schmidt, FuzeGo, High-Pass Filtering, Ehlers, Hyperspherical Colour Space, Modified IHS and Adaptive Wavelet-Based algorithms. The results show that the criteria-based algorithm is very successful in keeping the colour content and gives satisfactory spatial detail enhancement compared to other algorithms.

Assessment of pan-sharpened very high-resolution WorldView-2 images

The fusion of multispectral (MS) images with high spatial resolution panchromatic (pan) images compensates the trade-off between spatial and spectral resolutions. The performance of fusion algorithms for different sensors is an active area of research. Therefore, with the availability of new very high-resolution (VHR) sensors, it becomes customary to evaluate the applicability of existing fusion techniques. In this study, we focused on the WorldView-2 (WV2) spaceborne sensor, which captures data in eight MS (2 m spatial resolution) bands and one pan (0.5 m spatial resolution) band. We compared and assessed 12 fusion techniques, namely Brovey transform (BT), Ehlers, Gram–Schimdt (GS), hyperspherical colour sphere (HCS), high-pass filter (HPF), modified intensity hue saturation (ModIHS), Multiplicative, PANSHARP, PANSHARP2, principal component (PC), and wavelet (IHS and PC)-based methods. To measure the quality of fused products, qualitative and quantitative methods were used. In qualitative methods, visual analysis of different colour composites was carried out. In quantitative methods, band-wise eight quality metrics including the required processing time and controlling parameters were reported. For overall image quality assessment, it is necessary to combine the values of different quality metrics. Therefore, a mean observation score based on these values was calculated to rank the fusion methods. It was observed that the HPF and PANSHARP methods produced the most visually appealing images, whereas quantitative values indicated that HCS, HPF, and PANSHARP methods performed better than other methods. Combining the results of quantitative and qualitative analyses, it was found that PANSHARP and HPF methods are superior to other methods in preserving both spatial and spectral details.

Spectral information analysis of image fusion data for remote sensing applications

Fusion of images with different spatial resolutions has the capability of improving visualization and spatial resolution and enhancing structural/textural information of the involved images, while preserving the spectral information in multi-spectral (MS) images. In this paper, various fusion methods have been examined in the data fusion of GeoEye-1 and QuickBird imagery, followed by subsequent image control. The effectiveness of five techniques, the Gram-Schmidt (GS), high-pass filtering (HPF), modified intensity-hue-saturation (M-IHS), fast Fourier transform (FFT)-enhanced IHS (FFT-E) and wavelet principal component analysis (W-PCA), has been evaluated through visual inspection, histogram analysis and correlation analysis. Also, image quality information is assessed by means of global spectral information (relative dimensionless global error, relative average spectral error), spectral distortion (peak signal-to-noise ratio), spectral (bias, root means square error) and spatial information (mean, standard deviation) of the fused images. In addition, the extraction of object boundary is tested and evaluated using Canny edge detection. The results show that most of the image fusion techniques preserve spectral information of original image, but occasionally with some spectral distortion. It has been found that the GS method, followed by HPF, yields the best information quality in the fused image, suitable for improving visual interpretation and data quality from the viewpoint of remote sensing applications.

Remote Sensing of Ecological Hotspots: Producing Value-added Information from Multiple Data Sources

Fusing high-spatial resolution panchromatic and high-spectral resolution multispectral images with complementary characteristics provides basis for complex land-use and land-cover type classifications. In this research, we investigated how well different pan sharpening algorithms perform when applied to single-sensor single-date and multi-senor multi–date images that encompass the Horton Plains national park (HPNP), a highly fragile eco-region that has been experiencing severe canopy depletion since 1970s, in Sri Lanka. Our aim was to deliver resolution-enhanced multitemporal images from multiple earth observation (EO) data sources in support of long-term dieback monitoring in the HPNP. We selected six candidate fusion algorithms: Brovey transform, Ehlers fusion algorithm, high-pass filter (HPF) fusion algorithm, modified intensity-hue-saturation (MIHS) fusion algorithm, principal component analysis (PCA) fusion algorithm, and the wavelet-PCA fusion algorithm. These algorithms were applied to eight different aerial and satellite images taken over the HPNP during last five decades. Fused images were assessed for spectral and spatial fidelity using fifteen quantitative quality indicators and visual inspection methods. Spectral quality metrics include correlation coefficient, root-mean-square-error (RMSE), relative difference to mean, relative difference to standard deviation, spectral discrepancy, deviation index, peak signal-to-noise ratio index, entropy, mean structural similarity index, spectral angle mapper, and relative dimensionless global error in synthesis. The spatial integrity of fused images was assessed using Canny edge correspondence, high-pass correlation coefficient, RMSE of Sobel-filtered edge images, and Fast Fourier Transform correlation. The Wavelet-PCA algorithm exhibited the worst spatial improvement while the Ehlers.MIHS and PCA fusion algorithms showed mediocre results. With respect to our multidimensional quality assessment,the HPF emerged as the best performing algorithm for single-sensor single-date and multi-sensor multi-date data fusion.We further examined the effect of fusion in the object-based image analysis framework. Our subjective analysis showed the improvement of image object candidates when panchromatic images’ high-frequency information is injected to low resolution multispectral images.

