Application of Remote Sensing to the Chesapeake Bay Region. Volume 2: Proceedings

A conference was held on the application of remote sensing to the Chesapeake Bay region. Copies of the papers, resource contributions, panel discussions, and reports of the working groups are presented

Kahden varpukasvin spektrien kaksisuuntaiset heijastussuhdetekijämittaukset

Recent studies have shown the benefits of multiangular remote sensing techniques for characterizing vegetation reflection properties. The study of spectral anisotropy of understory vegetation enables methods for improved plant species identification, and provides valuable input data for radiation scattering models of forests. This thesis presents the applied methods and results of a research effort carried out over the growing season of 2017 for the temporal spectral characterization of two of the economically most important wild berry species in Finland: lingonberry (Vaccinium vitis-idaea) and blueberry (Vaccinium myrtillus). The spectral bidirectional reflectance factor (BRF) data on lingonberry and blueberry shrub samples were collected in a multidirectional measurement geometry using the Finnish Geodetic Institute Goniospectrometer (FIGIFIGO) in laboratory conditions. Leaf reflectance and transmittance spectra on both species were collected with SpectroClip-TR spectral probe. The anisotropic characteristics were analysed in the spectral range from 400 to 2200 nm for view angle dependence (-40° to +40°), illumination angle dependence (+40°, +55°), seasonal dynamics over the growing season (2017), and for berry and flower detection. Both lingonberry and blueberry shrubs have strong backward and notable forward scattering characteristics on the principal plane. In the interspecies comparison, lingonberry is brighter into all view direction in the visible and near infrared wavelengths but darker in the short-wave infrared. Increasing the illumination zenith angle by 15° improves the spectral discrimination of the two dwarf shrub species by inducing a 12% ratio of the spectral responses. Vegetation indices that are commonly used in remote sensing of forests (NDVI, NDVI705, MSI, PSRI) show low sensitivity to the changes in the view- and illumination angles. The presence of lingonberries and lingonberry flowers is indicated as a spectral peak around 679 nm in the spectral ratio of samples with berries or flowers to samples without berries or flowers. It was shown that the analysis of spectral data on the reflectance anisotropy improves the spectral discrimination of the dwarf shrub species. The contribution of the berries on the obtained shrub spectra was shown to be notable enough to justify further studies by applying unmanned aerial vehicle (UAV) platforms. Future studies on the aerial spectral data are suggested to evaluate the potential of berry mapping in larger-scale.Viimeaikaiset tutkimukset ovat osoittaneet monisuunta-spektrometrian hyödyt kasvillisuuden heijastusominaisuuksien karakterisoinnissa kaukokartoituksessa. Aluskasvillisuuden spektrien anisotropian tutkiminen edesauttaa kehittämään menetelmiä kasvilajien tunnistamiseksi ja tarjoaa validointiaineistoa metsien sirontamalleihin. Tämä diplomityö esittää menetelmät ja tulokset Suomen kahden taloudellisesti tärkeimmän luonnonmarjoja tuottavan varpukasvin, mustikan (Vaccinium myrtillus) ja puolukan (Vaccinium vitis-idaea), spektrien temporaalisesta karakterisointikampanjasta kasvukauden 2017 yli. Kaksisuuntainen heijastussuhdetekijä spektriaineisto mitattiin mustikan ja puolukan varpunäytteistä monisuuntamittausgeometriassa FIGIFIGO (Finnish Geodetic Institute Goniospectrometer) goniospektrometrillä laboratorio-olosuhteissa. Lehtien heijastus- ja läpäisyspektrit mitattiin molemmista lajeista käyttäen SpectroClip-TR mittalaitetta. Anisotropiset ominaispiirteet analysointiin aallonpituuksien 400 - 2200 nm välillä katselukulmariippuvuudelle (-40° to +40°), valaistuskulmariippuvuudelle (+40°, +55°), vuodenajan aiheuttamille muutoksille (kasvukausi 2017) sekä marja ja kukintojen tunnistamiselle. Sekä puolukka että mustikka osoittavat voimakasta taaksepäin suuntautuvaa ja huomattavaa eteenpäin suuntautuvaa ominaissirontaa päätasossa. Lajien välisessä vertailussa puolukka on kirkkaampi kaikkiin mitattuihin katselukulmiin näkyvän valon ja lähi-infrapunan aallonpituuksilla, mutta tummempi lyhytaaltoisen infrapunan alueella. Valaistuskulman zeniitin kasvattaminen 15° parantaa lajien spektrien erotettavuutta aiheuttamalla 12 %:n eron lajien heijastusvasteisiin. Yleisesti metsän kaukokartoituksessa käytetyt kasvillisuusindeksit (NDVI, NDVI705, MSI, PSRI) osoittavat matalaa herkkyyttä katselu- ja valaistuskulman muutoksille. Näytteessä olevat puolukanmarjat ja -kukat erottuvat spektrissä piikkinä 679 nm:n kohdalla, kun tarkastellaan marjallisten ja kukallisten näytteiden suhdetta marjattomiin ja kukattomiin. Spektriaineiston heijastus-anisotropian analysoinnin näytettiin edesauttavan varpukasvien erotettavuutta. Marjojen vahva kontribuutio varpunäytteistä mitattuihin spektreihin osoitettiin niin selkeästi, että jatkotutkimuksia UAV (unmanned aerial vehicle) -alustalla voidaan pitää perusteltuina. Ilma-aluksilla kerättyä aineistoa ehdotetaan käytettävän marjojen laajemman kartoituksen potentiaalin selvittämiseksi

