Brian Brisco

Bio: Brian Brisco is an academic researcher from Canada Centre for Remote Sensing. The author has contributed to research in topics: Synthetic aperture radar & Wetland classification. The author has an hindex of 35, co-authored 130 publications receiving 4266 citations. Previous affiliations of Brian Brisco include United States Geological Survey & Government of Canada.

Textural Infornation in SAR Images

Abstract: A multiplicative model was used to relate the image variance for a given land-use category to the individual variances associated with image speckle and target texture. Speckle was treated as a random process governed by signal fading and was considered to be statistically independent of the textural variations associated with the spatial variations of the scattering properties of visually "uniform" distributed targets. Seasat SAR imagery of Oklahoma was used to evaluate the textural autocorrelation function of five land-use categories: water, forest, pasture, urban, and cultivated. It was found that the maximum classification accuracy achievable using first-order statistics was 72 percent and that this level of accuracy was obtainable only by significantly degrading the spatial resolution in order to increase the number of independent samples per pixel. In contrast, second-order statistics-specifically, image contrast and inverse moment-provided a classification accuracy of 88 percent, with only a modest degradation in spatial resolution. A second study using SIR-A imagery of five forested regions has shown that the use of textural information can improve the classification accuracy among the five forest types from 75 to 93 percent.

Google Earth Engine for geo-big data applications: A meta-analysis and systematic review

Abstract: Google Earth Engine (GEE) is a cloud-based geospatial processing platform for large-scale environmental monitoring and analysis. The free-to-use GEE platform provides access to (1) petabytes of publicly available remote sensing imagery and other ready-to-use products with an explorer web app; (2) high-speed parallel processing and machine learning algorithms using Google’s computational infrastructure; and (3) a library of Application Programming Interfaces (APIs) with development environments that support popular coding languages, such as JavaScript and Python. Together these core features enable users to discover, analyze and visualize geospatial big data in powerful ways without needing access to supercomputers or specialized coding expertise. The development of GEE has created much enthusiasm and engagement in the remote sensing and geospatial data science fields. Yet after a decade since GEE was launched, its impact on remote sensing and geospatial science has not been carefully explored. Thus, a systematic review of GEE that can provide readers with the “big picture” of the current status and general trends in GEE is needed. To this end, the decision was taken to perform a meta-analysis investigation of recent peer-reviewed GEE articles focusing on several features, including data, sensor type, study area, spatial resolution, application, strategy, and analytical methods. A total of 349 peer-reviewed articles published in 146 different journals between 2010 and October 2019 were reviewed. Publications and geographical distribution trends showed a broad spectrum of applications in environmental analyses at both regional and global scales. Remote sensing datasets were used in 90% of studies while 10% of the articles utilized ready-to-use products for analyses. Optical satellite imagery with medium spatial resolution, particularly Landsat data with an archive exceeding 40 years, has been used extensively. Linear regression and random forest were the most frequently used algorithms for satellite imagery processing. Among ready-to-use products, the normalized difference vegetation index (NDVI) was used in 27% of studies for vegetation, crop, land cover mapping and drought monitoring. The results of this study confirm that GEE has and continues to make substantive progress on global challenges involving process of geo-big data.

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Abstract: Remote sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platform facilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platform was launched in 2010 and has proved its high potential for different applications, it has not been fully investigated and utilized for RS applications until recent years. Therefore, this study aims to comprehensively explore different aspects of the GEE platform, including its datasets, functions, advantages/limitations, and various applications. For this purpose, 450 journal articles published in 150 journals between January 2010 and May 2020 were studied. It was observed that Landsat and Sentinel datasets were extensively utilized by GEE users. Moreover, supervised machine learning algorithms, such as Random Forest, were more widely applied to image classification tasks. GEE has also been employed in a broad range of applications, such as Land Cover/land Use classification, hydrology, urban planning, natural disaster, climate analyses, and image processing. It was generally observed that the number of GEE publications have significantly increased during the past few years, and it is expected that GEE will be utilized by more users from different fields to resolve their big data processing challenges.

