Intensity-hue-saturation Research Articles

Generalized IHS-BT framework for the pansharpening of high-resolution satellite imagery

Image fusion is an important technique for enhancing the spatial resolution of multispectral imagery by merging with a corresponding high-resolution panchromatic image. Many algorithms have been developed to obtain a better trade-off between spectral fidelity and spatial resolution enhancement. The recently developed techniques use sophisticated algorithms; however, three issues are often ignored. The first involves the progressively increasing amount of high-resolution satellite image data. Second, the designed method needs to be versatile to process a variety of complex terrain and surface features. The third is that the proposed method must satisfy the requirements of both visual inspection and classification. Based on the previous studies, this work proposes a generalized intensity–hue–saturation and Brovey transform framework (GF) to tackle these three problems. Eight modules are integrated into this GF: four modules are designed for classification with a new intensity matching technique to improve the spectral quality; the other four modules are the spatial-enhancement version for visual inspection by adding more native spatial details. To verify the proposed technique, the experiments are conducted for QuickBird and WorldView-2 images. The results show that the GF modules can offer the maximal spatial information content while preserving good spectral information quality.

Fusion of Unmanned Aerial Vehicle Panchromatic and Hyperspectral Images Combining Joint Skewness-Kurtosis Figures and a Non-Subsampled Contourlet Transform.

To obtain fine and potential features, a highly informative fused image created by merging multiple images is usually required. In our study, a novel fusion algorithm called JSKF-NSCT is proposed for fusing panchromatic (PAN) and hyperspectral (HS) images by combining the joint skewness-kurtosis figure (JSKF) and the non-subsampled contourlet transform (NSCT). The JSKF model is used first to derive the three most sensitive bands from the original HS image according to the product of the skewness and the kurtosis coefficients of each band. Afterwards, an intensity-hue-saturation (IHS) transform is used to obtain the luminance component I of the produced false-colour image consisting of the above three bands. Then the NSCT method is used to decompose component I of the false-colour image and the PAN image. The weight-selection rule based on the regional energy is adopted to acquire the low-frequency coefficients and the correlation between the central pixel and its surrounding pixels is used to select the high-frequency coefficients. Finally, the fused image is obtained by applying an IHS inverse transform and an inverse NSCT transform. The unmanned aerial vehicle (UAV) HS and PAN images under low- and high-vegetation coverage of wheat at the flag leaf stage (Stage I) and the grain filling stage (Stage II) are used as the sample data sources. The fusion results are comparatively validated using spatial (entropy, standard deviation, average gradient and mean) and spectral (normalised difference vegetation, NDVI, and leaf area index, LAI) assessments. Additional comparative studies using anomaly detection and pixel clustering also demonstrate that the proposed method outperforms other methods. They show that the algorithm reported herein can better preserve the original spatial and spectral characteristics of the two types of images to be fused and is more stable than IHS, principal components analysis (PCA), non-negative matrix factorization (NMF) and Gram-Schmidt (GS).

A novel remote-sensing image fusion method based on hybrid visual saliency analysis

ABSTRACTIn practical applications, different regions in images may practically have different demands for the spatial and spectral resolution. However, most existing methods execute a unified fusion processing of the whole image with no consideration of such diverse demands. In this article, a new fusion method for remote-sensing images based on saliency analysis is proposed for addressing the issue. By introducing the hybrid visual saliency analysis, regions in the panchromatic (Pan) image and multispectral image would be automatically partitioned into two parts: salient and non-salient regions. Then, a subregion fusion strategy is applied to fuse the non-salient and salient regions, respectively. As for salient regions such as residential areas and roads, the adaptive intensity–hue–saturation (adaptive IHS) method is implemented for its merit of effective improvement in spatial quality. For non-salient regions such as farmland, forest, and grassland, the dual-tree complex wavelet transform is used in the process of spatial detail extraction, and the combination coefficients yielded by the adaptive IHS method are integrated to suppress the spectral distortion. Experimental results demonstrate that our proposal provides state-of-the-art performance as well as achieves a better balance between spatial injection and spectral maintenance in different regions.