Multisensory Imagery Cues for Object Separation, Specularity Detection and Deep Learning based Inpainting

Multisensory imagery cues have been actively investigated in diverse applications in the computer vision community to provide additional geometric information that is either absent or difficult to capture from mainstream two-dimensional imaging. The inherent features of multispectral polarimetric light field imagery (MSPLFI) include object distribution over spectra, surface properties, shape, shading and pixel flow in light space. The aim of this dissertation is to explore these inherent properties to exploit new structures and methodologies for the tasks of object separation, specularity detection and deep learning-based inpainting in MSPLFI. In the first part of this research, an application to separate foreground objects from the background in both outdoor and indoor scenes using multispectral polarimetric imagery (MSPI) cues is examined. Based on the pixel neighbourhood relationship, an on-demand clustering technique is proposed and implemented to separate artificial objects from natural background in a complex outdoor scene. However, due to indoor scenes only containing artificial objects, with vast variations in energy levels among spectra, a multiband fusion technique followed by a background segmentation algorithm is proposed to separate the foreground from the background. In this regard, first, each spectrum is decomposed into low and high frequencies using the fast Fourier transform (FFT) method. Second, principal component analysis (PCA) is applied on both frequency images of the individual spectrum and then combined with the first principal components as a fused image. Finally, a polarimetric background segmentation (BS) algorithm based on the Stokes vector is proposed and implemented on the fused image. The performance of the proposed approaches are evaluated and compared using publicly available MSPI datasets and the dice similarity coefficient (DSC). The proposed multiband fusion and BS methods demonstrate better fusion quality and higher segmentation accuracy compared with other studies for several metrics, including mean absolute percentage error (MAPE), peak signal-to-noise ratio (PSNR), Pearson correlation coefficient (PCOR) mutual information (MI), accuracy, Geometric Mean (G-mean), precision, recall and F1-score. In the second part of this work, a twofold framework for specular reflection detection (SRD) and specular reflection inpainting (SRI) in transparent objects is proposed. The SRD algorithm is based on the mean, the covariance and the Mahalanobis distance for predicting anomalous pixels in MSPLFI. The SRI algorithm first selects four-connected neighbouring pixels from sub-aperture images and then replaces the SRD pixel with the closest matched pixel. For both algorithms, a 6D MSPLFI transparent object dataset is captured from multisensory imagery cues due to the unavailability of this kind of dataset. The experimental results demonstrate that the proposed algorithms predict higher SRD accuracy and better SRI quality than the existing approaches reported in this part in terms of F1-score, G-mean, accuracy, the structural similarity index (SSIM), the PSNR, the mean squared error (IMMSE) and the mean absolute deviation (MAD). However, due to synthesising SRD pixels based on the pixel neighbourhood relationship, the proposed inpainting method in this research produces artefacts and errors when inpainting large specularity areas with irregular holes. Therefore, in the last part of this research, the emphasis is on inpainting large specularity areas with irregular holes based on the deep feature extraction from multisensory imagery cues. The proposed six-stage deep learning inpainting (DLI) framework is based on the generative adversarial network (GAN) architecture and consists of a generator network and a discriminator network. First, pixels’ global flow in the sub-aperture images is calculated by applying the large displacement optical flow (LDOF) method. The proposed training algorithm combines global flow with local flow and coarse inpainting results predicted from the baseline method. The generator attempts to generate best-matched features, while the discriminator seeks to predict the maximum difference between the predicted results and the actual results. The experimental results demonstrate that in terms of the PSNR, MSSIM, IMMSE and MAD, the proposed DLI framework predicts superior inpainting quality to the baseline method and the previous part of this research

