Multispectral Image Data Research Articles

Ship-Iceberg Discrimination in Sentinel-2 Multispectral Imagery by Supervised Classification

The European Space Agency Sentinel-2 satellites provide multispectral images with pixel sizes down to 10 m. This high resolution allows for fast and frequent detection, classification and discrimination of various objects in the sea, which is relevant in general and specifically for the vast Arctic environment. We analyze several sets of multispectral image data from Denmark and Greenland fall and winter, and describe a supervised search and classification algorithm based on physical parameters that successfully finds and classifies all objects in the sea with reflectance above a threshold. It discriminates between objects like ships, islands, wakes, and icebergs, ice floes, and clouds with accuracy better than 90%. Pan-sharpening the infrared bands leads to classification and discrimination of ice floes and clouds better than 95%. For complex images with abundant ice floes or clouds, however, the false alarm rate dominates for small non-sailing boats.

A new take on measuring relative nutritional density: The feasibility of using a deep neural network to assess commercially-prepared puréed food concentrations

Dysphagia affects 590 million people worldwide and increases risk for malnutrition. Puréed food may reduce choking, however preparation differences impact nutrient density making quality assurance necessary. This paper is the first study to investigate the feasibility of computational puréed food nutritional density analysis using an imaging system. Motivated by a theoretical optical dilution model, a novel deep neural network (DNN) was evaluated using 390 samples from thirteen types of commercially prepared purées at five dilutions. The DNN predicted relative concentration of the purée sample (20%, 40%, 60%, 80%, 100% initial concentration). Data were captured using same-side reflectance of multispectral imaging data at different polarizations at three exposures. Experimental results yielded an average top-1 prediction accuracy of 92.2% ± 0.41% with sensitivity and specificity of 83.0% ± 15.0% and 95.0% ± 4.8%, respectively. This DNN imaging system for nutrient density analysis of puréed food shows promise as a novel tool for nutrient quality assurance.

Development of a spectral‐pattern‐analysis‐based method for automated water body extraction using Landsat image data: A case study in central Vietnam and southern Laos

AbstractWater body mapping is an important application of optical remote sensing. Methods for classifying water bodies using multispectral image data have been successfully developed for water resource monitoring and management. However, in most cases, these traditional methods provide only partial automation capabilities. In this study, we propose a new spectral‐pattern‐analysis‐based (SPAB) method for water body extraction using simplified spectral patterns (SSPs). Simplified spectral patterns are a generalized transformation of spectral patterns from analogue to digital format, realized by nonrepetitive pairwise comparison of reflectance values between two bands. Simplified spectral patterns allow for the direct incorporation of spectral patterns into the recognition process. The advantage of this method compared to traditional methods is that the SPAB method allows automation of water body classification using an SSP database. In this study, we selected a mountainous region in central Vietnam and southern Laos as a study site. Four Landsat 8 OLI scenes from 2015 and four Landsat 7 ETM+ scenes from 2001 and 2002 were used. The results of our proposed method were compared with visual interpretation, normalized differential water index (NDWI) thresholding, and the Global Inland Water (GIW) dataset provided by the Global Land Cover Facility of the University of Maryland. It is concluded that the classification results of the SPAB method agree by more than 98.0% with NDWI results for Landsat 8 OLI images, and by more than 95.0% with the GIW dataset for Landsat 7 ETM+ images.

Effect of the Vegetation Fire on Backscattering: An Investigation Based on Sentinel-1 Observations

This paper aims at investigating the potential of Sentinel-1 C-band synthetic aperture radar (SAR) observations for detecting fire scars in vegetated areas at regional scale. A comprehensive analysis of the backscattering coefficients is carried out. The experimental analysis is conducted by analyzing the scenario of the Sardinia Island, which is one of the Italian regions most affected by fire events over the summer season. The detection capability of fire scars in such an environment is demonstrated by exploiting information extracted from dual-polarized SAR data. Our results reveal a significant decrease of the VH response over the fire-disturbed forests, thus, highlighting the effectiveness of such cross-polarized observations. In order to prove the validity of the proposed approach for the detection of fire scars in the vegetation layer, the results of the conducted experiments have been suitably compared with burned areas identified by using an existing fuzzy-based algorithm, which has been applied to multispectral Landsat-8 operational land imager data. This investigation opens the way to systematic methods for monitoring fire scars in heterogeneous environments, and in particular in fire-prone Mediterranean ecosystems.

