LANDSAT-D investigations in snow hydrology

Work undertaken during the contract and its results are described. Many of the results from this investigation are available in journal or conference proceedings literature - published, accepted for publication, or submitted for publication. For these the reference and the abstract are given. Those results that have not yet been submitted separately for publication are described in detail. Accomplishments during the contract period are summarized as follows: (1) analysis of the snow reflectance characteristics of the LANDSAT Thematic Mapper, including spectral suitability, dynamic range, and spectral resolution; (2) development of a variety of atmospheric models for use with LANDSAT Thematic Mapper data. These include a simple but fast two-stream approximation for inhomogeneous atmospheres over irregular surfaces, and a doubling model for calculation of the angular distribution of spectral radiance at any level in an plane-parallel atmosphere; (3) incorporation of digital elevation data into the atmospheric models and into the analysis of the satellite data; and (4) textural analysis of the spatial distribution of snow cover

Literature review of the remote sensing of natural resources

Abstracts of 596 documents related to remote sensors or the remote sensing of natural resources by satellite, aircraft, or ground-based stations are presented. Topics covered include general theory, geology and hydrology, agriculture and forestry, marine sciences, urban land use, and instrumentation. Recent documents not yet cited in any of the seven information sources used for the compilation are summarized. An author/key word index is provided

Multispectral texture synthesis

Synthesizing texture involves the ordering of pixels in a 2D arrangement so as to display certain known spatial correlations, generally as described by a sample texture. In an abstract sense, these pixels could be gray-scale values, RGB color values, or entire spectral curves. The focus of this work is to develop a practical synthesis framework that maintains this abstract view while synthesizing texture with high spectral dimension, effectively achieving spectral invariance. The principle idea is to use a single monochrome texture synthesis step to capture the spatial information in a multispectral texture. The first step is to use a global color space transform to condense the spatial information in a sample texture into a principle luminance channel. Then, a monochrome texture synthesis step generates the corresponding principle band in the synthetic texture. This spatial information is then used to condition the generation of spectral information. A number of variants of this general approach are introduced. The first uses a multiresolution transform to decompose the spatial information in the principle band into an equivalent scale/space representation. This information is encapsulated into a set of low order statistical constraints that are used to iteratively coerce white noise into the desired texture. The residual spectral information is then generated using a non-parametric Markov Ran dom field model (MRF). The remaining variants use a non-parametric MRF to generate the spatial and spectral components simultaneously. In this ap proach, multispectral texture is grown from a seed region by sampling from the set of nearest neighbors in the sample texture as identified by a template matching procedure in the principle band. The effectiveness of both algorithms is demonstrated on a number of texture examples ranging from greyscale to RGB textures, as well as 16, 22, 32 and 63 band spectral images. In addition to the standard visual test that predominates the literature, effort is made to quantify the accuracy of the synthesis using informative and effective metrics. These include first and second order statistical comparisons as well as statistical divergence tests

Pre-processing, classification and semantic querying of large-scale Earth observation spaceborne/airborne/terrestrial image databases: Process and product innovations.

