A Novel Technique Based on the Combination of Labeled Co-Occurrence Matrix and Variogram for the Detection of Built-up Areas in High-Resolution SAR Images

Interests in synthetic aperture radar (SAR) data analysis is driven by the constantly increased spatial resolutions of the acquired images, where the geometries of scene objects can be better defined than in lower resolution data. This paper addresses the problem of the built-up areas extraction in high-resolution (HR) SAR images, which can provide a wealth of information to characterize urban environments. Strong backscattering behavior is one of the distinct characteristics of built-up areas in a SAR image. However, in practical applications, only a small portion of pixels characterizing the built-up areas appears bright. Thus, specific texture measures should be considered for identifying these areas. This paper presents a novel texture measure by combining the proposed labeled co-occurrence matrix technique with the specific spatial variability structure of the considered land-cover type in the fuzzy set theory. The spatial variability is analyzed by means of variogram, which reflects the spatial correlation or non-similarity associated with a particular terrain surface. The derived parameters from the variograms are used to establish fuzzy functions to characterize the built-up class and non built-up class, separately. The proposed technique was tested on TerraSAR-X images acquired of Nanjing (China) and Barcelona (Spain), and on a COSMO-SkyMed image acquired of Hangzhou (China). The obtained classification accuracies point out the effectiveness of the proposed technique in identifying and detecting built-up areas

