Intensity, Hue And Saturation Research Articles

Remote Sensing Image Fusion Based on Wavelet Transform

In this study, we propose an effective image fusion method which applies the Wavelet Transform algorithm to the remote sensing image fusion. The wavelet transform decomposes the original image into a series of frequency channels, so two images with distinct characteristics can be fused separately in multiple frequency domains. By applying the Wavelet Transform algorithm, we can get a better effect than other traditional fusion algorithms such as IHS (Intensity, Hue, and Saturation).

Fusion of High-Resolution SAR and Optical Imageries Based on a Wavelet Transform and IHS Integrated Algorithm

Multi-sensor remote sensing data can significantly improve the interpretation and usage of large volume data sources. A combination of satellite Synthetic Aperture Radar (SAR) data and optical sensors enables the use of complementary features of the same image. In this paper, SAR data is injected into optical image using a combining fusion method based on the integration of wavelet Transform and IHS (Intensity, Hue, and Saturation) transform. Not only to preserve the spectral information of the original (MS) image, but also to maintain the spatial content of the high-resolution SAR image. Two data sets are used to evaluate the proposed fusion algorithm: one of them is Pleiades, Turkey and the other one is Boulder, Colorado, USA. The different fused outputs are compared using different image quality indices. Visual and statistical assessment of the fused outputs displays that the proposed approach has an effective translation from SAR to the optical image. Hence, enhances the SAR image interpretability.

Evaluation of Optical and Radar Images Integration Methods for LULC Classification in Amazon Region

The main objective of this study is to evaluate different methods to integrate (fusion and combination) Synthetic Aperture Radar (SAR) Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band SAR (PALSAR-1) (Fine Beam Dual mode-FDB) and LANDSAT images in order to identify those which lead to higher accuracy of land-use and land-cover (LULC) mapping in an agricultural frontier region in Amazon. One method used to integrate the multipolarized information in SAR images before the fusion process was also evaluated. In this method, the first principal component (PC1) of SAR data was used. Color compositions of fused data that presented better LULC classification were visually analyzed. Considering the proposed objective, the following fusion methods must be highlighted: Ehlers , Wavelet a trous, Intensity, Hue and Saturation (IHS), and selective principal component analysis (SPC). These latter three methods presented good results when processed using PC1 from ALOS/PALSAR-1 FBD backscatter filtered image or three SAR extracted and selected features. These results corroborate with the applicability of the proposed method for SAR data information integration. Distinct methods better discriminate different LULC classes. In general, densely forested classes were better characterized by the Ehlers _TM6 fusion method, in which at least the polarization HV was used. Intermediate and initial regeneration classes were better discriminated using SPC-fused data with PC1 of ALOS/PALSAR-1 FBD data. Bare soil and pasture classes were better discriminated in optical features and the PC1 of ALOS/PALSAR-1 FBD data fused by the IHS method. Soybean with approximately 40 days from seeding was better discriminated in image classification obtained from ALOS/PALSAR-1 FBD image.

RGB and multispectral UAV image fusion for Gramineae weed detection in rice fields

In this paper, a new method to fuse low resolution multispectral and high resolution RGB images is introduced, in order to detect Gramineae weed in rice fields with plants at 50 days after emergence (DAE).The images are taken from a fixed-wing unmanned aerial vehicle (UAV) at 60 and 70 m altitude. The proposed method combines the texture information given by a high resolution red–green–blue (RGB) image and the reflectance information given by a low resolution multispectral (MS) image, to obtain a fused RGB-MS image with better weed discrimination features. After analyzing the normalized difference vegetation index (NDVI) and normalized green red difference index (NGRDI) for weed detection, it was found that NGRDI presents better features. The fusion method consists of decomposing the RGB image using the intensity, hue and saturation (IHS) transformation, then, a second order Haar wavelet transformation is applied to the intensity layer (I) and the NGRDI image. From this transformation, the low–low (LL) coefficients of the NGRDI image are replaced by the LL coefficients of the I layer. Finally, the fused image is obtained by transforming the new wavelet coefficients to RGB space. To test the method, a one hectare experimental plot with rice plants at 50 DAE with Gramineae weeds was selected. Additionally, to compare the performance of the method, two indices were used, specifically, the M/MGT index which is the percentage of detected weed area, and the MP index which indicates the precision of weed detection. These indices were evaluated in four validation zones using three Neural Networks (NN) detection systems based on three types of images; namely, RGB, RGB + NGRDI, and fused RGB-NGRDI. The best weed detection performance was obtained by the NN with the fused image, with M/MGT index between 80 and 108% and MP between 70 and 85%.