Multi-sensor data fusion using NIHS transform and decomposition algorithms

Multi-spectral image fusion is to enhance the details present in multi-spectral bands with the spatial information available in the panchromatic image. Fused images have the effect of spectral distortions and lack of structural similarity. To overcome these limitations, three methods are proposed using intensity, hue, saturation (IHS) and nonlinear IHS (NIHS) transform along with the Dynamic Mode Decomposition (DMD) and 2D-Empirical Mode Decomposition (2D-EMD or IEMD). An intensity plane is calculated from the NIHS transform. The modes are constructed using DMD by considering the variations between the intensity plane computed using NIHS transforms of a low resolution multi-spectral image and a panchromatic image. Similarly, 2D-EMD is also used for image fusion. Modes are subjected to weighted fusion rule to get an intensity plane with spatial and edge information. Finally, the calculated intensity plane is concatenated along with the hue and saturation plane of low-resolution multi-spectral image and transformed into RGB color space. Thus, the fused images have high spatial and edge information on spectral bands. The experiments and its quality assessment assure that proposed methods perform better than the existing methods.

Effects of image pansharpening on soil total nitrogen prediction models in South India

Image fusion is in its infancy in the application of Digital Soil Mapping, and the incorporation of the image pansharpened spectral indices into the soil prediction models has seldom been analyzed. This research performed image pansharpening of Landsat 8, WorldView-2, and Pleiades-1A in a smallholder village called Masuti in South India using three pansharpening techniques: Brovey, Gram-Schmit (GS), and Intensity-Hue-Saturation (IHS) methods. The research analyzed the relationships between multispectral (MS) and pansharpened (PAN) spectral indices and soil total nitrogen (TN), developed the soil TN prediction models using Random Forest methods, and explored the effects of different PAN spectral indices on soil TN prediction models. The results showed the spectral behavior of PAN spectral indices and MS spectral indices were similar. The results also demonstrated that soil TN models based on MS/PAN spectral indices have slightly higher model performance and more detailed characterization of TN spatial pattern compared with soil TN models based on MS spectral indices. Soil TN models based on the GS PAN and MS spectral indices attained slightly higher prediction accuracy compared with those based on other PAN and MS spectral indices. This research advocates the promotion of image pansharpening techniques in digital soil mapping and soil nutrient management research.

Practical remote sensing image fusion method based on guided filter and improved SML in the NSST domain

Due to the different characteristics of image modality, the panchromatic (PAN) and multispectral (MS) images include complementary and redundancy information in the spatial and spectral resolutions. Image fusion is an effective way to integrate the source PAN and MS images to obtain high-resolution MS image. In this paper, a novel remote sensing image fusion scheme in non-subsample Shearlet transform (NSST) domain is presented. An enhancement strategy is designed to solve the insufficiency of spatial detail in multiresolution analysis (MRA)-based methods after the intensity–hue–saturation (IHS) color space transform. Then, in the NSST fusion process, a guided filter-based low-frequency coefficient fusion rule and an improved sum-modified-Laplacian (SML)-based high-frequency coefficient fusion rule are proposed. The final fused image can be obtained through the inverse NSST transform and inverse IHS transform. Two different groups of satellite dataset are utilized to evaluate the fusion performance. The experiment results demonstrate that the proposed approach can achieve more spatial details and less spectral distortion compared with the existing methods regarding both the visual quality and the objective measurements.

Single-frame super resolution of remote-sensing images by convolutional neural networks

ABSTRACTSuper resolution (SR) refers to generation of a high-resolution (HR) image from a decimated, blurred, low-resolution (LR) image set, which can be either a single-frame or multi-frame that contains a collection of images acquired from slightly different views of the same observation area. In this study, two convolutional neural network (CNN)-based deep learning techniques are adapted in single-frame SR to increase the resolution of remote sensing (RS) images by a factor of 2, 3, and 4. In order to both preserve the colour information and speed up the algorithm, first an intensity hue saturation (IHS) transform is utilized and the SR techniques are only applied to the intensity channel of the images. Colour information is then restored with an inverse IHS transformation. We demonstrate the results of the proposed method on RS images acquired from Satellites Pour l’Observation de la Terre (SPOT) or Earth-observing satellites and Pleiades satellites with different spatial resolution. First synthetic LR images are created by downsampling, then structural similarity (SSIM) Index, peak signal-to-noise ratio (PSNR), Spectral Angle Mapper (SAM) and Erreur Relative Globale Adimensionnelle de Synthese (ERGAS) values are calculated for a quantitative evaluation of the methods. Finally, the method, with better performance results, is tested within a real scenario, that is, with original LR images as the input. The obtained HR images demonstrated visible qualitative enhancements.

