Multi-source imagery fusion using deep learning in a cloud computing platform

Given the high availability of data collected by different remote sensing instruments, the data fusion of multi-spectral and hyperspectral images (HSI) is an important topic in remote sensing. In particular, super-resolution as a data fusion application using spatial and spectral domains is highly investigated because its fused images is used to improve the classification and tracking objects accuracy. On the other hand, the huge amount of data obtained by remote sensing instruments represent a key concern in terms of data storage, management and pre-processing. This paper proposes a Big Data Cloud platform using Hadoop and Spark to store, manages, and process remote sensing data. Also, a study over the parameter \textit{chunk size} is presented to suggest the appropriate value for this parameter to download imagery data from Hadoop into a Spark application, based on the format of our data. We also developed an alternative approach based on Long Short Term Memory trained with different patch sizes for super-resolution image. This approach fuse hyperspectral and multispectral images. As a result, we obtain images with high-spatial and high-spectral resolution. The experimental results show that for a chunk size of 64k, an average of 3.5s was required to download data from Hadoop into a Spark application. The proposed model for super-resolution provides a structural similarity index of 0.98 and 0.907 for the used dataset

A tip-tilt hardware-in-the-loop air-bearing test bed with physical emulation of the relative orbital dynamics

29th AAS/AIAA Space Flight Mechanics Meeting: Ka’anapali, Maui, Hawaii, U.S.A. Volume: Advances in the Astronautical Sciences (Vol. 168, pp. 3781–3799). Univelt Inc.A new hardware-in-the-loop (HIL) air bearing testbed that is capable of physically emulating the relative orbital dynamics is presented. Typically, air bearing testbeds consist of test vehicles operating on top of a planar and horizontally-leveled sur face. These test vehicles use air bearings to reduce the friction with the operating surface to negligible levels. The low friction, combined with the horizontally leveled surface, creates a low residual acceleration environment. These dynamics are representative of the environment that spacecraft experience during close proximity maneuvers. To extend the applicability of planar air bearing test beds to longer maneuvers or separations relative orbital dynamics need to be emulated. In this paper, using Hill-Clohessy-Wilshire dynamics, we emulated the relative orbital dynamics of a real spacecraft using a scaled Floating Spacecraft Simulator (FSS) on a dynamically inclined operating surface. The mathematical constructs of the tilt angles, screw height displacements and scaling parameters are developed via Euler’s rotation theorem, Buckingham’s Pi theorem and the similarity principle. The applicability of the new idea is demonstrated via a circumnavigation maneuver scenario of a spacecraft in a Low Earth Orbit (LEO). The simulation results show the viability and suitability of the new approach

Soil Contamination Mapping with Hyperspectral Imagery: Pre- Dnieper Chemical Plant (Ukraine) Case Study

Radioactive contamination of soils is an issue of severe importance for Ukraine remaining with a significant post-Soviet baggage of not settled problems regarding radioactive waste. Regular radioecological observations and up-to-date contamination mapping based on advanced geoinformation techniques give an ability to prepare for, respond to, and manage potential adverse effects from pollution with radionuclides and heavy metals. Hyperspectral satellite imagery provides potentially powerful tool for soil contamination detection and mapping. An intention to find a relation between remotely sensed hyperspectral and ground-based measured soil contamination fractions in area of the uranium mill tailings deposits near Kamianske city was made. An advanced algorithm based on known TCMI (target-constrained minimal interference)-matched filter with a nonnegative constraint was applied to determine the soil contamination fractions by hyperspectral imagery. The time series maps of spatial distribution of the soil contamination fractions within study area around the Sukhachevske tailings dump are presented. Time series analysis of the map resulted in two independent parameters: the average value for the entire observation period and the daily mean increment of the soil contamination fractions

Continuity of Landsat Obersvations: Short Term Considerations

As of writing in mid-2010, both Landsat-5 and -7 continue to function, with sufficient fuel to enable data collection until the launch of the Landsat Data Continuity Mission (LDCM) scheduled for December of 2012. Failure of one or both of Landsat-5 or -7 may result in a lack of Landsat data for a period of time until the 2012 launch. Although the potential risk of a component failure increases the longer the sensor\u27s design life is exceeded, the possible gap in Landsat data acquisition is reduced with each passing day and the risk of Landsat imagery being unavailable diminishes for all except a handful of applications that are particularly data demanding. Advances in Landsat data compositing and fusion are providing opportunities to address issues associated with Landsat-7 SLC-off imagery and to mitigate a potential acquisition gap through the integration of imagery from different sensors. The latter will likely also provide short-term, regional solutions to application-specific needs for the continuity of Landsat-like observations. Our goal in this communication is not to minimize the community\u27s concerns regarding a gap in Landsat observations, but rather to clarify how the current situation has evolved and provide an up-to-date understanding of the circumstances, implications, and mitigation options related to a potential gap in the Landsat data record

Using EO-1 Hyperion to Simulate HyspIRI Products for a Coniferous Forest: The Fraction of PAR Absorbed by Chlorophyll (fAPAR(sub chl)) and Leaf Water Content (LWC)