Assessment of pan-sharpening methods applied to image fusion of remotely sensed multi-band data

Image fusion is a useful tool for integrating a high resolution panchromatic image (PI) with a low resolution multispectral image (MIs) to produce a high resolution multispectral image for better understanding of the observed earth surface. Various methods proposed for pan-sharpening satellite images are examined from the viewpoint of accuracies with which the color information and spatial context of the original image are reproduced in the fused product image. In this study, methods such as Gram-Schmidt (GS), Ehler, modified intensity-hue-saturation (M-IHS), high pass filter (HPF), and wavelet-principal component analysis (W-PCA) are compared. The quality assessment of the products using these different methods is implemented by means of noise-based metrics. In order to test the robustness of the image quality, Poisson noise, motion blur, or Gaussian blur is intentionally added to the fused image, and the signal-to-noise and related statistical parameters are evaluated and compared among the fusion methods. And to achieve the assessed accurate classification process, we proposed a support vector machine (SVM) based on radial basis function kernel. By testing five methods with WorldView2 data, it is found that the Ehler method shows a better result for spatial details and color reproduction than GS, M-IHS, HPF and W-PCA. For QuickBird data, it is found that all fusion methods reproduce both color and spatial information close to the original image. Concerning the robustness against the noise, the Ehler method shows a good performance, whereas the W-PCA approach occasionally leads to similar or slightly better results. Comparing the performance of various fusion methods, it is shown that the Ehler method yields the best accuracy, followed by the W-PCA. The producer's and user's accuracies of the Ehler method are 89.94% and 90.34%, respectively, followed by 88.14% and 88.26% of the W-PCA method.

Assessing ground cover at patch and hillslope scale in semi-arid woody vegetation and pasture using fused Quickbird data

The amount and distribution of vegetation and ground cover are important factors that influence resource transfer (e.g. runoff, sediment) in patterned semi-arid landscapes. Identifying and describing these features in detail is an essential part of measuring and understanding ecohydrological processes at hillslope scales that can then be applied at broader scales. The aim of this study was to develop a comprehensive methodology to map ground cover using high resolution Quickbird imagery in woody and non-woody (pasture) vegetation. The specific goals were to: (1) investigate the use of several techniques of image fusion, namely principal components analysis (PCA), Brovey transform, modified intensity–hue–saturation (MIHS) and wavelet transform to increase the spatial detail of multispectral Quickbird data; (2) evaluate the performance of the red and near-infra-red bands (NIR), the difference vegetation index (DVI), and the normalised difference vegetation index (NDVI) in estimating ground cover, and (3) map and assess spatial and temporal changes in ground cover at hillslope scale using the most appropriate method or combination of methods. Estimates of ground cover from the imagery were compared with a subset of observed ground cover estimates to determine map accuracy. The MIHS algorithm produced images that best preserved spectral and spatial integrity, while the red band fused with the panchromatic band produced the most accurate ground cover maps. The patch size of the ground cover beneath canopies was similar to canopy size, and percent ground cover (mainly litter) increased with canopy size. Ground cover was mapped with relative accuracies of 84% in the woody vegetation and 86% in the pasture. From 2008 to 2009, ground cover increased from 55% to 65% in the woody vegetation and from 40% to 45% in the pasture. These ground cover maps can be used to explore the spatial ecohydrological interactions between areas of different ground cover at hillslope scale with application to management at broader scales.

Comparison of Global- and Local-scale Pansharpening for Rapid Assessment of Humanitarian Emergencies

We compared global-scale pansharpening of full- or large-scene QuickBird satellite imagery with local-scale pansharpening of a subset of these scenes by using modified intensity-hue-saturation (MIHS), principal component analysis (PCA), multiplicative (MP), and high pass filter ( HPF ) methods. The spectral properties of all pansharpened images were evaluated by using quantitative indices such as erreur relative globale adimensionnelle de synthese (ERGAS), correlation coefficient (CC), relative difference to mean (RDM), and relative difference to standard deviation (RDS). This study discovered that local-scale pansharpening was generally lower and higher than the global-scale approach in terms of ERGAs and CC, respectively. In particular, local-scale HPF produced pansharpening results very close to the original multispectral image with less than 0.18 percent and 0.07 percent of RDM and RDS, respectively. Local-scale fusion results with PCA and MP were similar to those of the global-scale approach. Local-scale pansharpening that uses very high spatial resolution imagery could lead to the rapid assessment of the magnitude and severity of humanitarian situations by producing a color image of high spatial resolution with reduced processing time.

Assessment of the fused image of multispectral and panchromatic images of SPOT5 in the investigation of geological hazards

The investigated area in this paper is located on the northern mountain in Guangzhou City. It is characterized by high relief and inaccessibility. Multispectral and pan Images of SPOT5 were used as the remote sensing data source, and high-pass filtering (HPF), Brovery transform (BT), intensity-hue-saturation (IHS), principal component analysis (PCA) and the modified IHS (MIHS) methods were adopted for image fusion. Here, a comparison has been made between the entire fused images and the original multispectral images. Subjective evaluation and objective evaluation (entropy, average gradient, correlation coefficient, distribution of gray) have been adopted to assess the quality of the fused images. Also regional geological survey has been taken to find the interpretation veracity. Results show that the MIHS is the best image fusion method for geological hazards interpretation, and the fused image can provide abundant textural and spectral information for easy interpretation of such geological hazards as collapse, landslip, and debris flow.