Remote Sensing Image Fusion Method Based on PCA and Curvelet Transform

In order to fuse two registered multi-spectral (MS) image and panchromatic (PAN) image in the same scene, a new remote sensing image fusion algorithm based on Principal Component Analysis (PCA) and Curvelet transform is proposed. The first principle component PC1 of MS image is extracted via PCA transform, at the same time, we perform the Morphology-Hat transform on the PAN image, and segment the transformed PAN image by the PCNN segmentation algorithm. Perform the Curvelet transform on the component PC1 of MS image and the PAN image after Morphology-Hat transform, and use different fusion rule to fuse different scale layers coefficients (coarse, detail and fine scale layer). For obtaining the fused image, we use the inverse Curvelet transform and inverse PCA transform to obtain the fused image. The experimental results illustrate that the proposed fusion algorithm outperforms Curvelet transform and other traditional fusion algorithms in whole such as intensity–hue–saturation, PCA, Brovey and Weighted Average both in visual effect and objective evaluation indexes (standard deviation, mean, information entropy, correlation coefficient, spectral distortions and deviation index).

A Novel Generalized Intensity-Hue-Saturation (GIHS) Based Pan-Sharpening Method With Variational Hessian Transferring

The popular intensity-hue-saturation (IHS) pan-sharpening method can provide high spatial quality while suffering from some spectral distortion, mainly due to the fact that it cannot estimate an accurate intensity image to substitute the original intensity image in the IHS space. To overcome this drawback, in this paper, we particularly use the modeling idea of variational complementary data fusion, and propose a novel yet highly effective pan-sharpening method with variational Hessian transferring in the generalized IHS (GIHS) transform domain, which aims to estimate a more accurate intensity image. More specifically, the novelty of proposed method consists in building a variational Hessian transferring model in the GIHS transform domain to transfer the Hessian-based spatial geometric information of panchromatic (Pan) image to the new intensity image and meanwhile take the spectral information preserving into consideration. Finally, the experimental results demonstrate the effectiveness of the proposed method which can perform higher spectral and spatial qualities, and higher efficiency than some state-of-the-art methods.

Weighted intensity hue saturation transform for image enhancement and pansharpening

In this paper, a weighted intensity hue saturation transform (WIHS) is proposed for image enhancement and pansharpening. The proposed transform obtains the intensity component of the classical intensity hue saturation (IHS) transform, by weighting the bands (red, green, and blue) of the image, whereas the hue and saturation components remain unchanged. The weight for each band is determined according to the brightness interval of the corresponding band and its standard deviation. The purpose of obtaining the intensity component by weighting is to keep more information from the bands of the input image. The classical intensity component used in image processing algorithms is replaced with the proposed weighted intensity component. The visual and quantitative comparisons show that the proposed transform is superior to the classical IHS transform.

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.

Optical SAR Images Fusion: Comparative Analysis of Resulting Images Data

Image fusion is a useful tool for integrating low spatial resolution multispectral (MS) images with a high spatial resolution panchromatic (PAN) image, thus producing a high resolution multispectral image for better understanding of the observed earth surface. A main proposed the research were the effectiveness of different image fusion methods while filtering methods added to speckle suppression in synthetic aperture radar (SAR) images. The quality assessment of the filtering fused image implemented by statistical parameter namely mean, standard deviation, bias, universal index quality image (UIQI) and root mean squared error (RMSE). In order to test the robustness of the image quality, either speckle noise (Gamma map filter) is intentionally added to the fused image. When comparing and testing result, Gram Scmidth (GS) methods have shown better results for good colour reproduction, as compared with high pass filtering (HPF). And the other hands, GS, and wavelet intensity hue saturation (W-IHS) have shown the preserving good colour with original image for Landsat TM data.