THE EFFECT OF SHADOW AREA ON SGM ALGORITHM AND DISPARITY MAP REFINEMENT FROM HIGH RESOLUTION SATELLITE STEREO IMAGES

Abstract. Semi Global Matching (SGM) algorithm is known as a high performance and reliable stereo matching algorithm in photogrammetry community. However, there are some challenges using this algorithm especially for high resolution satellite stereo images over urban areas and images with shadow areas. As it can be seen, unfortunately the SGM algorithm computes highly noisy disparity values for shadow areas around the tall neighborhood buildings due to mismatching in these lower entropy areas. In this paper, a new method is developed to refine the disparity map in shadow areas. The method is based on the integration of potential of panchromatic and multispectral image data to detect shadow areas in object level. In addition, a RANSAC plane fitting and morphological filtering are employed to refine the disparity map. The results on a stereo pair of GeoEye-1 captured over Qom city in Iran, shows a significant increase in the rate of matched pixels compared to standard SGM algorithm.

Fusion of Landsat 8 OLI and Sentinel-2 MSI Data

Sentinel-2 is a wide-swath and fine spatial resolution satellite imaging mission designed for data continuity and enhancement of the Landsat and other missions. The Sentinel-2 data are freely available at the global scale, and have similar wavelengths and the same geographic coordinate system as the Landsat data, which provides an excellent opportunity to fuse these two types of satellite sensor data together. In this paper, a new approach is presented for the fusion of Landsat 8 Operational Land Imager and Sentinel-2 Multispectral Imager data to coordinate their spatial resolutions for continuous global monitoring. The 30 m spatial resolution Landsat 8 bands are downscaled to 10 m using available 10 m Sentinel-2 bands. To account for the land-cover/land-use (LCLU) changes that may have occurred between the Landsat 8 and Sentinel-2 images, the Landsat 8 panchromatic (PAN) band was also incorporated in the fusion process. The experimental results showed that the proposed approach is effective for fusing Landsat 8 with Sentinel-2 data, and the use of the PAN band can decrease the errors introduced by LCLU changes. By fusion of Landsat 8 and Sentinel-2 data, more frequent observations can be produced for continuous monitoring (this is particularly valuable for areas that can be covered easily by clouds, thereby, contaminating some Landsat or Sentinel-2 observations), and the observations are at a consistent fine spatial resolution of 10 m. The products have great potential for timely monitoring of rapid changes.

Superpixel-Based Multiple Local CNN for Panchromatic and Multispectral Image Classification

Recently, very high resolution (VHR) panchromatic and multispectral (MS) remote-sensing images can be acquired easily. However, it is still a challenging task to fuse and classify these VHR images. Generally, there are two ways for the fusion and classification of panchromatic and MS images. One way is to use a panchromatic image to sharpen an MS image, and then classify a pan-sharpened MS image. Another way is to extract features from panchromatic and MS images, respectively, and then combine these features for classification. In this paper, we propose a superpixel-based multiple local convolution neural network (SML-CNN) model for panchromatic and MS images classification. In order to reduce the amount of input data for the CNN, we extend simple linear iterative clustering algorithm for segmenting MS images and generating superpixels. Superpixels are taken as the basic analysis unit instead of pixels. To make full advantage of the spatial-spectral and environment information of superpixels, a superpixel-based multiple local regions joint representation method is proposed. Then, an SML-CNN model is established to extract an efficient joint feature representation. A softmax layer is used to classify these features learned by multiple local CNN into different categories. Finally, in order to eliminate the adverse effects on the classification results within and between superpixels, we propose a multi-information modification strategy that combines the detailed information and semantic information to improve the classification performance. Experiments on the classification of Vancouver and Xi’an panchromatic and MS image data sets have demonstrated the effectiveness of the proposed approach.