Earth resources: A continuing bibliography with indexes (issue 55)

This bibliography lists 368 reports, articles and other documents introduced into the NASA scientific and technical information system between July 1 and September 30, 1987. Emphasis is placed on the use of remote sensing and geographical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Semi-automatic Road Extraction from Very High Resolution Remote Sensing Imagery by RoadModeler

Accurate and up-to-date road information is essential for both effective urban planning and disaster management. Today, very high resolution (VHR) imagery acquired by airborne and spaceborne imaging sensors is the primary source for the acquisition of spatial information of increasingly growing road networks. Given the increased availability of the aerial and satellite images, it is necessary to develop computer-aided techniques to improve the efficiency and reduce the cost of road extraction tasks. Therefore, automation of image-based road extraction is a very active research topic. This thesis deals with the development and implementation aspects of a semi-automatic road extraction strategy, which includes two key approaches: multidirectional and single-direction road extraction. It requires a human operator to initialize a seed circle on a road and specify a extraction approach before the road is extracted by automatic algorithms using multiple vision cues. The multidirectional approach is used to detect roads with different materials, widths, intersection shapes, and degrees of noise, but sometimes it also interprets parking lots as road areas. Different from the multidirectional approach, the single-direction approach can detect roads with few mistakes, but each seed circle can only be used to detect one road. In accordance with this strategy, a RoadModeler prototype was developed. Both aerial and GeoEye-1 satellite images of seven different types of scenes with various road shapes in rural, downtown, and residential areas were used to evaluate the performance of the RoadModeler. The experimental results demonstrated that the RoadModeler is reliable and easy-to-use by a non-expert operator. Therefore, the RoadModeler is much better than the object-oriented classification. Its average road completeness, correctness, and quality achieved 94%, 97%, and 94%, respectively. These results are higher than those of Hu et al. (2007), which are 91%, 90%, and 85%, respectively. The successful development of the RoadModeler suggests that the integration of multiple vision cues potentially offers a solution to simple and fast acquisition of road information. Recommendations are given for further research to be conducted to ensure that this progress goes beyond the prototype stage and towards everyday use

Earth resources: A continuing bibliography, issue 46

This bibliography lists 467 reports, articles and other documents introdcued into the NASA scientific and technical information system between April 1 and June 30, 1985. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental cultural resources geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economical analysis

Building Footprint Extraction from LiDAR Data and Imagery Information

This study presents an automatic method for regularisation of building outlines. Initially, building segments are extracted using a new fusion method. Data- and model-driven approaches are then combined to generate approximate building polygons. The core part of the method includes a novel data-driven algorithm based on likelihood equation derived from the geometrical properties of a building. Finally, the Gauss-Helmert and Gauss-Markov models adjustment are implemented and modified for regularisation of building outlines considering orthogonality constraints

Empirical Studies on Multiangular, Hyperspectral, and Polarimetric Reflectance of Natural Surfaces

