We present RAAD (Rapid Acquisition Atmospheric Detector), a detector designed to study Terrestrial Gamma ray Flashes (TGFs) and other fast hard X-ray and soft gamma-ray phenomena. TGFs are bursts of radiation from thunderstorms which occur on sub-microsecond timescales. Most detectors used to study TGFs have been limited by deadtime and timing precision, and sometimes poor calibration at lower energies. We will present calibration and space qualification tests of a detector aimed at the 20 keV - 2500 keV range with ~ 100 ns time response and good spectral resolution. This uses 2 X 2 arrays of two different scintillation crystals, Cerium Bromide and Lanthanum BromoChloride, both of which have very fast decay times. We couple them to both standard photomultiplier tubes (PMTs) and silicon photomultipliers (SiPMs) along with custom electronics designed to provide very fast sampling with very low power consumption per channel. Each crystal array fits into < 1U of a cubesat, and provides ~20 cm2 of effective area to photons < 200 keV and ~10 cm2 at 600 keV. The RAAD concept is the winner of the Mini-satellite competition held by the UAE Space Agency in 2018, largely developed with undergraduates at NYUAD, and is expected to be fully developed and launched by 2020. Two detectors, one with PMTs and one with SiPMs will be deployed on a 3U CubeSat, providing head to head performance tests for both crystal types and light sensor types. This will serve as a proof of concept showing how such detectors could be deployed in a network of CubeSats to study TGFs and other phenomena.
This paper assesses the suitability of 8-band Worldview-2 (WV2) satellite data and object-based random forest algorithm
for the classification of avocado growth stages in Mexico. We tested both pixel-based with minimum distance (MD) and
maximum likelihood (MLC) and object-based with Random Forest (RF) algorithm for this task. Training samples and
verification data were selected by visual interpreting the WV2 images for seven thematic classes: fully grown, middle
stage, and early stage of avocado crops, bare land, two types of natural forests, and water body. To examine the
contribution of the four new spectral bands of WV2 sensor, all the tested classifications were carried out with and
without the four new spectral bands. Classification accuracy assessment results show that object-based classification
with RF algorithm obtained higher overall higher accuracy (93.06%) than pixel-based MD (69.37%) and MLC (64.03%)
method. For both pixel-based and object-based methods, the classifications with the four new spectral bands (overall
accuracy obtained higher accuracy than those without: overall accuracy of object-based RF classification with vs
without: 93.06% vs 83.59%, pixel-based MD: 69.37% vs 67.2%, pixel-based MLC: 64.03% vs 36.05%, suggesting that
the four new spectral bands in WV2 sensor contributed to the increase of the classification accuracy.
In this paper, we develop a new framework for spectral unmixing of multispectral remote sensing images with
limited spectral resolution. Our proposed approach performs dimensionality expansion by taking advantage of the
spatial information contained in such images. For this purpose, in this work, we experiment with morphological
profiles and morphological attribute filters, which allow expanding the dimensionality of the original image and
obtaining a detailed signature (profile) at each pixel using the SVM classifier. This allows for the application of
spectral unmixing techniques that integrate both the spatial and the spectral information, since the unmixing is
not only based on the original multispectral/color information but also takes into account the additional bands
included by exploiting the spatial information. The unmixing chain considered in this work comprises a classic
endmember extraction algorithm: vertex component analysis (VCA) followed by fully constrained linear spectral
unmixing (FCLSU) to estimate the abundance of each endmember in each pixel of the image. Kernel principal
component analysis (KPCA) is also used in the considered chain, to increase dimensionality in the spectral
domain only and to perform feature extraction. In order to quantitatively validate the proposed framework, we
use the RGB bands of a set of registered hyperspectral images. Specifically, we use the ground-truth to validate
the unmixing results obtained for the lower spatial resolution scenes. Our experimental results indicate that the
proposed dimensionality expansion strategy allows for the successful unmixing of multispectral satellite images,
specially for RGB/color images.
In this paper we investigate the application of Morphological Attribute Profiles to both hyperspectral and LiDAR data
to fuse spectral, spatial and elevation data for classification purposes. While hyperspectral data provides a wealth of
spectral information, multi-return LiDAR data provides geometrical information on the elevation and the structure of the
objects on the ground as well as a measure of their laser cross section. Therefore, hyperspectral and LiDAR data are
complementary information sources and potentially their joint analysis can improve classification accuracies.
Morphological Profiles (MPs) and Morphological Attribute Profiles (MAPs) have been successfully used as tools to
combine spectral and spatial information for classification of remote sensing data. MPs and MAPs can also be used with
the LiDAR data to reduce the irregularities in the LiDAR measurements which are inherent with the sampling strategy
used in the acquisition process. Experiments carried out on hyperspectral and LiDAR data acquired on a urban area of
the city of Trento (Italy) point out the effectiveness of MAPs for the classification process.
Morphological profiles (MPs) have been effective tools to fuse spectral and spatial information for the classification of
remote sensing data. However, the previous applications have been limited to the multi-/ hyper-spectral data analysis. In
this study, the application of morphological profiles is extended for the classification of polarimetric synthetic aperture
radar (POLSAR) data. The MPs are constructed with the diagonal elements of the covariance matrix and the features
derived from the eigenvalue decomposition method. The resulting extended morphological profile (EMP) which is a
stack of all the MPs of various features is used for supervised classification of the images using a support vector machine
(SVM) classifier. It is shown that significant improvements in classification accuracies can be achieved by using the
profiles.
The paper presents some recent developments on object-based change detection and classification. In detail,
the following algorithms were implemented either as Matlab or IDL programmes or as plug-ins for Definiens
Developer: i) object-based change detection: segmentation of bitemporal datasets, change detection using the
Multivariate Alteration Detection1 based on object features; ii) object features and object feature extraction:
moment invariants, automated extraction of object features using Bayesian statistics; iii) object-based classification
by neural networks: FFN and Class- dependent FFN using five different learning algorithms. The paper
introduces the methodologies, describes the implementation and gives some examples results on the application.
Earth observation generally represents a key source of information for the different national and international
bodies involved in the implementation of international agreements. If the area of interest is not accessible,
remote sensing sensors represent one of the few opportunities to gather almost realtime data over the area.
Taking into consideration recent developments in satellite sensor technologies and software solutions, the given
paper discusses some challenges with regard to both technical and political issues.
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