Abstract: This paper presents a novel feature point descriptor for the multispectral image case: Far-Infrared and Visible Spectrum images. It allows matching interest points on images of the same scene but acquired in different spectral bands. Initially, points of interest are detected on both images through a SIFT-like based scale space representation. Then, these points are characterized using an Edge Oriented Histogram (EOH) descriptor. Finally, points of interest from multispectral images are matched by finding nearest couples using the information from the descriptor. The provided experimental results and comparisons with similar methods show both the validity of the proposed approach as well as the improvements it offers with respect to the current state-of-the-art.

Abstract: Earth-observing satellites usually not only take ordinary red-green-blue images but also provide several images including the near-infrared and infrared spectrum. These images are called multispectral, for about four to seven different bands, or hyperspectral, for higher dimensional images of up to 210 bands. The drawback of the additional spectral information is that each spectral band has rather low spatial resolution. In this paper we propose a new variational method for sharpening high dimensional spectral images with the help of a high resolution gray-scale image while preserving the spectral characteristics used for classification and identification tasks. We describe the application of split Bregman minimization to our energy, prove convergence speed, and compare the split Bregman method to a descent method based on the ideas of alternating directions minimization. Finally, we show results on Quickbird multispectral as well as on AVIRIS hyperspectral data.

Abstract: Objectives We examined canopy gap structure and regeneration patterns at the landscape scale using a combination of remote sensing and field-based surveys.

Abstract: Background Improved maps of species distributions are important for effective management of wildlife under increasing anthropogenic pressures. Recent advances in lidar and radar remote sensing have shown considerable potential for mapping forest structure and habitat characteristics across landscapes. However, their relative efficacies and integrated use in habitat mapping remain largely unexplored. We evaluated the use of lidar, radar and multispectral remote sensing data in predicting multi-year bird detections or prevalence for 8 migratory songbird species in the unfragmented temperate deciduous forests of New Hampshire, USA. Methodology and Principal Findings A set of 104 predictor variables describing vegetation vertical structure and variability from lidar, phenology from multispectral data and backscatter properties from radar data were derived. We tested the accuracies of these variables in predicting prevalence using Random Forests regression models. All data sets showed more than 30% predictive power with radar models having the lowest and multi-sensor synergy (“fusion”) models having highest accuracies. Fusion explained between 54% and 75% variance in prevalence for all the birds considered. Stem density from discrete return lidar and phenology from multispectral data were among the best predictors. Further analysis revealed different relationships between the remote sensing metrics and bird prevalence. Spatial maps of prevalence were consistent with known habitat preferences for the bird species. Conclusion and Significance Our results highlight the potential of integrating multiple remote sensing data sets using machine-learning methods to improve habitat mapping. Multi-dimensional habitat structure maps such as those generated from this study can significantly advance forest management and ecological research by facilitating fine-scale studies at both stand and landscape level.

Abstract: Due to the harsh conditions of space, the detectors within satellite-based multispectral imagers are always at risk of damage or failure. In particular, 15 out of the 20 detectors that produce the 1.6- μm band 6 of Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua are either dead or noisy. In this paper, we describe a quantitative image restoration (QIR) algorithm that is able to accurately estimate and restore the data lost due to multiple-detector failure. The small number of functioning detectors is used to train a restoration function that is based on a multivariate regression using the information in a spatial-spectral window around each restored pixel. The information from other spectral bands allows QIR to perform well even when standard image interpolation breaks down due to large contiguous sections of the image being missing, as is the case for MODIS band 6 on Aqua. We present a comprehensive evaluation of the QIR algorithm by simulating the Aqua damage using the working 1.6- μm band of MODIS on Terra and then comparing the QIR restoration to the original (unbroken) Terra image. We also compare our results with other researchers' prior work that has been based on the assumption that band 6 could be approximated well solely as a function of the related band 7. We present empirical evidence that there is information in the other 500- and 250-m bands, excluding bands 6 and 7, that can inform the estimation of missing band 6 data. We demonstrate superior performance of QIR over previous algorithms as reflected by a reduced root-mean-square-error evaluation. The QIR algorithm may also be adapted to other cases and provides a powerful and general algorithm to mitigate the risks of detector damage in multispectral remote sensing.