The reflectance factor is a quantity describing the efficiency of a surface to reflect light and affecting the observed brightness of reflected light. It is a complex property that varies with the view and illumination geometries as well as the wavelength and polarization of the light. The reflectance factor response is a peculiar property of each target surface. In optical remote sensing, the observed reflectance properties of natural surfaces are used directly for, e.g., classifying targets. Also, it is possible to extract target physical properties from observations, but generally this requires an understanding and modeling of the reflectance properties of the target. The most direct way to expand our understanding of the reflectance properties of natural surfaces is through empirical measurements. This thesis presents three original measurement setups for obtaining the reflectance properties of natural surfaces and some of the results acquired using them. The first instrument is the Finnish Geodetic Institute Field Goniospectrometer (FIGIFIGO); an instrument for measuring the view angle dependency of polarized hyperspectral reflectance factor on small targets. The second instrument is an unmanned aerial vehicle (UAV) setup with a consumer camera used for taking measurements. The procedure allows 2D-mapping of the reflectance factor view angle dependency over larger areas. The third instrument is a virtual hyperspectral LiDAR, i.e. a setup for acquiring laser scanner point clouds with 3D-referenced reflectance spectra ([x,y,z,R(λ)]). During the research period 2005 2011, the FIGIFIGO was used to measure the angular reflectance properties of nearly 400 remote sensing targets, making the acquired reflectance library one of the largest of its kind in the world. These data have been exploited in a number of studies, including studies dealing with the vicarious calibration of airborne remote sensing sensors and satellite imagery and the development and characterization of reflectance reference targets for airborne remote sensing sensors, and the reflectance measurements have been published as a means of increasing the general understanding of the scattering of selected targets. The two latter instrument prototypes demonstrate emerging technologies that are being used in a novel way in remote sensing. Both measurement concepts have shown promising results, indicating that, in some cases, it can be beneficial to use such a methodology in place of the traditional remote sensing methods. Thus, the author believes that such measurement concepts will be used more widely in the near future. Heijastuskerroin on kullekin kohteelle yksilöllinen ominaisuus joka kuvaa kohteesta heijastuneen valon määrää. Heijastuskertoimen arvo riippuu havainto- ja valaistusgeometriasta sekä valon aallonpituudesta ja polarisaatiosta. Useimmissa optisen kaukokartoituksen menetelmissä mitataan kohteiden heijastuskerrointa. Näitä heijastuskerroinhavaintoja käytetään suoraan esim. kohteiden luokittelussa. Kehittyneemmissä menetelmissä havainnoista on myös mahdollista irrottaa joitain kohteen fysikaalisia ominaisuuksia, mutta yleensä tämä edellyttää kohteen ymmärtämistä sekä valonsironnan mallintamista. Suorin tapa laajentaa ymmärrystä luonnon pintojen valonsironnasta on tehdä empiirisiä mittauksia. Tässä väitöskirjassa esitellään kolme mittalaitetta luonnon pintojen valonsironnan mittaamiseksi sekä näillä laitteilla kerättyjä tuloksia. Ensimmäinen esiteltävä mittalaite on Finnish Geodetic Institute Field Goniospectrometer (FIGIFIGO), jolla voidaan mitata kohteen sirottaman valon suuntariippuvuutta valon aallonpituuden sekä polarisaation funktiona. Toinen mittalaite on automaattinen miehittämätön helikopteri. Kopteriin asennetun kameran sekä kuvien yhdistämismenetelmän avulla maaston valonsironnan suuntariippuvuutta voidaan kartoittaa laajemmilla alueilla kuin FIGIFIGO:a käyttäen. Kolmas mittalaite on virtuaalinen valkean valon LiDAR, jolla voidaan mitata laboratoriokohteen 3D rakenne yhdessä heijastusspektrien kanssa ([x,y,z,R(λ)]). Tutkimusjakson (2005 2011) aikana FIGIFIGO:a on käytetty lähes 400 kaukokartoituskohteen sironnan suuntariippuvuuden mittaamiseen. Näillä mittauksilla kerätty datakirjasto on yksi maailman suurimmista ja kattavimmistaan lajissaan. FIGIFIGO-mittauksia on hyödynnetty useissa tutkimuksissa esim. satelliitti havaintojen ja kaukokartoitus sensoreiden lennonaikaisessa kalibroinnissa ja validoinnissa, sekä ilmakuvauksen heijastuskerroinreferenssikohteiden kehittämisessä. Mittaustulokset on myös julkaistu tieteellisissä julkaisuissa laajentaen yleistä ymmärrystä kaukokartoituskohteiden valonsironnasta. Kaksi jälkimmäistä mittalaitetta ovat prototyyppejä joilla on testattu ja demonstroitu uutta tekniikkaa jota ei ole aiemmin hyödynnetty kaukokartoituksessa tällä tavoin. Molemmat mittauskonseptit tuottivat lupaavia tuloksia mahdollistaen uudentyyppisten mittausten tekemisen. Saadut tulokset antavat ymmärtää että mittauskonseptien kehittämistä kannattaa jatkaa ja on todennäköistä että tämän kaltaiset mittausmenetelmät tulevat jo lähitulevaisuudessa leviämään laajempaan käyttöön kaukokartoituksessa

Empirical Studies on Multiangular, Hyperspectral, and Polarimetric Reflectance of Natural Surfaces

