9 research outputs found
Spatial-Spectral Manifold Embedding of Hyperspectral Data
In recent years, hyperspectral imaging, also known as imaging spectroscopy,
has been paid an increasing interest in geoscience and remote sensing
community. Hyperspectral imagery is characterized by very rich spectral
information, which enables us to recognize the materials of interest lying on
the surface of the Earth more easier. We have to admit, however, that high
spectral dimension inevitably brings some drawbacks, such as expensive data
storage and transmission, information redundancy, etc. Therefore, to reduce the
spectral dimensionality effectively and learn more discriminative spectral
low-dimensional embedding, in this paper we propose a novel hyperspectral
embedding approach by simultaneously considering spatial and spectral
information, called spatial-spectral manifold embedding (SSME). Beyond the
pixel-wise spectral embedding approaches, SSME models the spatial and spectral
information jointly in a patch-based fashion. SSME not only learns the spectral
embedding by using the adjacency matrix obtained by similarity measurement
between spectral signatures, but also models the spatial neighbours of a target
pixel in hyperspectral scene by sharing the same weights (or edges) in the
process of learning embedding. Classification is explored as a potential
strategy to quantitatively evaluate the performance of learned embedding
representations. Classification is explored as a potential application for
quantitatively evaluating the performance of these hyperspectral embedding
algorithms. Extensive experiments conducted on the widely-used hyperspectral
datasets demonstrate the superiority and effectiveness of the proposed SSME as
compared to several state-of-the-art embedding methods
Spatial-Spectral Manifold Embedding of Hyperspectral Data
In recent years, hyperspectral imaging, also known as imaging spectroscopy, has been paid an increasing interest in geoscience and
remote sensing community. Hyperspectral imagery is characterized by very rich spectral information, which enables us to recognize the materials of interest lying on the surface of the Earth more easier. We have to admit, however, that high spectral dimension inevitably brings some drawbacks, such as expensive data storage and transmission, information redundancy, etc. Therefore, to reduce the spectral dimensionality effectively and learn more discriminative spectral low-dimensional embedding, in this paper we propose a novel hyperspectral embedding approach by simultaneously considering spatial and spectral information, called spatialspectral manifold embedding (SSME). Beyond the pixel-wise spectral embedding approaches, SSME models the spatial and spectral information jointly in a patch-based fashion. SSME not only learns the spectral embedding by using the adjacency matrix obtained by similarity measurement between spectral signatures, but also models the spatial neighbours of a target pixel in hyperspectral scene by sharing the same weights (or edges) in the process of learning embedding. Classification is explored as a potential strategy
to quantitatively evaluate the performance of learned embedding representations. Classification is explored as a potential application for quantitatively evaluating the performance of these hyperspectral embedding algorithms. Extensive experiments conducted on the widely-used hyperspectral datasets demonstrate the superiority and effectiveness of the proposed SSME as compared to several state-of-the-art embedding methods
X-ModalNet: A Semi-Supervised Deep Cross-Modal Network for Classification of Remote Sensing Data
This paper addresses the problem of semi-supervised transfer learning with
limited cross-modality data in remote sensing. A large amount of multi-modal
earth observation images, such as multispectral imagery (MSI) or synthetic
aperture radar (SAR) data, are openly available on a global scale, enabling
parsing global urban scenes through remote sensing imagery. However, their
ability in identifying materials (pixel-wise classification) remains limited,
due to the noisy collection environment and poor discriminative information as
well as limited number of well-annotated training images. To this end, we
propose a novel cross-modal deep-learning framework, called X-ModalNet, with
three well-designed modules: self-adversarial module, interactive learning
module, and label propagation module, by learning to transfer more
discriminative information from a small-scale hyperspectral image (HSI) into
the classification task using a large-scale MSI or SAR data. Significantly,
X-ModalNet generalizes well, owing to propagating labels on an updatable graph
constructed by high-level features on the top of the network, yielding
semi-supervised cross-modality learning. We evaluate X-ModalNet on two
multi-modal remote sensing datasets (HSI-MSI and HSI-SAR) and achieve a
significant improvement in comparison with several state-of-the-art methods
HOTSPOT ANALYSIS AND COMPARISON BETWEEN SATELLITE-DERIVED AEROSOL OPTICAL DEPTH AND GROUND-BASED PARTICULATE MATTER MEASUREMENTS IN METRO MANILA