Abstract: Image processing techniques that involve multispectral remotely sensed data are considered attractive for bathymetry applications as they provide a time- and cost-effective solution to water depths estimation. In this paper the potential of 8-bands image acquired by Worldview-2 satellite in providing precise depth measurements was investigated. Multispectral image information was integrated with available echo sounding and GPS data for the determination of the depth in the area of interest. In particular the main objective of this research was to evaluate the effectiveness of high spatial and spectral resolution of the new imagery data on water depth measurements using the Lyzenga linear bathymetry model. The existence of sea grass in a part of the study area influenced the linear relationship between water reflectance and depth. Therefore the bathymetric model was applied in three image parts: an area with sea grass, a mixed area and a sea grass-free area. In the last two areas the model worked successfully supported by the multiplicity of the imagery bands.

Abstract: Land cover data represent a fundamental data source for various types of scientific research. The classification of land cover based on satellite data is a challenging task, and an efficient classification method is needed. In this study, an automatic scheme is proposed for the classification of land use using multispectral remote sensing images based on change detection and a semi-supervised classifier. The satellite image can be automatically classified using only the prior land cover map and existing images; therefore human involvement is reduced to a minimum, ensuring the operability of the method. The method was tested in the Qingpu District of Shanghai, China. Using Environment Satellite 1(HJ-1) images of 2009 with 30 m spatial resolution, the areas were classified into five main types of land cover based on previous land cover data and spectral features. The results agreed on validation of land cover maps well with a Kappa value of 0.79 and statistical area biases in proportion less than 6%. This study proposed a simple semi-automatic approach for land cover classification by using prior maps with satisfied accuracy, which integrated the accuracy of visual interpretation and performance of automatic classification methods. The method can be used for land cover mapping in areas lacking ground reference information or identifying rapid variation of land cover regions (such as rapid urbanization) with convenience.

Abstract: Jan Svejkovsky – Ocean Imaging Corp. 201 Lomas Santa Fe Dr., Suite 370, Solana Beach CA 92075. E-mail – jan@oceani.com William Lehr – National Oceanographic and Atmospheric Administration, 7600 Sand Point Way NE, Seattle, WA 98115-6349. E-mail Bill.Lehr@noaa.gov Judd Muskat – California Department of Fish & Game, Office of Spill Prevention and Response, 1700 \"K\" Street, Sacramento, CA 95811. E-mail jmuskat@ospr.dfg.ca.gov George GraettingerNational Oceanographic and Atmospheric Administration, 7600 Sand Point Way NE, Seattle, WA 98115-6349. E-mail George.Graettinger@noaa.gov Joseph Mullin – Joseph Mullin Consulting, LLC 8003 Chestnut Grove Road Frederick, MD 21701-3405. (Retired from Bureau of Ocean Energy Management, Regulation, and Enforcement after research was conducted). E-mail josephmullinconsulting@comcast.net

Abstract: This paper presents a new approach to multiscale segmentation of satellite multispectral imagery using edge information. The Canny edge detector is applied to perform multispectral edge detection. The detected edge features are then utilized in a multiscale segmentation loop, and the merge procedure for adjacent image objects is controlled by a separability criterion that combines edge information with segmentation scale. The significance of the edge is measured by adjacent partitioned regions to perform edge assessment. The present method is based on a half-partition structure, which is composed of three steps: single edge detection, separated pixel grouping, and significant feature calculation. The spectral distance of the half-partitions separated by the edge is calculated, compared, and integrated into the edge information. The results show that the proposed approach works well on satellite multispectral images of a coastal area.

Abstract: Palmprints have been widely studied for biometric recognition for many years. Traditionally, a white light source is used for illumination. Recently, multispectral imaging has drawn attention because of its high recognition accuracy. Multispectral palmprint systems can provide more discriminant information under different illuminations in a short time, thus they can achieve better recognition accuracy. Previously, multispectral palmprint images were taken as a kind of multi-modal biometrics, and the fusion scheme on the image level or matching score level was used. However, some spectral information will be lost during image level or matching score level fusion. In this study, we propose a new method for multispectral images based on a quaternion model which could fully utilize the multispectral information. Firstly, multispectral palmprint images captured under red, green, blue and near-infrared (NIR) illuminations were represented by a quaternion matrix, then principal component analysis (PCA) and discrete wavelet transform (DWT) were applied respectively on the matrix to extract palmprint features. After that, Euclidean distance was used to measure the dissimilarity between different features. Finally, the sum of two distances and the nearest neighborhood classifier were employed for recognition decision. Experimental results showed that using the quaternion matrix can achieve a higher recognition rate. Given 3000 test samples from 500 palms, the recognition rate can be as high as 98.83%.