Benthic mapping of the Bluefields Bay fish sanctuary, Jamaica

Small island states, such as those in the Caribbean, are dependent on the nearshore marine ecosystem complex and its resources; the goods and services provided by seagrass and coral reef for example, are particularly indispensable to the tourism and fishing industries. In recognition of their valuable contributions and in an effort to promote sustainable use of marine resources, some nearshore areas have been designated as fish sanctuaries, as well as marine parks and protected areas. In order to effectively manage these coastal zones, a spatial basis is vital to understanding the ecological dynamics and ultimately inform management practices. However, the current extent of habitats within designated sanctuaries across Jamaica are currently unknown and owing to this, the Government of Jamaica is desirous of mapping the benthic features in these areas. Given the several habitat mapping methodologies that exist, it was deemed necessary to test the practicality of applying two remote sensing methods - optical and acoustic - at a pilot site in western Jamaica, the Bluefields Bay fish sanctuary. The optical remote sensing method involved a pixel-based supervised classification of two available multispectral images (WorldView-2 and GeoEye-1), whilst the acoustic method comprised a sonar survey using a BioSonics DT-X Portable Echosounder and subsequent indicator kriging interpolation in order to create continuous benthic surfaces. Image classification resulted in the mapping of three benthic classes, namely submerged vegetation, bare substrate and coral reef, with an overall map accuracy of 89.9% for WorldView-2 and 86.8% for GeoEye-1 imagery. These accuracies surpassed those of the acoustic classification method, which attained 76.6% accuracy for vegetation presence, and 53.5% for bottom substrate (silt, sand and coral reef/ hard bottom). Both approaches confirmed that the Bluefields Bay is dominated by submerged aquatic vegetation, with contrastingly smaller areas of bare sediment and coral reef patches. Additionally, the sonar revealed that silty substrate exists along the shoreline, whilst sand is found further offshore. Ultimately, the methods employed in this study were compared and although it was found that satellite image classification was perhaps the most cost-effective and well-suited for Jamaica given current available equipment and expertise, it is acknowledged that acoustic technology offers greater thematic detail required by a number of stakeholders and is capable of operating in turbid waters and cloud covered environments ill-suited for image classification. On the contrary, a major consideration for the acoustic classification process is the interpolation of processed data; this step gives rise to a number of potential limitations, such as those associated with the choice of interpolation algorithm, available software and expertise. The choice in mapping approach, as well as the survey design and processing steps is not an easy task; however the results of this study highlight the various benefits and shortcomings of implementing optical and acoustic classification approaches in Jamaica.Persons automatically associate tropical waters with spectacular views of coral reefs and colourful fish; however many are perhaps not aware that these coral reefs, as well as other living organisms inhabiting the seabed are in fact extremely valuable to our existence. Healthy coral reefs and seagrass assist in maintaining the sand on our beaches and fish populations and are thereby crucial to the tourism and fishing industries in the Caribbean. For this reason, a number of areas are protected by law and have been designated fish sanctuaries or marine protected areas. In order to understand the functioning of theses areas and effectively inform management strategy, the configuration of what exists on the seafloor is crucial. In the same vein that a motorist needs a road map to navigate unknown areas, coastal stakeholders require maps of the seafloor in order to understand what is happening beneath the water’s surface. The location of seafloor habitats within fish sanctuaries in Jamaica are currently unknown and the Government is interested in mapping them. However a myriad of methods exist that could be employed to achieve this goal. Remote sensing is a broad grouping of methods that involve collecting information about an object without being in direct physical contact with it. Many researchers have successfully mapped marine areas using these techniques and it was believed crucial to test the practicality of two such methods, specifically optical and acoustic remote sensing. The main question to be answered from this study was therefore: Which mapping approach is better for benthic habitat mapping in Jamaica and possibly the wider Caribbean? Optical remote sensing relates to the interaction of energy with the Earth’s surface. A digital photograph is taken from a satellite and subsequently interpreted. Acoustic/ sonar technology involves the recording of waveforms reflected from the seabed. Both methods were employed at a pilot site, the Bluefields Bay fish sanctuary, situated in western Jamaica. The optical remote sensing method involved the classification of two satellite images (named WorldView-2 and GeoEye-1) and this process was informed using known positions of seafloor features, this being known as supervised image classification. With regard to the acoustic method, a field survey utilising sonar equipment (BioSonics DT-X Portable Echosounder) was undertaken in order to collect the necessary sonar data. The processed field data was modelled in order to convert lines of field point data to one continuous map of the sanctuary, a process known as interpolation. The accuracy of each method was then tested using field knowledge of what exists in the sanctuary. The map resulting from the image classification revealed three seafloor types, namely submerged vegetation, coral reef and bare seafloor. The overall map accuracy was 89.9% for the WorldView-2 image and 86.8% for GeoEye-1 imagery. These accuracies surpassed those attained from the acoustic classification method (76.6% for vegetation presence and 53.5% for bottom type - silt, sand and coral reef/ hard bottom). Similar to previous studies undertaken, it was shown that the seabed of Bluefields Bay is primarily inhabited by submerged aquatic vegetation (including seagrass and algae), with contrastingly smaller areas of bare sediment and coral reef. Ultimately, the methods employed in this study were compared and the pros and cons of each were weighed in order to deem one method more suitable in Jamaica. Often, the presence of cloud and suspended matter in the water block the view of the seafloor making image classification difficult. On the contrary, acoustic surveys are capable of operating throughout cloudy conditions and attaining more detailed information of the ocean floor, otherwise not possible with optical remote sensing. A major step in the acoustic classification process however, was the interpolation of processed data, which may introduce additional limitations if careful consideration is not given to the intricacies of the process. Lastly, the acoustic survey certainly required greater financial resources than satellite image classification. In answer to the main question of this study, the most cost effective and feasible mapping method for Jamaica is satellite image classification (based on the results attained). It must be stressed however that the effective implementation of any method will depend on a number of factors, such as available software, equipment, expertise and user needs, that must be weighed in order to select the most feasible mapping method for a particular site

Remote Sensing And Geomorphometry For Studying Relief Production In High Mountains