Efficient pan-sharpening of satellite images with the contourlet transform

Recent studies show that hybrid panchromatic sharpening (pan-sharpening) methods using the non-sub-sampled contourlet transform (NSCT) and classical pan-sharpening methods such as intensity, hue and saturation (IHS), principal component analysis (PCA), and adaptive principal component analysis (APCA) reduce spectral distortion in pan-sharpened images. The NSCT is a shift-invariant multi-resolution decomposition. It is based on non-sub-sampled pyramid (NSP) decomposition and non-sub-sampled directional filter banks (NSDFBs). We compare the performance of the APCA–NSCT using different NSP filters, NSDFB filters, number of decomposition levels, and number of orientations in each level on SPOT 4 data with a spatial resolution ratio of 1:2, and Quickbird data with a spatial resolution ratio of 1:4. Experimental results show that the quality of pan-sharpening of remote-sensing images of different spatial resolution ratios using the APCA–NSCT method is affected by NSCT parameters. For the NSP, the ‘maxflat’ filters have the best quality, while the ‘sk’ filters give the best quality for the NSDFB. Changing the number of orientations at the same level of decomposition in the NSCT has a small effect on both the spectral and spatial qualities. The spectral and spatial qualities of pan-sharpened images mainly depend on the number of decomposition levels. Too few decomposition levels result in poor spatial quality, while excessive levels of decomposition result in poor spectral quality. Two levels of decomposition in the case of SPOT 4 data with a spatial resolution ratio of 1:2 achieve the best results. Also, three levels of decomposition in the case of QuickBird data with a spatial resolution ratio of 1:4 show the best results.

Spectral Information Analysis from Multisensor Image Fusion for Land Use/Land Cover Classification in a Tropical Area: A Case Study in Bogor, Indonesia

This paper evaluates the analysis of spectral information using RADARSAT, SPOT Panchromatic, and Landsat-TM images for land use/land cover classification in a tropical area. The case study is of Bogor, located in West Java, Indonesia. In order to increase spectral contrast of the Landsat-TM image, two procedures for information compression, colour composite and PCA (Principal Component Analysis) were tested. The best five RGB colour composites were produced, which were visually and statistically investigated, including the RGB colour composites of PC-1, PC-2, and PC-3, which were created from standard and selective principal component analysis (PCA), respectively. To merge images, two spectral-based image fusion techniques were applied: IHS (Intensity, Hue, and Saturation) and Brovey transforms. A land use/land cover classification using IHS transform from the RGB colour composites of PC-1, PC-2, and PC-3, where the intensity was replaced by SPOT Panchromatic, was the best result. It has an overall accura...

A Wavelet and IHS Integration Method to Fuse High Resolution SAR with Moderate Resolution Multispectral Images

Synthetic aperture radar (SAR) imaging can be a feasible alternative or a complement to traditional optical remote sensing techniques because it does not depend on solar illumination and weather conditions. The high spatial resolution of SAR, such as the Intermap STAR-3i airborne SAR image with 1.25 m spatial resolution, makes it applicable for high spatial resolution mapping purposes. However, difficulties sometimes exist in the interpretation of SAR images. Image fusion presents an alternative to improve the interpretability of SAR images by fusing the color information from moderate spatial resolution multispectral (MS) images. In this paper, a new fusion method based on the integration of wavelet transform and IHS (Intensity, Hue, and Saturation) transform is proposed for SAR and MS fusion to maintain the spectral content of the original MS image while retaining the spatial detail of the high-resolution SAR image. Three data sets are used to evaluate the proposed fusion method: two sets are airborne SAR images with MS images at different spatial resolutions; the other set is a Radarsat image with a Landsat TM image. The fusion results are evaluated visually and statistically. The evaluation shows that successful results are achieved in the fusion of all SAR and MS images from a variety of sensors with significant spatial and spectral variations by using the proposed image fusion method.

The use of Remote Sensing Technology in geological Investigation and mineral Detection in El Azraq-Jordan

This study examines the use of Remote Sensing (RS) technology in geological studies in El Azraq area. LandSat Enhanced Thematic Mapper plus (ETM+) and Radar SAR images were used to (i) classify the various geological units found in El Azraq area located in the North-East of Jordan, (ii) discriminate the lithology and structure of this area, and (iii) delineate the associated zones of hydrothermal alteration. A wide variety of digital image processing techniques were applied such as the Principal Components (PC) analysis, ratioing and Intensity, Hue and Saturation (IHS) transformation. The color composite of Principal Components (1, 2, and 3), the ratio images (3/1, 4/3, and 5/7) and the IHS (1, 3, 5) enabled us to determine the different types of igneous rocks in the study area. A remote sensed lineament map was produced using two different methods: (i) application of directional filters and edge enhancement; and (ii) data fusion of ETM+ with SAR image. The selective PC analysis of ETM+ using bands 1, 3, 4, and 5 was used in mapping iron and iron oxide bearing minerals. The same method was also applied using bands 1, 4, 5, and 7 for hydroxyl bearing minerals detection. Finally, the hyper spectral technique was used for detecting the different minerals in the study area based on the spectral library of minerals.