Identifying Suitable Areas for Urban Development in Rampur Municipality of Palpa District, Nepal

Identifying suitable areas for urban development is one of the critical issues of urban planning in hilly areas in Palpa district. The study illustrates the use of GIS suitability criteria and weighted overlay analysis technique for selection of suitable areas for urban development in Rampur municipality. The main objectives of this research is to identifying suitable areas for urban development of Rampur Municipality, Palpa. For this purpose panchromatic and multispectral satellite image data were used to generate for land use land cover map using digitalization and visual interpretation method in ArcGIS software. The criteria using five parameters i.e. geology, elevation, slope, aspect, and Land Use Land Cover (LULC) and the method of Suitability Criteria and Weighted Overlay Analysis were used for identifying the suitable areas for urban development. The eight categories are Agriculture, Commercial, Forest, Industrial, Public Use, Residential, Road and Water Bodies were used for LULC map. The result of this research shows highly suitable areas of Rampur municipality is 5.54 Sq. Km. and it covers 4.49% for suitable areas for urban development.

The timing and the scale of the proliferation of Sargassum polycystum in Funafuti Atoll, Tuvalu

The biomass of the algal bloom of Sargassum polycystum along the lagoon shores of Funafuti Atoll (Tuvalu) was estimated in November 2014 using a multispectral spaceborne Pleiades image and concurrent field data. Field work established (1) the geographical limit of the distribution of the algae in the lagoon, (2) the different types of S. polycystum beds that developed in the lagoon, and (3) the range of biomass per square meter for 12 stations. Extrapolation of field data using the green spectral band of the image over the distribution domain and manual correction to remove outliers suggested a standing biomass of 852 t of wet biomass (±95% confidence interval = 420–1747 t) over 0.74 km2 of reef. Six months later, the processing of a Pleiades image from April 2015 suggested a decrease of algal biomass by 17% (710 ± 333–1542 t). The examination of a multi-sensor series of very high-resolution multispectral images (acquired in 2002, 2003, 2004, 2010, 2011, 2012, 2014, 2015) suggested that algal proliferation started in 2010, with a strong unambiguous signal 1 year later in 2011. The time-series of images also suggested that overall, the biomass had remained fairly stable since 2012, but spatial variations occur, with algae disappearing in some areas, and developing in others. Perspectives for future monitoring are discussed.

Remote Sensing for Crop Water Management: From ET Modelling to Services for the End Users.

The experiences gathered during the past 30 years support the operational use of irrigation scheduling based on frequent multi-spectral image data. Currently, the operational use of dense time series of multispectral imagery at high spatial resolution makes monitoring of crop biophysical parameters feasible, capturing crop water use across the growing season, with suitable temporal and spatial resolutions. These achievements, and the availability of accurate forecasting of meteorological data, allow for precise predictions of crop water requirements with unprecedented spatial resolution. This information is greatly appreciated by the end users, i.e., professional farmers or decision-makers, and can be provided in an easy-to-use manner and in near-real-time by using the improvements achieved in web-GIS methodologies (Geographic Information Systems based on web technologies). This paper reviews the most operational and explored methods based on optical remote sensing for the assessment of crop water requirements, identifying strengths and weaknesses and proposing alternatives to advance towards full operational application of this methodology. In addition, we provide a general overview of the tools, which facilitates co-creation and collaboration with stakeholders, paying special attention to these approaches based on web-GIS tools.