By definition of Wikipedia, “big data is the term adopted for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The big data challenges typically include capture, curation, storage, search, sharing, transfer, analysis and visualization”. Proposed by the intergovernmental Group on Earth Observations (GEO), the visionary goal of the Global Earth Observation System of Systems (GEOSS) implementation plan for years 2005-2015 is systematic transformation of multisource Earth Observation (EO) “big data” into timely, comprehensive and operational EO value-adding products and services, submitted to the GEO Quality Assurance Framework for Earth Observation (QA4EO) calibration/validation (Cal/Val) requirements. To date the GEOSS mission cannot be considered fulfilled by the remote sensing (RS) community. This is tantamount to saying that past and existing EO image understanding systems (EO-IUSs) have been outpaced by the rate of collection of EO sensory big data, whose quality and quantity are ever-increasing. This true-fact is supported by several observations. For example, no European Space Agency (ESA) EO Level 2 product has ever been systematically generated at the ground segment. By definition, an ESA EO Level 2 product comprises a single-date multi-spectral (MS) image radiometrically calibrated into surface reflectance (SURF) values corrected for geometric, atmospheric, adjacency and topographic effects, stacked with its data-derived scene classification map (SCM), whose thematic legend is general-purpose, user- and application-independent and includes quality layers, such as cloud and cloud-shadow. Since no GEOSS exists to date, present EO content-based image retrieval (CBIR) systems lack EO image understanding capabilities. Hence, no semantic CBIR (SCBIR) system exists to date either, where semantic querying is synonym of semantics-enabled knowledge/information discovery in multi-source big image databases. In set theory, if set A is a strict superset of (or strictly includes) set B, then A B. This doctoral project moved from the working hypothesis that SCBIR computer vision (CV), where vision is synonym of scene-from-image reconstruction and understanding EO image understanding (EO-IU) in operating mode, synonym of GEOSS ESA EO Level 2 product human vision. Meaning that necessary not sufficient pre-condition for SCBIR is CV in operating mode, this working hypothesis has two corollaries. First, human visual perception, encompassing well-known visual illusions such as Mach bands illusion, acts as lower bound of CV within the multi-disciplinary domain of cognitive science, i.e., CV is conditioned to include a computational model of human vision. Second, a necessary not sufficient pre-condition for a yet-unfulfilled GEOSS development is systematic generation at the ground segment of ESA EO Level 2 product. Starting from this working hypothesis the overarching goal of this doctoral project was to contribute in research and technical development (R&D) toward filling an analytic and pragmatic information gap from EO big sensory data to EO value-adding information products and services. This R&D objective was conceived to be twofold. First, to develop an original EO-IUS in operating mode, synonym of GEOSS, capable of systematic ESA EO Level 2 product generation from multi-source EO imagery. EO imaging sources vary in terms of: (i) platform, either spaceborne, airborne or terrestrial, (ii) imaging sensor, either: (a) optical, encompassing radiometrically calibrated or uncalibrated images, panchromatic or color images, either true- or false color red-green-blue (RGB), multi-spectral (MS), super-spectral (SS) or hyper-spectral (HS) images, featuring spatial resolution from low (> 1km) to very high (< 1m), or (b) synthetic aperture radar (SAR), specifically, bi-temporal RGB SAR imagery. The second R&D objective was to design and develop a prototypical implementation of an integrated closed-loop EO-IU for semantic querying (EO-IU4SQ) system as a GEOSS proof-of-concept in support of SCBIR. The proposed closed-loop EO-IU4SQ system prototype consists of two subsystems for incremental learning. A primary (dominant, necessary not sufficient) hybrid (combined deductive/top-down/physical model-based and inductive/bottom-up/statistical model-based) feedback EO-IU subsystem in operating mode requires no human-machine interaction to automatically transform in linear time a single-date MS image into an ESA EO Level 2 product as initial condition. A secondary (dependent) hybrid feedback EO Semantic Querying (EO-SQ) subsystem is provided with a graphic user interface (GUI) to streamline human-machine interaction in support of spatiotemporal EO big data analytics and SCBIR operations. EO information products generated as output by the closed-loop EO-IU4SQ system monotonically increase their value-added with closed-loop iterations

Optical Methods in Sensing and Imaging for Medical and Biological Applications

The recent advances in optical sources and detectors have opened up new opportunities for sensing and imaging techniques which can be successfully used in biomedical and healthcare applications. This book, entitled ‘Optical Methods in Sensing and Imaging for Medical and Biological Applications’, focuses on various aspects of the research and development related to these areas. The book will be a valuable source of information presenting the recent advances in optical methods and novel techniques, as well as their applications in the fields of biomedicine and healthcare, to anyone interested in this subject

Connected Attribute Filtering Based on Contour Smoothness

A new attribute measuring the contour smoothness of 2-D objects is presented in the context of morphological attribute filtering. The attribute is based on the ratio of the circularity and non-compactness, and has a maximum of 1 for a perfect circle. It decreases as the object boundary becomes irregular. Computation on hierarchical image representation structures relies on five auxiliary data members and is rapid. Contour smoothness is a suitable descriptor for detecting and discriminating man-made structures from other image features. An example is demonstrated on a very-high-resolution satellite image using connected pattern spectra and the switchboard platform

Depth-resolved quantitative phase imaging using lensfree interferometric microscopy