The reflectance factor is a quantity describing the efficiency of a surface to reflect light and affecting the observed brightness of reflected light. It is a complex property that varies with the view and illumination geometries as well as the wavelength and polarization of the light. The reflectance factor response is a peculiar property of each target surface. In optical remote sensing, the observed reflectance properties of natural surfaces are used directly for, e.g., classifying targets. Also, it is possible to extract target physical properties from observations, but generally this requires an understanding and modeling of the reflectance properties of the target. The most direct way to expand our understanding of the reflectance properties of natural surfaces is through empirical measurements. This thesis presents three original measurement setups for obtaining the reflectance properties of natural surfaces and some of the results acquired using them. The first instrument is the Finnish Geodetic Institute Field Goniospectrometer (FIGIFIGO); an instrument for measuring the view angle dependency of polarized hyperspectral reflectance factor on small targets. The second instrument is an unmanned aerial vehicle (UAV) setup with a consumer camera used for taking measurements. The procedure allows 2D-mapping of the reflectance factor view angle dependency over larger areas. The third instrument is a virtual hyperspectral LiDAR, i.e. a setup for acquiring laser scanner point clouds with 3D-referenced reflectance spectra ([x,y,z,R(λ)]). During the research period 2005 2011, the FIGIFIGO was used to measure the angular reflectance properties of nearly 400 remote sensing targets, making the acquired reflectance library one of the largest of its kind in the world. These data have been exploited in a number of studies, including studies dealing with the vicarious calibration of airborne remote sensing sensors and satellite imagery and the development and characterization of reflectance reference targets for airborne remote sensing sensors, and the reflectance measurements have been published as a means of increasing the general understanding of the scattering of selected targets. The two latter instrument prototypes demonstrate emerging technologies that are being used in a novel way in remote sensing. Both measurement concepts have shown promising results, indicating that, in some cases, it can be beneficial to use such a methodology in place of the traditional remote sensing methods. Thus, the author believes that such measurement concepts will be used more widely in the near future. Heijastuskerroin on kullekin kohteelle yksilöllinen ominaisuus joka kuvaa kohteesta heijastuneen valon määrää. Heijastuskertoimen arvo riippuu havainto- ja valaistusgeometriasta sekä valon aallonpituudesta ja polarisaatiosta. Useimmissa optisen kaukokartoituksen menetelmissä mitataan kohteiden heijastuskerrointa. Näitä heijastuskerroinhavaintoja käytetään suoraan esim. kohteiden luokittelussa. Kehittyneemmissä menetelmissä havainnoista on myös mahdollista irrottaa joitain kohteen fysikaalisia ominaisuuksia, mutta yleensä tämä edellyttää kohteen ymmärtämistä sekä valonsironnan mallintamista. Suorin tapa laajentaa ymmärrystä luonnon pintojen valonsironnasta on tehdä empiirisiä mittauksia. Tässä väitöskirjassa esitellään kolme mittalaitetta luonnon pintojen valonsironnan mittaamiseksi sekä näillä laitteilla kerättyjä tuloksia. Ensimmäinen esiteltävä mittalaite on Finnish Geodetic Institute Field Goniospectrometer (FIGIFIGO), jolla voidaan mitata kohteen sirottaman valon suuntariippuvuutta valon aallonpituuden sekä polarisaation funktiona. Toinen mittalaite on automaattinen miehittämätön helikopteri. Kopteriin asennetun kameran sekä kuvien yhdistämismenetelmän avulla maaston valonsironnan suuntariippuvuutta voidaan kartoittaa laajemmilla alueilla kuin FIGIFIGO:a käyttäen. Kolmas mittalaite on virtuaalinen valkean valon LiDAR, jolla voidaan mitata laboratoriokohteen 3D rakenne yhdessä heijastusspektrien kanssa ([x,y,z,R(λ)]). Tutkimusjakson (2005 2011) aikana FIGIFIGO:a on käytetty lähes 400 kaukokartoituskohteen sironnan suuntariippuvuuden mittaamiseen. Näillä mittauksilla kerätty datakirjasto on yksi maailman suurimmista ja kattavimmistaan lajissaan. FIGIFIGO-mittauksia on hyödynnetty useissa tutkimuksissa esim. satelliitti havaintojen ja kaukokartoitus sensoreiden lennonaikaisessa kalibroinnissa ja validoinnissa, sekä ilmakuvauksen heijastuskerroinreferenssikohteiden kehittämisessä. Mittaustulokset on myös julkaistu tieteellisissä julkaisuissa laajentaen yleistä ymmärrystä kaukokartoituskohteiden valonsironnasta. Kaksi jälkimmäistä mittalaitetta ovat prototyyppejä joilla on testattu ja demonstroitu uutta tekniikkaa jota ei ole aiemmin hyödynnetty kaukokartoituksessa tällä tavoin. Molemmat mittauskonseptit tuottivat lupaavia tuloksia mahdollistaen uudentyyppisten mittausten tekemisen. Saadut tulokset antavat ymmärtää että mittauskonseptien kehittämistä kannattaa jatkaa ja on todennäköistä että tämän kaltaiset mittausmenetelmät tulevat jo lähitulevaisuudessa leviämään laajempaan käyttöön kaukokartoituksessa