Extraction of Geological Structural Lineaments in Northern Kuwait Using High Resolution Landsat 8 ETM+ Satellite Images and Edge Enhancement Techniques

Earth consists of hard rock layers where water is restricted to secondary permeability, and thus to fractures and the weather zones. Satellite images, aerial photographs and digital elevation models will give lineament information. Recent advances in digital image processing allow such lineament extraction to be accomplished in semi-automatic to fully automatic approaches. Precise delineation and mapping of geological structural lineaments is very much needed in Northern Kuwait in view of their close spatial association with subsurface tectonic fractures in oil fields at deep levels and groundwater fields (aquifers) at shallow depths. As such, high spatial resolution remote sensing multispectral image data (Landsat 8 ETM+ satellite) will be used delineate and map the lineaments. Lineaments are linear features which provide information about the underlying geological structure. Their analysis are widely considered in the location or sitting of suitable groundwater exploration sites since the water will tend to flow along the fracture zones. The major objective was to extract the lineaments in Northern Kuwait, with a view of using the analysis for groundwater exploration in the future to mitigate the requirement of groundwater in the area. The analysis was carried out using Band 5, 6 and 7 of Landsat 8 (OLI), which was processed using the edge enhancement filters available to extract the lineaments in the study area. These were then verified using manually digitized lineaments from a geological map partly covering the region and false color composites of the study area. The results showed that the semi-automatic extraction of lineaments can yield tangible results using optical imagery. However, the lineaments still require verification through comparison with known lineaments. Filters which are Sobel, Laplacian and Directional filtering of Band 5, 6 and 7 of Landsat 8 proved to be useful in visual identification and verification of lineaments.

Improved field-mapping and artifact correction in multispectral imaging.

To develop a method for improved B0 field-map estimation, deblurring, and image combination for multispectral imaging near metal. A goodness-of-fit field-map estimation technique is proposed that uses only the multispectral imaging (MSI) data to estimate the field map. Using the improved field map, a novel deblurring technique is proposed that also employs a new image combination scheme to reduce the effects of noise and other residual MSI artifacts. The proposed field-map estimation and deblurring techniques are compared to the current methods in phantoms and/or in vivo from subjects with knee, hip, and spinal metallic implants. Phantom experiments validate that the goodness-of-fit field-map estimation is less sensitive to noise and bias than the conventional center-of-mass technique, which reduces distortion in the deblurring methods. The new deblurring approach also is substantially less sensitive to noise and distortion than the current deblurring method, as demonstrated in phantoms and in vivo, and is able to find a good tradeoff between deblurring and distortion. The proposed methods not only enable field-mapping with reduced noise sensitivity but are able to create deblurred images with less distortion and better signal-to-noise ratio with no additional scan time, thereby enabling improved visualization of underlying anatomy near metallic implants. Magn Reson Med 78:2022-2034, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Comparison between Possibilistic c-Means (PCM) and Artificial Neural Network (ANN) Classification Algorithms in Land use/ Land cover Classification

There are several statistical classification algorithms available for land use/land cover classification. However, each has a certain bias or compromise. Some methods like the parallel piped approach in supervised classification, cannot classify continuous regions within a feature. On the other hand, while unsupervised classification method takes maximum advantage of spectral variability in an image, the maximally separable clusters in spectral space may not do much for our perception of important classes in a given study area. In this research, the output of an ANN algorithm was compared with the Possibilistic c-Means an improvement of the fuzzy c-Means on both moderate resolutions Landsat8 and a high resolution Formosat 2 images. The Formosat 2 image comes with an 8m spectral resolution on the multispectral data. This multispectral image data was resampled to 10m in order to maintain a uniform ratio of 1:3 against Landsat 8 image. Six classes were chosen for analysis including: Dense forest, eucalyptus, water, grassland, wheat and riverine sand. Using a standard false color composite (FCC), the six features reflected differently in the infrared region with wheat producing the brightest pixel values. Signature collection per class was therefore easily obtained for all classifications. The output of both ANN and FCM, were analyzed separately for accuracy and an error matrix generated to assess the quality and accuracy of the classification algorithms. When you compare the results of the two methods on a per-class-basis, ANN had a crisper output compared to PCM which yielded clusters with pixels especially on the moderate resolution Landsat 8 imagery.