TM and IRS-1C-PAN data fusion using multiresolution decomposition methods based on the 'a tròus' algorithm

Image fusion represents an important tool for remote sensing data elaborations. This technique is used for many purposes. Very often it is used to produce improved spatial resolution. The most common situation is represented by a pair of images: the first acquired by a multispectral sensor with a pixel size greater than the pixel size of the second image given by a panchromatic sensor (PAN). Starting from these images fusion produces a new multispectral image with a spatial resolution equal, or close, to that of the PAN. Very often fusion introduces important distortions on the pixel spectra. This fact could compromise the extraction of information from the image, especially when using an automatic algorithm based on spectral signature such as in the case of image classification. In this work we present the analysis of two fusion methods based on multiresolution decomposition obtained using the 'a tròus' algorithm and applied to a pair of images acquired by Thematic Mapper (TM) and Indian Remote Sensing (IRS)-1C-PAN sensors. The methods studied are also compared with two classical fusion methods, the intensity, hue and saturation (IHS) and standardized principal components (SPC). Fused results are studied and compared using various tests including supervised classification. Most of the tests used have been extracted from literature regarding the assessment of spatial and spectral quality of fused images. This study shows that the methods based on multiresolution decomposition outperform the classical fusion methods considered with respect to spectral content preservation. Moreover, it is shown that some of the quality tests are more significant than others. The discussion of this last aspect furnishes important indications for data quality assessment methods.

Evaluation of various digital image processing techniques for detection of coastal wetlands using ERS-1 SAR data

One of the problems associated with synthetic aperture radar (SAR) data analysis is the presence of random noise or speckle SAR data, being achromatic in nature, which offers very limited scope for the detection and delineation of various terrain features. ERS-1 SAR data for the coastal region of West Bengal, India were processed (a) to suppress the random noise using various filters, (b) to generate the intensity, hue and saturation (IHS) transform from temporal SAR data, and (c) to study the synergism of SAR data with optical sensor data. The results indicate that the Gamma MSP filter with a 5 5 pixel kernel size has been the most efficient in suppressing the noise and concurrently improving the image contrast. The IHS transform of temporal SAR data made it easier to discriminate between various wetland categories. This was also the case with hybrid image generated by the Indian Remote Sensing Satellite (IRS-1B) Linear Imaging and Self-scanning Sensor (LISS-II) data when compared to SAR data alone.

Design and VLSI implementation of an ASIC for real-time manipulation of digital colour images

This paper presents the design and VLSI implementation, on a single chip, of a new real-time colour space converter, which performs the transformation of the RGB colour coordinates to the intensity, hue and saturation (IHS) colour space. This high speed VLSI chip is designed to work at 13.3 MHz and can process high resolution colour images of up to 697 pixels per horizontal line. Its minimum rate of operation is 133 MIPS. The high speed of operation was achieved by pipelining the data in a vector fashion. The chip was implemented using a DLM, 1.0 μm, N-well, CMOS process provided by European Silicon Structures and the die size dimensions for the chip are 5.669 mm x 6.144 mm = 34.830 mm 2. The chip is intended to be used as the front end of colour machine vision systems in inspection tasks and in autonomous applications, where the need for short processing times is crucial. Finally, simulation results exhibiting the usefulness of the IHS colour space transform using the proposed ASIC and real colour image data are also presented.

Location of kimberlites using Landsat Thematic Mapper images and aerial photographs: the Redondao diatreme, Brazil

Abstract Landsat Thematic Mapper images and aerial photographs were used in the detection of kimberlile-derived materials in the Redondao test site. In this area kimberlite-derived soils show a flora constituted mainly by grasses and shrubs, which differ from the surrounding savanna-park (cerrado) vegetation cover. Band-ratio images were able to distinguish kimberlite-derived materials by enhancing areas with different vegetation covers. However, the coarse spatial resolution of Landsat-TM images compared with the spatial variability of the study area, and the removal of topographic shadowing effects on ratio images blurred several landscape features. To increase discrimination, Landsat Thematic Mapper ratio images were merged with digitized aerial photographs through intensity, hue and saturation (IHS) colour transforms. The resulting merged colour composite highlighted the spatial and spectral features of the study area permitting an accurate definition of the kimberlite-derived materials within the Redondao diatreme.