Mountain topography is the result of highly scale-dependent interactions involving climatic, tectonic, and surface processes. No complete understanding of the geodynamics of mountain building and topographic evolution yet exists, although numerous conceptual and physical models indicate that surficial erosion plays a significant role. Mapping and assessing landforms and erosion in mountain environments is essential in order to understand landscape denudation and complex feedback mechanisms. This requires the development and evaluation of new approaches in remote sensing and geomorphometry. The research herein evaluates the problem of topographic normalization of satellite imagery and demonstrates the use of terrain analysis using a digital elevation model (DEM) to evaluate the relief structure of the landscape in the western Himalaya. We specifically evaluated the Cosine-correction and Minnaert-correction methods to reduce spectral variation in imagery caused by the topography. Semivariogram analyses of the topography were used to examine the relationships between relief and surface processes. Remote-sensing results indicate that the Minnaert-correction method can be used to reduce the “topographic effect” in satellite imagery for mapping, although extreme radiance values are the result of not accounting for the diffuse-skylight and adjacent-terrain irradiance. Geomorphometry results indicate that river incision and glaciation can generate extreme relief, although the greatest mesoscale relief is produced by glaciation at high altitudes. At intermediate altitudes, warm-based glaciation was found to decrease relief. Our results indicate that glaciation can have a differential influence on the relief structure of the landscape. Collectively, our results indicate that scale-dependent analysis of the topography is required to address radiation transfer issues and the polygenetic nature of landscape denudation and relief production

Modeling Spatially-Referenced MALDI Imaging Data Using a Process Convolution Approach

Matrix-assisted laser desorption/ionization Fourier-transform ion-cyclotron resonance (MALDI FT-ICR) imaging mass spectrometry (IMS) technology allows researchers to measure the abundance of ionized fragments over a two-dimensional space. Despite advances in IMS technology, methods used to analyze such data have lagged. In particular, the variability in IMS data can be attributed to both spatial and random sources. Additionally, the frequency of masses with high proportions of zero abundance measures is often quite large. To address these issues, we automate a procedure to account for spatial variability across multiple regions of interest. Using that procedure, we then develop and propose log-linear regression models facilitating group-level comparisons of ionized fragment abundance, which further account for both the data\u27s spatial structure and excess zeros. Our regression models, while accounting for the spatial structure in the same way, differ in their assumptions about the nature of the zeros, in particular whether they are accurately measured zeros or left-censored observations. We evaluate our models using simulated data and compare performance to approaches that account for the spatial information with differing complexity. We demonstrate that our methods maintain lower type I error rates and higher coverage compared with other approaches. These trends become more pronounced with increasing proportions of zeros, whether those zeros be true zero abundance measures or censored observations. We apply our models to a study examining glycosylation patterns in metastatic breast cancer. We identify N-glycans with differential abundance between primary and secondary tumor tissues, as well tissues stained negative and positive for tumor-associated macrophages. Upon classifying N-glycans into functional groups, we identify patterns that suggest underlying changes in enzymatic activity. Lastly, we develop the R package imagingPC that utilizes our methods to make them accessible to investigators. The R package distills our methods into a small set of functions that require limited knowledge of R. In addition to the base functions that use our methods, we incorporate functions that make our approach transparent and allow users to assess model assumptions

Local directional denoising

Published versio

Geostatistical Analysis of Point Soil Water Retention Parameters for Flint Sand

Geostatistics were employed to characterize sub-core scale heterogeneity and identify spatial structure in previously published water retention data (Kang et al., 2014) obtained using neutron radiography for Flint sand. The water retention data were parameterized using the Brooks and Corey (BC) model. The BC parameters investigated were: saturated water content (Ѳs), residual water content (Ѳr), air entry value (ψa), and pore size distribution index (λ). Spatial dependency in the BC parameters was identified using semivariograms. Of the four BC parameters analyzed, two were found to be spatially correlated, Ѳs and ψa. The spherical model fit to the cross variogram was used to perform co-kriging and map out the spatial dependency of these parameters. Low and high values apparent at the top and bottom of the kriged map for ψa implicated packing and compressive stress as the major causes of sub-core scale heterogeneity for this parameter. A concentrated area of high values in the center of the kriged map for Ѳs suggests that neutron scattering and the normalization procedure employed during image analysis to eliminate the effect of variable neutron path lengths was not completely successful. To alleviate these effects a trend correction process was developed by generating a second dataset using cross-validation, calculating the difference between the observed and leave-one-out cross validation data set, and adding the average of the observed data to the newly created residual variable. This trend correction process was validated using an independent data set collected by Cropper (2014). Mann-Whitney and Kolmogorov-Smirnov two sample tests were employed to determine if the Cropper (2014) parameters were significantly different from the trend corrected parameters in terms of their median values and frequency distributions, respectively. The results from both tests found significant differences between the two data sets indicating the trend correction procedure was unsuccessful, likely due to the unconsolidated sample and cylindrical geometry employed. Since spatial structure can have profound effects on flow and transport predictions, future work using neutron radiography to measure point BC parameters should focus on consolidated samples and rectangular sample geometry. Further exploration of the novel trend correction procedure is warranted

An investigation into reach scale estimates of sub-pixel fluvial grain size from hyperspatial imagery.