Schopnost zkoumat slabě rozptylující vzorky je klíčová například pro odvětví zabývající se studiem buněk nebo optickou analýzou povrchů. Tyto tzv. fázové objekty však neprodukují dostatečný signál na to, aby mohly být zobrazovány pomocí klasické optické mikroskopie. Řešením tohoto problému je využití interferometrie. Bezobjektivový interferometrický mikroskop (LIM) je zařízením, které využívá dvojlomných krystalů a částečně koherentních, kolimovaných zdrojů světla ke zkoumání plošného rozložení indexu lomu ve vzorcích s vysokým axiálním rozlišením. Schopnost získat informace o třírozměrném rozložení fázových objektů v objemu vzorku by umožnila využití tohoto mikroskopu v nových vědeckých i průmyslových odvětvích, například biomedicínském zobrazování, datových úložištích na bázi skla nebo monitorování defektů v optických elementech. Tato diplomová práce se zabývá rozšířením funkčnosti bezobjektivového interferometrického mikroskopu do oblasti tomografického zobrazování. Toho je dosaženo realizací hloubkového rozlišování pro fázové objekty ve zkoumaných vzorcích. Pro umožnění měření byl sestaven prototyp mikroskopu a ověřen vliv různých parametrů optického uspořádání na kvalitu pořízeného obrazu. Současně byla navržena nová metoda k získání nakloněného, kolimovaného osvětlení. Kombinací několika úhlů osvitu a následného algoritmického zpracování získaných dat byly pořízeny kvantitativní fázové snímky se zorným polem 35 mm2, plošným rozlišením 10 um a axiálním rozlišením menším než 1 nm. Následně bylo navrženo několik metod umožňujících hloubkové rozlišování zobrazovaných objektů. Tyto postupy, využívající nakloněného osvitu a numerické propagace optického pole, byly implementovány a ověřeny měřením na vícevrstvých vzorcích. Nejlepší výsledky byly získány pomocí metody backpropagated pixel-by-pixel verification (beta-PbP). Tato nově navržená metoda byla úspěšně využita pro třírozměrnou rekonstrukci rozložení fázových objektů ve vzorcích objemu V = 0.5 cm3 s axiální přesností menší než 1 mm. Společně s ostatními navrženými metodami se jedná o první demonstraci využití LIM jako tomografické zobrazovací techniky.Examining of weakly scattering transparent structures is highly desirable especially in areas such as cell imaging and quality control of transparent surfaces. However, such structures can not be efficiently imaged in conventional light microscopes due to low scattering signal. To measure such structures, techniques such as interferometry are more suitable. The lensfree interferometric microscope (LIM) is a compact device that utilizes birefringent crystals and partially coherent collimated light beams to acquire information about refractive index distribution of planar samples with sub-nanometer precision. Extending the phase imaging ability of such device from two to three dimensions would allow multitude of new applications across various research and industrial fields including biomedical imaging, physical data storage systems, defect mapping in glasses or holographic security element validation. This thesis focuses on expanding the measurement capabilities of the LIM device into the field of tomographical imaging by enabling depth resolving of transparent (phase) objects. First, an overview of the LIM, its design and related computational methods is provided. The microscope prototype was built, all the optical setup parameters were assessed and the influence of different components was evaluated, laying the foundation for further development of commercial prototypes. To emphasize the potential for a point-of-care portable device, all the hardware controls and data processing were implemented on a single-board computer. Simultaneously, a novel solution to obtain different illumination angles was proposed using multicore optical fiber bundle. Utilizing a combination of the multiple illumination angles and computational post-processing, phase maps were reconstructed across a field-of-view of 35 mm2 with spatial resolution of 10 um and axial resolution in the sub-nanometer region. To enable depth resolving, multiple techniques were proposed for the LIM, taking advantage of both digital holographic refocusing and the angled illumination. These proposed methods were verified by measuring real, custom-made multilayered transparent samples. The best results were obtained by a self-developed algorithm named backpropagated pixel-by-pixel verification (beta-PbP). This new method enables layer-by-layer phase map reconstruction in the sample volume V = 0.5 cm3 with axial accuracy for preliminary results being below 1 mm. Together with the other introduced techniques, this demonstrates the first proof-of-concept of using the LIM for tomographical imaging