Compact polarimetry overview and applications assessment

Abstract: A synthetic aperture radar (SAR) with hybrid-polarity (CL-pol) architecture transmits circular polarization and receives two orthogonal, mutually coherent linear polarizations, which is one manifestation of compact polarimetry. The resulting radar is relatively simple to implement and has unique self-calibration features and low susceptibility to noise. It also enables maintenance of a larger swath coverage than fully polarimetric SAR systems. A research team composed of various departments of the Government of Canada evaluated this compact polarimetry mode configuration for application to soil moisture estimation, crop identification, ship detection, and sea-ice classification. This paper presents an overview of compact polarimetry, the approach developed for evaluation, and preliminary results for applications important to the Government of Canada. The implications of the results are also discussed with respect to future SAR missions such as the Canadian RADARSAT Constellation Mission, the American DESD...

Multisensor image fusion in remote sensing: Concepts, methods and applications

Abstract: With the availability of multisensor, multitemporal, multiresolution and multifrequency image data from operational Earth observation satellites the fusion of digital image data has become a valuable tool in remote sensing image evaluation. Digital image fusion is a relatively new research field at the leading edge of available technology. It forms a rapidly developing area of research in remote sensing. This review paper describes and explains mainly pixel based image fusion of Earth observation satellite data as a contribution to multisensor integration oriented data processing.

Remote Sensing And Image Interpretation

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Spectral mixture modeling - A new analysis of rock and soil types at the Viking Lander 1 site. [on Mars]

Abstract: A Viking Lander 1 image was modeled as mixtures of reflectance spectra of palagonite dust, gray andesitelike rock, and a coarse rocklike soil. The rocks are covered to varying degrees by dust but otherwise appear unweathered. Rocklike soil occurs as lag deposits in deflation zones around stones and on top of a drift and as a layer in a trench dug by the lander. This soil probably is derived from the rocks by wind abrasion and/or spallation. Dust is the major component of the soil and covers most of the surface. The dust is unrelated spectrally to the rock but is equivalent to the global-scale dust observed telescopically. A new method was developed to model a multispectral image as mixtures of end-member spectra and to compare image spectra directly with laboratory reference spectra. The method for the first time uses shade and secondary illumination effects as spectral end-members; thus the effects of topography and illumination on all scales can be isolated or removed. The image was calibrated absolutely from the laboratory spectra, in close agreement with direct calibrations. The method has broad applications to interpreting multispectral images, including satellite images.

Adaptive speckle filters and scene heterogeneity

Abstract: The presence of speckle in radar images makes the radiometric and textural aspects less efficient for class discrimination. Many adaptive filters have been developed for speckle reduction, the most well known of which are analyzed. It is shown that they are based on a test related to the local coefficient of variation of the observed image, which describes the scene heterogeneity. Some practical criteria are introduced to modify the filters in order to make them more efficient. The filters are tested on a simulated synthetic aperture radar (SAR) image and an SAR-580 image. As was expected, the new filters perform better, i.e. they average the homogeneous areas better and preserve texture information, edges, linear features, and point target responses better at the same time. Moreover, they can be adapted to features other than the coefficient of variation to reduce the speckle while preserving the corresponding information. >

Radar scattering and equilibrium ranges in wind‐generated waves with application to scatterometry

Abstract: To provide theoretical basis for the connection between observed radar scattering and wind-generated waves, a model for the response of surface waves in the gravity-capillary equilibrium region of the spectrum is proposed on the basis of a local (in wavenumber) balance between wind input and dissipation. The wind input function was constructed on the basis of laboratory observations of short-wave growth, while the dissipation function was developed from ideas of viscous dissipation and wave breaking in response to local accelerations and modified by kinematic effects of phase and group velocity differences. The model was exercised at L, C, X, and Ka bands to demonstrate the differences in wind speed and water temperature sensitivity.