Spaceborne sensors systems are characterized by scarce onboard computing and storage resources and by communication links with reduced bandwidth. Random projections techniques have been demonstrated as an effective and very light way to reduce the number of measurements in hyperspectral data, thus, the data to be transmitted to the Earth station is reduced. However, the reconstruction of the original data from the random projections may be computationally expensive. SpeCA is a blind hyperspectral reconstruction technique that exploits the fact that hyperspectral vectors often belong to a low dimensional subspace. SpeCA has shown promising results in the task of recovering hyperspectral data from a reduced number of random measurements. In this manuscript we focus on the implementation of the SpeCA algorithm for graphics processing units (GPU) using the compute unified device architecture (CUDA).
Experimental results conducted using synthetic and real hyperspectral datasets on the GPU architecture by NVIDIA: GeForce GTX 980, reveal that the use of GPUs can provide real-time reconstruction. The achieved speedup is up to 22 times when compared with the processing time of SpeCA running on one core of the Intel i7-4790K CPU (3.4GHz), with 32 Gbyte memory.
Hyperspectral imaging can be used for object detection and for discriminating between different objects based on their spectral characteristics. One of the main problems of hyperspectral data analysis is the presence of mixed pixels, due to the low spatial resolution of such images. This means that several spectrally pure signatures (endmembers) are combined into the same mixed pixel. Linear spectral unmixing follows an unsupervised approach which aims at inferring pure spectral signatures and their material fractions at each pixel of the scene. The huge data volumes acquired by such sensors put stringent requirements on processing and unmixing methods.
This paper proposes an efficient implementation of a unsupervised linear unmixing method on GPUs using CUDA. The method finds the smallest simplex by solving a sequence of nonsmooth convex subproblems using variable splitting to obtain a constraint formulation, and then applying an augmented Lagrangian technique. The parallel implementation of SISAL presented in this work exploits the GPU architecture at low level, using shared memory and coalesced accesses to memory. The results herein presented indicate that the GPU implementation can significantly accelerate the method's execution over big datasets while maintaining the methods accuracy.
A large number of remote sensing data sets have been collected in recent years by Earth observation instruments such as the moderate resolution imaging spectroradiometer (MODIS) aboard the Terra/Aqua satellite and the spinning enhanced visible and infrared imager (SEVIRI) aboard the geostationary platform Meteosat Second Generation. The advanced remote sensing products resulting from the analysis of these data are useful in a wide variety of applications but require significant resources in terms of storage, retrieval, and analysis. Despite the wide availability of these MODIS/SEVIRI products, the data coming from these instruments are spread among different locations and retrieved from different sources, and there is no common data repository from which the data or the associated products can be retrieved. We take a first step toward the development of a geo-portal for storing and efficiently retrieving MODIS/SEVIRI remote sensing products. The products are obtained using an automatic system that processes the data as soon as they are provided by the collecting antennas, and then the final products are uploaded with a one day delay in the geo-portal. Our focus in this work is on describing the design and efficient implementation of the geo-portal, which allows for a user-friendly and effective access to a full repository of MODIS/SEVIRI advanced products (comprising tens of terabytes of data) using geolocation retrieval capabilities. The geo-portal has been implemented as a web application composed of different layers. Its modular design provides quality of service and scalability (capacity for growth without any quality losing), allowing for the addition of components without the need to modify the entire system. On the client layer, an intuitive web browser interface provides users with remote access to the system. On the server layer, the system provides advanced data management and storage capabilities. On the storage layer, the system provides a secure massive storage service. An experimental evaluation of the geo-portal in terms of efficiency and product retrieval accuracy is also presented and discussed.
Hyperspectral imaging is concerned with the measurement, analysis, and interpretation of spectra acquired from
a given scene (or specific object) at a short, medium or long distance by an airbone or satellite sensor. Over the
last few years, hyperspectral image data sets have been collected for a great amount of locations over the world,
using a variety of instruments for Earth observation. Despite the increasing importance of hyperspectral images
in remote sensing applications, there is no common repository of hyperspectral data intended to distribute and
share hyperspectral data sets in the community. Quite opposite, the hyperspectral data sets which are available
for public use are spread among different storage locations and present significant heterogeneity regarding the
storage format, associated meta-data (if any), or ground-truth availability. As a result, the development of
a standardized hyperspectral data repository is a highly desired goal in the remote sensing community. In
this paper, we take a necessary first step towards the development of a digital repository for remotely sensed
hyperspectral data. The proposed system allows uploading new hyperspectral data sets along with meta-data,
ground-truth and analysis results, with the ultimate goal of sharing publicly available hyperspectral images within
the remote sensing community. The database has been designed in order to allow storing relevant information for
the hyperspectral data available through the system, including basic image characteristics (width, height, number
of bands, format) and more advanced meta-data (ground-truth information, publications in which the data has
been used). The current implementation consists of a front-end to ease the management of images through
a web interface, thus containing both synthetic and real hyperspectral images from two highly representative
instruments, such as NASAs Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the Cuprite Mining
District in Nevada. Most importantly, the developed system includes a spectral unmixing-based content based
image retrieval (CBIR) functionality which allows searching for images on the spectral unmixing information
(spectrally pure components or endmembers and their associated abundances in the scene). This information
is stored as meta-data associated to each hyperspectral image instance, and then used to search and retrieve
images based on information content. This paper presents the design of the system and a preliminary validation
of the unmixing-based retrieval functionality using both synthetic and real hyperspectral images stored in the