Grain size data for gravel bed rivers is important in a wide variety of contexts; providing crucial information to guide the development of flood defences, and maintaining navi-gability, biodiversity and ecological integrity within large gravel bed rivers. Advances in remote sensing technologies have seen an increase in the acquisition of hyperspatial imagery (imagery with a spatial resolution of < 10 cm), and advances in computational power have complemented this data acquisition allowing for the application of complex image processing techniques. An improved methodology is presented for extracting reach scale grain size information. Of particular note is the ability to generate estimates of sub-pixel surface sand content, as well as sub-pixel grain size distributions. The methodology was applied to Queens Bar, NBar, Calamity Bar and Harrison Bar within the gravel reach of the Fraser River (British Columbia, Canada). Hyperspatial imagery was acquired at 3 cm resolution, along with independent surface grain size information. Surface sand estimates were calculated through a first order standard deviation textural layer; calibrations revealed an inequality based relationship be-tween texture and sand content, allowing for the production of binary maps of surface sand content with an approximate accuracy of 70%. Calibrations were calculated for 7 grain size percentiles for the gravel fraction of the grain size distribution ( > 2 mm); D5, D16, D35, D50, D65, D84 and D95 were achieved, following a wide ranging parameter investigation. A combination of first order standard deviation along with several second order Grey Level Co-occurrence Matrix textural parameters (entropy, contrast and cor-relation) calibrated to grain size using multiple linear regression. The best performing calibrations were found for smaller and intermediate percentiles; cross validated mean square error (%) at 0.61, 3.55, 9.58 and 16.25 for D5, D16, D35, and D50 respectively. Calibrations began to break down for the largest percentiles; cross validated mean square error (%) at 26.43 and 44.99 for D84 and D95. The breakdown of calibrations for larger percentiles is attributed to the ‘pixel averaging eect’; for smaller percentiles a larger population of grains were averaged into one pixel, thus variance across multiple pixels is low, whereas for the larger percentiles the grain size approaches the spatial resolution of the pixels, therefore a smaller population of grains makes up one pixel and introduces in-creased variance across multiple pixels. Overall, this new methodology presents a means for extracting sub-pixel grain size information from hyperspatial imagery, with higher ac-curacies for the smaller percentiles than previously published. This allows for the rapid acquisition of a large amount of grain size information without the need for time intensive field techniques

Adaptif Range-Constrained Otsu Untuk Pemilihan Threshold Secara Otomatis Pada Histogram Citra Dengan Variansi Kelas Yang Tidak Seimbang

Image Thresholding merupakan proses segmentasi untuk memisahkan foreground dan background pada citra dengan cara membagi histogram citra menjadi dua kelas. Beberapa metode thresholding seperti Otsu dan Range-constrained Otsu menggunakan nilai variansi dari histogram untuk mendapatkan titik threshold, namun ketika menangani citra yang memiliki nilai variansi kelas foreground dan background tidak seimbang titik threshold yang dihasilkan kurang tepat. Paper ini mengusulkan metode Adaptif Range-constrained Otsu untuk mengatasi permasalahan variansi kelas yang tidak seimbang dengan cara mencari kelas yang memiliki nilai variansi lebih besar, untuk mendapatkan titik threshold yang lebih tepat. Pengujian menggunakan 22 NDT image dengan evaluasi misclassification error rate dan metode perankingan menunjukkan metode ini menghasilkan rerata ME 0.1153. Sedangkan Otsu sebesar 0.1746. Nilai rerata ranking 3.55, selisih 0.05 dibanding Kittler III. Hasil ini menunjukkan metode yang diusulkan kompetitif, terutama untuk segmentasi citra yang memiliki variansi kelas tidak sama