Tissue classification for laparoscopic image understanding based on multispectral texture analysis.

Intraoperative tissue classification is one of the prerequisites for providing context-aware visualization in computer-assisted minimally invasive surgeries. As many anatomical structures are difficult to differentiate in conventional RGB medical images, we propose a classification method based on multispectral image patches. In a comprehensive ex vivo study through statistical analysis, we show that (1)multispectral imaging data are superior to RGB data for organ tissue classification when used in conjunction with widely applied feature descriptors and (2)combining the tissue texture with the reflectance spectrum improves the classification performance. The classifier reaches an accuracy of 98.4% on our dataset. Multispectral tissue analysis could thus evolve as a key enabling technique in computer-assisted laparoscopy.

Nonlinear Fusion of Multispectral Citrus Fruit Image Data with Information Contents.

The main issue of vison-based automatic harvesting manipulators is the difficulty in the correct fruit identification in the images under natural lighting conditions. Mostly, the solution has been based on a linear combination of color components in the multispectral images. However, the results have not reached a satisfactory level. To overcome this issue, this paper proposes a robust nonlinear fusion method to augment the original color image with the synchronized near infrared image. The two images are fused with Daubechies wavelet transform (DWT) in a multiscale decomposition approach. With DWT, the background noises are reduced and the necessary image features are enhanced by fusing the color contrast of the color components and the homogeneity of the near infrared (NIR) component. The resulting fused color image is classified with a C-means algorithm for reconstruction. The performance of the proposed approach is evaluated with the statistical F measure in comparison to some existing methods using linear combinations of color components. The results show that the fusion of information in different spectral components has the advantage of enhancing the image quality, therefore improving the classification accuracy in citrus fruit identification in natural lighting conditions.

Urban landcover classification from multispectral image data using optimized AdaBoosted random forests

ABSTRACTWith an ever growing need to classify multispectral images, the accuracy of the classification becomes a matter of concern, especially when mapping heterogeneous environments such as urban areas. Not all of the features of a high-resolution multispectral image would equally contribute to the formulation of an ideal hypothesis, some of which might be redundant and might hinder the classification itself. Random forests (RF), an ensemble of decision trees, would assist in the removal of such extraneous features. While RF has proved to be one of the most efficient classifiers for spectral imagery, it holds untapped potential which could be honed to yield much more accurate prediction rates. This article discusses the application of AdaBoosted random forest (ABRF) to classify landcover segments from multispectral satellite or aerial imagery. A stage-wise optimization procedure is applied to achieve the best possible performance. It involves genetic feature selection and strategic empirical calibration to determine the ideal set of parameters. The application of boosting to RF resulted in the increase in the overall accuracy from 84.42% to 88.8% with an increase in kappa coefficient from 0.804 to 0.859.

Analysis of LAPAN-IPB image lossless compression using differential pulse code modulation and huffman coding

LAPAN-A3/IPB satellite is the latest Indonesian experimental microsatellite with remote sensing and earth surveillance missions. The satellite has three optical payloads, which are multispectral push-broom imager, digital matrix camera and video camera. To increase data transmission efficiency, the multispectral imager data can be compressed using either lossy or lossless compression method. This paper aims to analyze Differential Pulse Code Modulation (DPCM) method and Huffman coding that are used in LAPAN-IPB satellite image lossless compression. Based on several simulation and analysis that have been done, current LAPAN-IPB lossless compression algorithm has moderate performance. There are several aspects that can be improved from current configuration, which are the type of DPCM code used, the type of Huffman entropy-coding scheme, and the use of sub-image compression method. The key result of this research shows that at least two neighboring pixels should be used for DPCM calculation to increase compression performance. Meanwhile, varying Huffman tables with sub-image approach could also increase the performance if on-board computer can support for more complicated algorithm. These results can be used as references in designing Payload Data Handling System (PDHS) for an upcoming LAPAN-A4 satellite.