This study presents development of prototype products for terrestrial ecosystems in preparation for the future imaging spectrometer planned for the Hyperspectral Infrared Imager (HyspIRI) mission. We present a successful demonstration example in a coniferous forest of two product prototypes: fraction of photosynthetic active radiation (PAR) absorbed by chlorophyll of a canopy (fAPAR(sub chl)) and leaf water content (LWC), for future HyspIRI implementation at 60 m spatial resolution. For this, we used existing 30 m resolution imaging spectrometer data available from the Earth Observing One (EO-1) Hyperion satellite to simulate and prototype the level one radiometrically corrected radiance (L1R) images expected from the HyspIRI visible through shortwave infrared spectrometer. The HyspIRI-like images were atmospherically corrected to obtain surface reflectance, and spectrally resampled to produce 60 m reflectance images for wavelength regions that were comparable to all seven of the MODerate resolution Imaging Spectroradiometer (MODIS) land bands. Thus, we developed MODIS-like surface reflectance in seven spectral bands at the HyspIRI-like spatial scale, which was utilized to derive fAPARchl and LWC with a coupled canopy-leaf radiative transfer model (PROSAIL2) for the coniferous forest[1]. With this study, we provide additional evidence that the fAPARchl product is more realistic for describing the physiologically active canopy than the traditional fAPAR parameter for the whole canopy (fAPAR(sub canopy)), and thus should replace it in ecosystem process models to reduce uncertainties in terrestrial carbon cycle studies and ecosystem studies

Preface: the environmental mapping and analysis program (EnMAP) mission: preparing for its scientific exploitation

Open access; distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) licenseThe imaging spectroscopy mission EnMAP aims to assess the state and evolution of terrestrialandaquaticecosystems,examinethemultifacetedimpactsofhumanactivities,andsupport a sustainable use of natural resources. Once in operation (scheduled to launch in 2019), EnMAP will provide high-quality observations in the visible to near-infrared and shortwave-infrared spectral range. The scientific preparation of the mission comprises an extensive science program. This special issue presents a collection of research articles, demonstrating the potential of EnMAP for various applications along with overview articles on the mission and software tools developed within its scientific preparation.Ye

The investigation of the inability to distinguish among the coniferous forest species using hyperspectral satellite image