Highly urbanized regions such as the Metro Manila area in the Philippines contribute to the deterioration of air quality through overpopulation, excessive vehicle emissions, and industrialization. However, the limited number of ground monitoring stations hinders the detailed estimation of the region’s overall air quality. Satellite-derived air pollutant concentrations have been used in several research studies as a substitute or supplementary to ground-based data due to their extensive spatial and temporal coverage. Using the aerosol optical depth (AOD) from the MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm and ground measurements of coarse particulate matter (PM10), this study explores the comparison between satellite-derived and ground-based air pollutant concentrations measured from 2017 to 2020 through trend analysis of monthly average values per city. With 16 stations located in different cities, the monthly average values of AOD vs PM10 showed inconsistent results due to significant gaps in the ground data. Through optimized hotspot analysis, it was found that 7.24% of the Metro Manila region are considered hotspots using the MAIAC AOD values from 2017 to 2019 (pre-pandemic). From 2018 to 2020 (pandemic), 23.86% of Metro Manila are counted as hotspots. The AOD derived from satellite imagery and hotspot analysis can be used for future studies that focus on the development of models to predict ground pollutant values and the designation of non-attainment areas
Estimación de la concentración de material particulado mediante sensoramiento remoto en la provincia de Lima, 2020
La contaminación del aire es una de las mayores preocupaciones, ya que, genera
afectaciones en la salud y el ambiente, por otro lado, el monitoreo mediante
estaciones convencionales tiene un alto costo y requiere constante mantenimiento
generando brechas temporales a largo plazo. En tal sentido, la provincia de Lima
por su gran expansión urbana tiene una alta contaminación por material
particulado y las estaciones actuales tienen desventajas. Es por ello, que el
objetivo de esta investigación fue estimar la concentración de material particulado
mediante sensoramiento remoto en la provincia de Lima. Para ello, se utilizaron
las imágenes multiespectrales del sensor MSI a bordo de los satélites Sentinel 2A
y 2B, por otro lado, se solicitaron a las estaciones automáticas de SENAMHI los
datos de material particulado (PM10 y PM2.5) a escala diaria y horaria para el
periodo conformado por los años 2017 al 2020. Las imágenes multiespectrales se
dividieron según el porcentaje de nubosidad (20% <= NUBOSIDAD < 20%), asÃ
mismo, se calculó la reflectancia en la parte superior de la atmosfera (TOA). De
esta manera, en función a los datos de material particulado solicitados se
identificaron las bandas espectrales que influyeron significativamente en la
estimación de estos contaminantes, adicionalmente, mediante el análisis de
varianza se validaron las ecuaciones obtenidas (p-valor < 0.05), finalmente al
contrastar los valores medidos con los estimados se obtuvo como resultado que el
poder estimador para las concentraciones de PM10 a escala diaria fueron
mayores con coeficientes de determinación de 0.63 (20% <= NUBOSIDAD) y de
0.65 (NUBOSIDAD < 20%), para el caso de las concentraciones horarias se
obtuvieron coeficientes de determinación de 0.52 (20% <= NUBOSIDAD) y 0.35
(NUBOSIDAD < 20%). En el caso de las concentraciones de PM2.5 el poder
estimador fue mÃnimo, puesto que, se obtuvieron valores de 0.41 (20% <=
NUBOSIDAD) y 0.45 (NUBOSIDAD < 20%) a escala diaria y de 0.30 (20% <=
NUBOSIDAD) y 0.34 (NUBOSIDAD < 20%) a escala horaria
Advances in Evaporation and Evaporative Demand
The importance of evapotranspiration is well-established in different disciplines such as hydrology, agronomy, climatology, and other geosciences. Reliable estimates of evapotranspiration are also vital to develop criteria for in-season irrigation management, water resource allocation, long-term estimates of water supply, demand and use, design and management of water resources infrastructure, and evaluation of the effect of land use and management changes on the water balance. The objective of this Special Issue is to define and discuss several ET terms, including potential, reference, and actual (crop) ET, and present a wide spectrum of innovative research papers and case studies
Estimation of PMx Concentrations from Landsat 8 OLI Images Based on a Multilayer Perceptron Neural Network
The estimation of PMx (incl. PM10 and PM2.5) concentrations using satellite observations is of great significance for detecting environmental issues in many urban areas of north China. Recently, aerosol optical depth (AOD) data have been being used to estimate the PMx concentrations by implementing linear and/or nonlinear regression analysis methods. However, a lot of relevant research based on AOD published so far have demonstrated some limitations in estimating the spatial distribution of PMx concentrations with respect to estimation accuracy and spatial resolution. In this research, the Google Earth Engine (GEE) platform is employed to obtain the band reflectance (BR) data of a large number of Landsat 8 Operational Land Imager (OLI) remote sensing images. Combined with the meteorological, time parameter and the latitude and longitude zone (LLZ) method proposed in this article, a new BR (band reflectance)-PMx (incl. PM10 and PM2.5) model based on a multilayer perceptron neural network is constructed for the estimation of PMx concentrations directly from Landsat 8 OLI remote sensing images. This research used Beijing, China as the test area and the conducted experiments demonstrated that the BR-PMx model achieved satisfactory performances for the PMx-concentration estimations. The coefficient of determination (R2) of the BR-PM2.5 and BR-PM10 models reached 0.795 and 0.773, respectively, and the root mean square error (RMSE) reached 20.09 μg/m3 and 31.27 μg/m3. Meanwhile, the estimation results have been compared with the results calculated by Kriging interpolation at the same time point, and the spatial distribution is consistent. Therefore, it can be concluded that the proposed BR-PMx model provides a new promising method for acquiring accurate PMx concentrations for various cities of China