Abstract: Over the last decade DigitalGlobe (DG) has built and launched a series of remote sensing satellites with steadily increasing capabilities: QuickBird, WorldView-1 (WV-1), and WorldView-2 (WV-2). Today, this constellation acquires over 2.5 million km2 of imagery on a daily basis. This paper presents the configuration and performance capabilities of each of these satellites, with emphasis on the unique spatial and spectral capabilities of WV-2. WV-2 employs high-precision star tracker and inertial measurement units to achieve a geolocation accuracy of 5 m Circular Error, 90% confidence (CE90). The native resolution of WV-2 is 0.5 m GSD in the panchromatic band and 2 m GSD in 8 multispectral bands. Four of the multispectral bands match those of the Landsat series of satellites; four new bands enable novel and expanded applications. We are rapidly establishing and refreshing a global database of very high resolution (VHR) 8-band multispectral imagery. Control moment gyroscopes (CMGs) on both WV-1 and WV-2 improve collection capacity and provide the agility to capture multi-angle sequences in rapid succession. These capabilities result in a rich combination of image features that can be exploited to develop enhanced monitoring solutions. Algorithms for interpretation and analysis can leverage: 1) broader and more continuous spectral coverage at 2 m resolution; 2) textural and morphological information from the 0.5 m panchromatic band; 3) ancillary information from stereo and multi-angle collects, including high precision digital elevation models; 4) frequent revisits and time-series collects; and 5) the global reference image archives. We introduce the topic of creative fusion of image attributes, as this provides a unifying theme for many of the papers in this WV-2 Special Session.

Abstract: Multispectral remote sensing of the Earth using Landsat sensors was ushered on July 23, 1972, with the launch of Landsat-1. Following that success, four more Landsat satellites were launched, and each of these carried the Multispectral Scanner System (MSS). These five sensors provided the only consistent multispectral space-based imagery of the Earth's surface from 1972 to 1982. This work focuses on developing both a consistent and absolute radiometric calibration of this sensor system. Cross-calibration of the MSS was performed through the use of pseudoinvariant calibration sites (PICSs). Since these sites have been shown to be stable for long periods of time, changes in MSS observations of these sites were attributed to changes in the sensors themselves. In addition, simultaneous data collections were available for some MSS sensor pairs, and these were also used for cross-calibration. Results indicated substantial differences existed between instruments, up to 16%, and these were reduced to 5% or less across all MSS sensors and bands. Lastly, this paper takes the calibration through the final step and places the MSS sensors on an absolute radiometric scale. The methodology used to achieve this was based on simultaneous data collections by the Landsat-5 MSS and Thematic Mapper (TM) instruments. Through analysis of image data from a PICS location and through compensating for the spectral differences between the two instruments, the Landsat-5 MSS sensor was placed on an absolute radiometric scale based on the Landsat-5 TM sensor. Uncertainties associated with this calibration are considered to be less than 5%.

Abstract: The information content of a single image is mainly limited by the spatial and spectral resolution of the imaging system. Current imaging systems offer a trade-off between high spatial and high spectral resolutions. No single system offers both of these characteristics. In order to obtain the both characteristics in a single image, that is high spatial and spectral resolutions, image fusion technique can be employed. Image fusion is a process of combining two or more images into an image. It can extract features from source images, and provide more information than one image can. In this research, we propose a novel method for multimodality remote sensing image fusion. As a possible remedy for this problem we propose a technique for the fusion of Panchromatic and Multispectral images based on Curvelet transform. This paper introduces new application of the curvelet in multispectral remote sensing image fusion. The methodological approaches proposed in this paper result in merged images with improved quality with respect to those obtained by IHS, DWT, wavelet a trous algorithm and ridgelet and curvelet transform. Results show proposed method preserves more spectral features with less spatial distortion.

Abstract: This study presents a detailed analysis of Iterative Self Organizing Data Analysis (ISODATA) clustering for multispectral data classification ISODATA is an unsupervised classification method which assumes that each class obeys a multivariate normal distribution, hence requires the class means and covariance matrices for each class In this study, we use ISODATA to classify a diverse tropical land covers recorded from Landsat 5 TM satellite The classification is carefully examined using visual analysis, classification accuracy, band correlation and decision boundary The results show that ISODATA is able to detect eight classes from the study area with 93% agreement with the reference map The behavior of mean and standard deviation of the classes in the decision space is believed to be one of the main factors that enable ISODATA to classify the land covers with relatively good accuracy