Günümüzde çağdaş ormancılığın amacı, ormanın sürekliliğini sağlayarak optimum yararlanmayı temin etmektir. Dolayısıyla ormanın verimi, sürekliliği ve korunması için yürütülen gençleştirme, bakım, ağaçlandırma gibi ormancılık faaliyetlerinin en iyi şekilde gerçekleştirilebilmesi için orman alanlarına ait bilginin güncel olması gerekmektedir. Son yıllarda teknolojinin gelişmesi ile birlikte ucuz ve hızlı sonuç alınabilinen uzaktan algılama tekniğinin ormancılık çalışmalarında önemi giderek artmaktadır. Bitki örtüsünün çok bantlı uydu görüntüleri ile izlenmesi literatürde en çok kullanılan yöntemlerden birisidir. Son zamanlarda objelerin ayırt edilebilirliklerini arttırarak daha iyi sonuçlar elde etmek amacıyla hiperspektral veya görüntü spektroskopisi olarak adlandırılan birçok yeni algılayıcı geliştirilmiştir. Objelerin spektral karakteristikleri sürekli ve daha çok spektral bantlara sahip olan hiperspektral uydu görüntüleri ile daha iyi belirlenmektedir. Bu çalışmada da Sündiken Kütlesi’ndeki ibreli orman türleri EO–1 Hyperion hiperspektral uydu görüntüsü (17.09.2004) ile ayırt edilmeye çalışılmıştır. Sündiken Kütlesi’nde baskın olarak saf ibreli orman türleri ve bunun yanında meşe ile ibreli ağaç türlerinin birlikte yetiştiği karışık orman alanları bulunmaktadır. Çalışmada yer ölçmelerine bağlı olarak Hiperspektral uydu görüntülerinden elde edilen test alanlarına ait spektral eğriler kullanılarak SAM (Spectral Angle Mapper) algoritması ile sınıflandırma işlemi yapılmıştır. Elde edilen sonuçlar Karaçam (Pinus nigra) ve Sarıçam (Pinus sylvestris) ağaç türlerinin spektral özelliklerinin çok benzer olması sebebiyle birbirinden ayırt edilebilirliğinin zayıf, Kızılçam’ın (Pinus brutia) ise ayırt edilebilirliğinin yüksek olduğunu göstermiştir. Anahtar Kelimeler: Uydu görüntüsü, hiperspektral, sınıflandırma, orman, ibreli.In nowadays, the principal goal of modern forestry is to provide the capacity to be useful itself of the sustainability of forest. Consequently, current information on areas of forest should be reached in order to realize the services of forestry like the regeneration, the forest tending, and the forestation which are carried out in order to contribute to the productivity, continuity, and the protection of the forests. With recently developed technology, the remote sensing providing of the cost-effective, reliable and fast results obtains the significance information on the forest applications. Vegetation monitoring using broad band multispectral remote sensing is well established. Recently, many new sensors are being developed to increase the discrimination ability of the objects, such as the acquisition of hundreds of narrow band spectra, termed hyperspectral remote sensing or imaging spectroscopy. Imaging spectroscopy refers to data acquired by an airborne or spaceborne imaging spectrometer and the analysis techniques applied to these data in ways that exploit the instrument's ability to resolve absorption features caused by the chemical bonds and physical structure of surface materials. In comparison to the handful of channels available with multispectral, broad band remote sensing, imaging spectrometers measure the radiation upwelling from a surface in hundreds of contiguous, narrow band width channels. The advantage offered by such spectroscopic measurements is the ability to resolve absorption features and determine their specific wavelength positions and characteristic shapes. These absorption features can be related to the material or materials causing them; thus, the materials occurring in a pixel of imaging spectroscopy data can be identified. In this study, the coniferous forest tree species were tried to be distinguished by using hyperspectral image taken by the EO-1 Hyperion at Sundiken Mountain, Turkey. The Sundiken Mountain is strongly covered with pure coniferous forests as well as mixed stands with deciduous trees. The dominant deciduous trees are oak (Quercus spp.), occurring in mixed stands with pine species stands. Anatolian Black Pine (Pinus nigra), Turkish Red Pine (Pinus brutia) and Scots Pine (Pinus sylvestris) are the dominant naturally-growing coniferous trees. Anatolian Black Pine and Turkish Red Pine occurs mixed with the oak trees. The spatial distribution of forest species on the Sundiken Mountain is changing related to the altitude and aspect. The intemperate forests at high elevations in the region receive large amounts of precipitation during the long, cold winter. At lower elevations, in Sundiken's relatively temperate valleys, Turkish Red Pine and oak trees communities predominate. In addition to altitude, the aspect has an influence on the distribution of plants within the mountain.The atmospheric and the topographic effects in the EO-1 Hyperion image, used in this study, were corrected and converted to reflectance values using the ATCOR-4 (ATmospheric CORrection ) program. Hyperion collects data in 224 contiguous channels of approximately 10-nm bandpass over the spectral wavelength range of 0.35?2.50 µm (from visible light to near-infrared). The missing portions of the spectrum have low signal to noise due to strong atmospheric water vapour absorption, and are not used in subsequent calculations. In Sundiken, for the mean elevation of 1500 m, the Hyperion sensor measured pixels with a nominal size of 30 m at nadir view. The sensor swath width was approximately 7.5 km. Hyperion data were acquired on September 17, 2004, at approximately 10:20 a.m. local time. Our goal is to measure the ability to distinguish related to the spectral variability of coniferous types using hyperspectral data. To do this, the measurements of band depths for chlorophyll and leaf water content of each forest types obtained from hyperspectral image are examined. Finally, we classified the hyperspectral image using the spectral angle mapper (SAM) classification algorithm and compared directly to the ground truth. The Spectral Angle Mapper is a technique to classify hyperspectral data by determining the similarity between an endmember spectrum and a pixel spectrum in an n-dimensional space. Smaller angles represent closer matches to the reference spectrum. Image-based end member spectra of the main land cover types and tree species in the test area are used as input for the classification. The results obtained from the classification showed that the distinction of Turkish Red Pine from other species is satisfied while to distinguish the tree species of Anatolian Black Pine and Scots Pine are weak; because their spectral properties are very similar. Keywords: Satellite images, hyperspectral, classification, forest, coniferous

Reduction of Radiometric Miscalibration—Applications to Pushbroom Sensors

The analysis of hyperspectral images is an important task in Remote Sensing. Foregoing radiometric calibration results in the assignment of incident electromagnetic radiation to digital numbers and reduces the striping caused by slightly different responses of the pixel detectors. However, due to uncertainties in the calibration some striping remains. This publication presents a new reduction framework that efficiently reduces linear and nonlinear miscalibrations by an image-driven, radiometric recalibration and rescaling. The proposed framework—Reduction Of Miscalibration Effects (ROME)—considering spectral and spatial probability distributions, is constrained by specific minimisation and maximisation principles and incorporates image processing techniques such as Minkowski metrics and convolution. To objectively evaluate the performance of the new approach, the technique was applied to a variety of commonly used image examples and to one simulated and miscalibrated EnMAP (Environmental Mapping and Analysis Program) scene. Other examples consist of miscalibrated AISA/Eagle VNIR (Visible and Near Infrared) and Hawk SWIR (Short Wave Infrared) scenes of rural areas of the region Fichtwald in Germany and Hyperion scenes of the Jalal-Abad district in Southern Kyrgyzstan. Recovery rates of approximately 97% for linear and approximately 94% for nonlinear miscalibrated data were achieved, clearly demonstrating the benefits of the new approach and its potential for broad applicability to miscalibrated pushbroom sensor data