Comparison of Three Image Fusion Techniques Employed on High Resolution QuickBird Images

Image fusion involves integration of the geometric details of a high-resolution panchromatic (PAN) image and the spectral information of a low resolution multispectral (XS) image which is useful for regional planning and large scale urban mapping. Present study compares the effectiveness of three image fusion techniques namely, Principal Component Analysis (PCA), Wavelet Transform (WT) and Intensity Hue Saturation (IHS) to merge the XS information and PAN data of QuickBird satellite imagery. Comparison between the fused images obtained from the three fusion techniques is carried out on the basis of qualitative and quantitative evaluations implying, visual interpretation, inter-band correlation, correlation coefficient, standard deviation and mean. Results indicate that all three fusion techniques improves spatial resolution as well as spectral details, however, IHS technique provides the best spectral fidelity by preserving the XS integrity between all the bands under consideration.

A New Saliency-Driven Fusion Method Based on Complex Wavelet Transform for Remote Sensing Images

In remote sensing images, demands for spectral and spatial resolution vary from region to region. Regions with abundant texture and well-defined boundaries (like residential areas and roads) need more spatial details to provide better descriptions of various ground objects while regions such as farmland and mountains are mainly discriminated by spectral characteristic. However, most existing fusion algorithms for remote sensing images execute a unified processing in the whole image, leaving those important needs out of consideration. The employment of diverse fusion strategy for regions with different needs can provide an effective solution to this problem. In this letter, we propose a new saliency-driven fusion method based on complex wavelet transform. First, an adaptive saliency detection method based on clustering and spectral dissimilarity is presented to generate saliency factor for indicating diverse needs of the two kinds of resolutions in regions. Then, we combine nonlinear intensity–hue–saturation transform with multiresolution analysis based on dual-tree complex wavelet transform in order to complement each other’s advantages. Finally, saliency factor is employed to control the detail injection in the fusion, helping to satisfy different needs of different regions. Experiments reveal the validity and advantages of our proposal.

ESTIMATING CROP COVER FRACTION FROM DIGITAL COLOR IMAGES

Abstract. The use of automated methods to estimate crop cover fraction from digital color images has increased in recent years. Crop cover fraction can determine accurate, fast and inexpensive with this methods. A digital color images was acquired over each of the 30 sample fields in 2014 year at 2–3 week intervals. Study area has 15 sunflower fields and 15 corn fields. Digital color images were collected during 4 months, namely over the course of the growing season from sowing until harvesting to determine crop cover fraction. We used two approach to estimate crop cover fraction. In first method, the images were transformed from the RGB (red, green, blue) color space to the HSI (hue, intensity, saturation) color space. We used an object-based image analysis approach to classify the images into green vegetation and the other materials. In the second method, The Green Crop Tracker is less labor and time intensive than the object-based classification approach, is a viable alternative to ground-based methods. By comparing object-based classification method and Green Crop Tracker software 2014 growing season, results were obtained: There were high correlations between the estimations obtained by object-based classification method and Green Crop Tracker software (for 2014 R2 = 0.89). The relationship between two methods for 2014-23 sunflower field was calculated R2 = 0.97.

The effect of fusing Sentinel-2 bands on land-cover classification

ABSTRACTThe Sentinel-2 satellite currently provides freely available multispectral bands at relatively high spatial resolution but does not acquire the panchromatic band. To improve the resolution of 20 m bands to 10 m, existing pansharpening methods (Brovey transform [BT], intensity–hue–saturation [IHS], principal component analysis [PCA], the variational method [P + XS], and the wavelet method) required adjustment, which was achieved using higher resolution multispectral bands in the role of a panchromatic band to fuse bands at a lower spatial resolution. After preprocessing, six bands at lower resolution were divided into two groups because some image fusion methods (e.g. BT, IHS) are limited to a maximum of three input bands of a lower resolution at a time. With respect to the spectral range, the higher resolution band for the first group was synthesized from bands 4 and 8, and band 8 was selected for the second group. Given that one of the main remote sensing applications is land-cover classification, the classification accuracy of the fusion methods was assessed as well as the comparison with reference bands and pixels. The supervised classification methods were Maximum Likelihood Classifier, artificial neural networks, and object-based image analysis. The classification scheme contained five classes: water, built-up, bare soil, low vegetation, and forest. The results showed that most of the fusion methods, particularly P + XS and PCA, improved the overall classification accuracy, especially for the classes of forest, low vegetation, and bare soil and in the detection of coastlines. The least satisfying results were obtained from the wavelet method.