Abstract: The aim of this study is to improve classification results of multispectral satellite imagery for supporting flood risk assessment analysis in a catchment area in Cyprus For this purpose, precipitation and ground spectroradiometric data have been collected and analyzed with innovative statistical analysis methods Samples of regolith and construction material were in situ collected and examined in the spectroscopy laboratory for their spectral response under consecutive different conditions of humidity Moreover, reflectance values were extracted from the same targets using Landsat TM/ETM+ images, for drought and humid time periods, using archived meteorological data The comparison of the results showed that spectral responses for all the specimens were less correlated in cases of substantial humidity, both in laboratory and satellite images These results were validated with the application of different classification algorithms (ISODATA, maximum likelihood, object based, maximum entropy) to satellite images acquired during time period when precipitation phenomena had been recorded

Abstract: Pan-sharpening of remote sensing multispectral imagery directly influences the accuracy of interpretation, classification, and other data mining methods. Different tasks of multispectral image analysis and processing require specific properties of input pan-sharpened multispectral data such as spectral and spatial consistency, complexity of the pan-sharpening method, and other properties. The quality of a pan-sharpened image is assessed using quantitative measures. Generally, the quantitative measures for pan-sharpening assessment are taken from other topics of image processing (e.g., image similarity indexes), but the applicability basis of these measures (i.e., whether a measure provides correct and undistorted assessment of pan-sharpened imagery) is not checked and proven. For example, should (or should not) a quantitative measure be used for pan-sharpening assessment is still an open research topic. Also, there is a chance that some measures can provide distorted results of the quality assessment and the suitability of these quantitative measures as well as the application for pan-sharpened imagery assessment is under question. The aim of the authors is to perform statistical analysis of widely employed measures for remote sensing imagery pan-sharpening assessment and to show which of the measures are the most suitable for use. To find and prove which measures are the most suitable, sets of multispectral images are processed by the general fusion framework method (GFF) with varying parameters. The GFF is a type of general image fusion method. Variation of the method parameter set values allows one to produce imagery data with predefined quality (i.e., spatial and spectral consistency) for further statistical analysis of the assessment measures. The use of several main multispectral sensors (Landsat 7ETM+, IKONOS, and WorldView-2) imagery allows one to assess and compare available quality assessment measures and illustrate which of them are most suitable for each satellite.

Abstract: This paper evaluates the performance of SIFT and SURF for cross band matching of multispectral images. The evaluation is based on matching a reference spectral image with the images acquired at different spectral bands. The reference image possesses scale and (in-plane) rotational differences in addition to spectral variations. Additive white Gaussian noise is also added to compare performance degradation at different noise levels. We use the precision and repeatability criteria for performance evaluation. Experimental results demonstrate that SIFT performs better than SURF in multispectral environment.

Abstract: It is a well-known problem of remotely sensed images classification due to its complexity. This paper proposes a remotely sensed image classification method based on weighted complex network clustering using the traditional K-means clustering algorithm. First, the degree of complex network and clustering coefficient of weighted feature are used to extract the features of the remote sensing image. Then, the integrated features of remote sensing image are combined to be used as the basis of classification. Finally, K-means algorithm is used to classify the remotely sensed images. The advantage of the proposed classification method lies in obtaining better clustering centers. The experimental results show that the proposed method gives an increase of 8% in accuracy compared with the traditional K-means algorithm and the Iterative Self-Organizing Data Analysis Technique (ISODATA) algorithm.

Abstract: An image processing system combines higher-resolution panchromatic images and lower resolution multispectral images using a hyperspherical color space pan-sharpening technique. By converting the multispectral images into a hyperspherical color space, the intensities of the multispectral images can be intensity matched to the intensities of the panchromatic image and then retransformed back to the original color space. The intensity matching can utilize a number of techniques, including, but not limited to, direct substitution of the intensities of the panchromatic image for the intensities of the multispectral images, modification of the intensities of the multispectral images based on predefined statistical models and modification of the intensities of the multispectral images based on dynamically generated statistical models and a selected sharpening parameter β.

Abstract: A novel image fusion method using IKONOS satellite images In satellite remote sensing, spatial resolutions of multispectral images over a particular region can be enhanced using better spatial resolution panchromatic images for the same region by a process called image fusion, or more generally data fusion. A fusion method is considered successful, if the spatial detail of the panchromatic image is transferred into the multispectral image and the spectral content of the original multispectral image is preserved in the fused product. This research proposes a novel image fusion algorithm which takes aim at producing both spatially enhanced and spectrally appealing fused multispectral images. In the proposed method, first an intermediary image is created using original panchromatic and multispectral images. This intermediary image contains the high frequency content of the panchromatic source image such that it is the one closest to the given multispectral source image (upsampled) by a natural semi inner product defined. The final fused image is obtained by applying a function which performs convex linear combination of the intermediary image and the upsampled multispectral image. The function used depends on the local standard deviations of the source images.To test the performance of the method, the images from IKONOS sensor are fused using the Brovey, IHS, PCA, wavelet transform based methods, and the proposed method. Both visual and quantitative evaluation results indicate that the proposed method yields to both spectrally and spatially appealing results as the wavelet transform based method, and it gives a better performance when both spatial detail enhancement and spectral content preservation in the fused products are considered. It is also obvious that the method has a potential to get better results if a better fitting, more complex function is found.