Macular Pigment Quantification with Multispectral Retinal Image Analysis

Objective: Variations in macular pigment (MP) have been linked with changes in the risk of visual loss secondary to macular pathology. MP density can be modified by diet; however there doesn’t appear to be a direct link between dietary intake of MP components and MP density in the retina and clinicians therefore need a reliable and objective method for MP measurement to establish if any intervention is yielding the required results. The objective of this study was to investigate whether multispectral retinal image analysis (MRIA), a new technique for mapping retinal pigments, is useful for measuring levels and distribution of MP to find differences between individuals with no clinical evidence of macular pathology and those diagnosed with Age-related Macular Degeneration (AMD). Methods: The study involved 90 volunteers from three subject groups: aged under 50 without AMD, aged 50 and over without AMD and aged 50 and over with AMD. The experiments yielded 607 usable data sets that were used for analysis. Multispectral image data was acquired at six selected wavelengths using a modified fundus camera. MRIA maps of MP were computed from 3 × 3 mm regions of interest (approximately 10 degrees of visual angle) centred at the fovea. Indices characterising MP distribution were computed both for individuals and for the three subject groups. For comparison Macular Pigment Optical Density (MPOD) measurements were acquired from the Macular Pigment Screener 9000 (MPS) based on the heterochromatic flicker photometry (HFP). Correlations for MP quantities measured with the two methods were computed between MP quantity and age, MP quantity and AMD diagnosis, and between the two methods. Results: MP maps obtained from MRIA were consistent with known histology and in agreement with expectations based on previous studies. Pooled results from the three groups suggest that the overall levels of MP across both the fovea and the parafovea are on average higher in healthy under-50 individuals than that over-50 with or without AMD. MP distribution might be more irregular in the over-50 groups than in the younger group. The correlation between age and MP levels was weak as measured individually by both techniques. The MRIA indices were not correlated with HFP-MPOD measurements for individuals, but high correlation was found between mean HFP-MPOD and mean MRIA peak value for pooled results. Conclusion: MRIA has potential to offer an objective, fast and reliable method of measuring MP throughout the posterior pole.

On the theory of function-valued mappings and its application to the processing of hyperspectral images

The concept of a mapping, which takes its values in an infinite-dimensional functional space, has been studied by the mathematical community since the third decade of the last century. This effort has produced a range of important contributions, many of which have already made their way to applied sciences, where they have been successfully used to facilitate numerous practical applications across various fields. Surprisingly enough, one particular field, which could have benefited from the above contributions to a much greater extent, still relies on finite-dimensional models and approximations, thus missing out on numerous advantages offered through adopting a more general framework. This field is image processing, which is in the focus of this study. In particular, in this paper, we introduce an alternative approach to the analysis of multidimensional imagery data based on the mathematical theory of function-valued mappings. In addition to extending various tools of standard functional calculus, we generalize the notions of Fourier and fractal transforms, followed by their application to processing of multispectral imaging data. Some applications and future extensions of this work are discussed as well.

Timing of optical maturation of recently exposed material on Ceres

AbstractOn Ceres, multispectral imaging data from the Dawn spacecraft show a distinct bluish characteristic for recently exposed material from the subsurface in, for example, crater ejecta. Ejecta blankets of presumably old craters show a more reddish spectrum. We selected areas in which fresh material from the Cerean subsurface was exposed at a specific time in the past, and no later geologic process is expected to have changed its surface composition or its cratering record. For each area, we determined two color ratios and the crater retention age. The measured color ratios show an exponential diminishment of the bluish characteristic over time. Although the cause of the color change remains uncertain, the time‐dependent change in spectral properties is evident, which could help identify the process.