DETECTING FORESTS DAMAGED BY PINE WILT DISEASE AT THE INDIVIDUAL TREE LEVEL USING AIRBORNE LASER DATA AND WORLDVIEW-2/3 IMAGES OVER TWO SEASONS

Abstract. Pine wilt disease is caused by the pine wood nematode (Bursaphelenchus xylophilus) and Japanese pine sawyer (Monochamus alternatus). This study attempted to detect damaged pine trees at different levels using a combination of airborne laser scanning (ALS) data and high-resolution space-borne images. A canopy height model with a resolution of 50 cm derived from the ALS data was used for the delineation of tree crowns using the Individual Tree Detection method. Two pan-sharpened images were established using the ortho-rectified images. Next, we analyzed two kinds of intensity-hue-saturation (IHS) images and 18 remote sensing indices (RSI) derived from the pan-sharpened images. The mean and standard deviation of the 2 IHS images, 18 RSI, and 8 bands of the WV-2 and WV-3 images were extracted for each tree crown and were used to classify tree crowns using a support vector machine classifier. Individual tree crowns were assigned to one of nine classes: bare ground, Larix kaempferi, Cryptomeria japonica, Chamaecyparis obtusa, broadleaved trees, healthy pines, and damaged pines at slight, moderate, and heavy levels. The accuracy of the classifications using the WV-2 images ranged from 76.5 to 99.6 %, with an overall accuracy of 98.5 %. However, the accuracy of the classifications using the WV-3 images ranged from 40.4 to 95.4 %, with an overall accuracy of 72 %, which suggests poorer accuracy compared to those classes derived from the WV-2 images. This is because the WV-3 images were acquired in October 2016 from an area with low sun, at a low altitude.

Correlative Microscopy Combining Secondary Ion Mass Spectrometry and Electron Microscopy: Comparison of Intensity–Hue–Saturation and Laplacian Pyramid Methods for Image Fusion

Correlative microscopy combining various imaging modalities offers powerful insights into obtaining a comprehensive understanding of physical, chemical, and biological phenomena. In this article, we investigate two approaches for image fusion in the context of combining the inherently lower-resolution chemical images obtained using secondary ion mass spectrometry (SIMS) with the high-resolution ultrastructural images obtained using electron microscopy (EM). We evaluate the image fusion methods with three different case studies selected to broadly represent the typical samples in life science research: (i) histology (unlabeled tissue), (ii) nanotoxicology, and (iii) metabolism (isotopically labeled tissue). We show that the intensity-hue-saturation fusion method often applied for EM-sharpening can result in serious image artifacts, especially in cases where different contrast mechanisms interplay. Here, we introduce and demonstrate Laplacian pyramid fusion as a powerful and more robust alternative method for image fusion. Both physical and technical aspects of correlative image overlay and image fusion specific to SIMS-based correlative microscopy are discussed in detail alongside the advantages, limitations, and the potential artifacts. Quantitative metrics to evaluate the results of image fusion are also discussed.

PET and MRI image fusion based on combination of 2-D Hilbert transform and IHS method

BackgroundThe process of medical image fusion is combining two or more medical images such as Magnetic Resonance Image (MRI) and Positron Emission Tomography (PET) and mapping them to a single image as fused image. So purpose of our study is assisting physicians to diagnose and treat the diseases in the least of the time. MethodsWe used Magnetic Resonance Image (MRI) and Positron Emission Tomography (PET) as input images, so fused them based on combination of two dimensional Hilbert transform (2-D HT) and Intensity Hue Saturation (IHS) method. Evaluation metrics that we apply are Discrepancy (Dk) as an assessing spectral features and Average Gradient (AGk) as an evaluating spatial features and also Overall Performance (O.P) to verify properly of the proposed method. ResultsIn this paper we used three common evaluation metrics like Average Gradient (AGk) and the lowest Discrepancy (Dk) and Overall Performance (O.P) to evaluate the performance of our method. Simulated and numerical results represent the desired performance of proposed method. ConclusionsSince that the main purpose of medical image fusion is preserving both spatial and spectral features of input images, so based on numerical results of evaluation metrics such as Average Gradient (AGk), Discrepancy (Dk) and Overall Performance (O.P) and also desired simulated results, it can be concluded that our proposed method can preserve both spatial and spectral features of input images.