Abstract: In this letter, analyze the satellite images by using discrete cosine transform and singular value decomposition. The proposed technique presents an advance multiband satellite colour, contrast improvement technique of a poor-contrast satellite images. The input image is decomposed into the two frequency sub bands by using DCT and estimates the singular value matrix of the lowâ"low sub band image and then it reconstructs the enhanced image by applying inverse DCT. This technique is useful for the betterment of the INSAT as well as LANDSAT satellite image for the feature extraction purpose. The singular value matrix represents the intensity information of the given image and any change on the singular values change the intensity of the input image. The proposed technique converts the image into the DCT-SVD domain and after normalizing the singular value matrix, the enhanced image is reconstructed by using IDCT. The visual and quantitative results suggest that the proposed DCT-SVD method clearly shows the increased efficiency and flexibility of the proposed method over the exiting methods such as the Decor relation Stretching, Linear Contrast Stretch, GHE and DWT-SVD based techniques. The experimental results show the superiority of the proposed method over conventional methods.

Abstract: A multispectral camera acquires spectral color images with high fidelity by splitting the light spectrum into more than three bands. Because of the shift of focal length with wavelength, the focus of each channel should be mechanically adjusted in order to obtain sharp images. Because progressive adjustment is quite time consuming, the clear focus must be determined by using a limited number of images. This paper exploits the symmetry of focus measure distribution and proposes a simple yet efficient autofocus method. The focus measures are computed using first-order image derivatives, and the focus curve is obtained by spline interpolation. The optimal focus position, which maximizes the symmetry of the focus measure distribution, is then computed according to distance metrics. The effectiveness of the proposed method is validated in the multispectral camera system, and it is also applicable to relevant imaging systems.

Abstract: Unsupervised classification is a popular tool for unlabeled datasets in data mining and exploratory data analysis, such as K-means and Fuzzy C-mean. Although these unsupervised techniques have demonstrated substantial success for satellite imagery, they have some limitations. The initialization number of clusters in K-means clustering application is often needed in advance as an input parameter to the algorithm. Our previous paper regarding the initialization number of clusters in K-means clustering application with a co-occurrence matrix technique has been published. Although our previous approach regarding the number of cluster was discovered, but it was limited to count a number of peaks in occurrence matrix as the number of clusters by manual counting. The best of our previous approach need to automatically find and count a number of peaks in occurrence matrix. In this research, we assume that the satellite imagery is given and we have no knowledge beforehand for segmentation. Hence, this paper presents a simple, parameter-free K-means method for K-means in satellite imagery clustering application to determine the initialization number of clusters with image processing algorithms based on the co-occurrence matrix technique. A maxima technique is proposed for automatic counting a number of peaks in occurrence matrix as the number of clusters. The parameter-free method was tested with hyperspectral imagery and multispectral imagery. The results from the tests confirm the effectiveness of the proposed method in K-means method and compared with isodata algorithm.

Abstract: The invention discloses an image characteristic registration based geometrical fine correction method for an aviation multispectral remote sensing image, comprising following steps of: 1) utilizing a Sobel edge detection operator to carry out edge extraction on both an aviation multispectral remote sensing image to be registered and a normal incidence high-resolution satellite image which is taken as a standard; 2) utilizing a Harris angular point detection method to detect angular points of the aviation multispectral remote sensing image to be registered and the normal incidence high-resolution satellite image which is taken as the standard; 3) carrying out rough correlation between the two images through a correlation method; 4) carrying out fine correlation between the angular points of the two images through calculating supporting strength; 5) carrying out back calculation to obtain a multinomial coefficient according to a multinomial model; and 6) carrying out gray level re-sampling by adopting a bilinear interpolation to obtain a registered image. According to the image characteristic registration based geometrical fine correction method for the aviation multispectral remote sensing image, disclosed by the invention, the geometrical correction problem of the aviation multispectral remote sensing image lacking of a ground reference point can be better solved and the geometrical correction precision of an aviation multispectral scanner is improved, so that an aviation remote sensing technology can be better applied to production livings of national economy.