The decomposition of the observed pixel spectrum of hyperspectral data into its constituent spectral signatures (or endmembers) and a set of corresponding fractional abundances is known as hyperspectral unmixing. To carry out this analysis, it is required to use a computational technology with high-performance computing due to the high dimensionality of the data and the high complexity of the unmixing algorithms. Field Programmable Gate Arrays (FPGAs) are excellently suited to processing needs since they provide flexibility, low power consumption, and great performance. Faster alternative development methods are needed because the rate at which new algorithms for hyperspectral analysis are developed is substantially higher than the rate at which these algorithms are implemented on FPGAs. High-level synthesis (HLS) is a technology that assists with the transformation of a behavioral description of hardware into a register transfer level (RTL) model, thus reducing development times. In this work, the automatic target classification and detection algorithm (ATDCA) has been accelerated on FPGAs using Dataparalell C++ (DPC++) and Intel oneAPI. The analysis of the algorithm and the three accelerations carried out achieve a speedup above 13×.

A new open-source software, called SunMap, has been developed to obtain synoptic maps in an easy and quick way from multiple full-disc solar images. Our objective is to provide a free and straightforward application for heliophysicists and geophysicists interested in generating solar synoptic maps. SunMap allows comparison of structures and patterns of solar activity over various periods. Thus, the short- and long-term evolution of solar regions of interest can be studied. To reach this goal, different solar images taken day by day are stored in a single map that uses a sequence of images and allows the positioning of each observable element on it. A simple comparison between a synoptic map generated by SunMap and another previously constructed map is presented to show the versatility of this new available software.

In hyperspectral image (HSI) analysis, one of the most important tasks is target detection, requiring the execution of algorithms with high computational complexity. Recently, research efforts have focused on on-board real-time target detection to provide timely responses for swift decisions. Therefore, it is necessary to use a technology that provides the performance needed for real-time target detection, and at the same time meets the satellite payload requirements. Field-programmable gate arrays (FPGAs) have very interesting properties in terms of performance, size, and power consumption, which have become the standard option for on-board processing. In this letter, we present a hardware optimized implementation for FPGAs of the automatic target detection and classification algorithm (ATDCA) using the Gram–Schmidt (GS) method for orthogonalization purposes. The ATDCA-GS algorithm is directly coded using VHDL and verified on a Virtex-7 XC7VX690T FPGA using real hyperspectral data [collected by Hyperspectral Digital Imagery Collection Experiment (HYDICE) sensor and by NASA’s Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)] and a synthetic image. Experimental results demonstrate that our hardware version of the ATDCA-GS algorithm outperforms previous implementations (multicore processors, GPUs, and accelerators) in both computation time (obtaining real-time performance) and power consumption, demonstrating the suitability of FPGAs for this purpose.

Accurate and effective rapid detection in remote sensing images plays an extremely important role in natural disasters, landslides, flooding problems, and military defense. Specially in earthquake damage detection, time critical tasks, such as performing the damage assessment or providing immediate delivery of relief assistance, require responses for swift decisions. To minimize response time, this article proposes the portability and acceleration of the inferences process on a deep convolution neural network model to detect rapid earthquake damage on very high resolution (VHR) remote sensing data using the Intel OpenVINO toolkit. This model, based on a previous work, has been optimized for high performance computing techniques to minimize processing times. Along with the immediate responses, the correctness of the result is also very crucial parameter for such type of applications. For analysis and performance, we use the GeoEye-1 VHR disaster images of the Haiti earthquake occurred in year 2010. Experimental results show that the optimized model provides good accuracy for damage detection with significant execution speed on CPU+GPU using mixed precision technique compared to other previous work. Moreover, OpenVINO toolkit presents satisfactory performance in the inference stage compared to other toolkits, such as TensorFlow serving, using asynchronous inference mode executed on an Intel Xeon processor and Intel Movidius NCS as coprocessor.

There is an emerging interest in using hyperspectral data for land cover classification. The motivation behind using hyperspectral data is the notion that increasing the number of narrowband spectral channels would provide richer spectral information and thus help improve the land cover classification performance. Although hyperspectral data with hundreds of channels provide detailed spectral signatures, the curse of dimensionality might lead to degradation in the land cover classification performance. Moreover, in some practical applications, hyperspectral data may not be available due to cost, data storage, or bandwidth issues, and RGB and near infrared (NIR) could be the only image bands available for land cover classification. Light detection and ranging (LiDAR) data is another type of data to assist land cover classification especially if the land covers of interest have different heights. In this paper, we examined the performance of two Convolutional Neural Network (CNN)-based deep learning algorithms for land cover classification using only four bands (RGB+NIR) and five bands (RGB+NIR+LiDAR), where these limited number of image bands were augmented using Extended Multi-attribute Profiles (EMAP). The deep learning algorithms were applied to a well-known dataset used in the 2013 IEEE Geoscience and Remote Sensing Society (GRSS) Data Fusion Contest. With EMAP augmentation, the two deep learning algorithms were observed to achieve better land cover classification performance using only four bands as compared to that using all 144 hyperspectral bands.

Hyperspectral (HS) data have found a wide range of applications in recent years. Researchers observed that more spectral information helps land cover classification performance in many cases. However, in some practical applications, HS data may not be available, due to cost, data storage, or bandwidth issues. Instead, users may only have RGB and near infrared (NIR) bands available for land cover classification. Sometimes, light detection and ranging (LiDAR) data may also be available to assist land cover classification. A natural research problem is to investigate how well land cover classification can be achieved under the aforementioned data constraints. In this paper, we investigate the performance of land cover classification while only using four bands (RGB+NIR) or five bands (RGB+NIR+LiDAR). A number of algorithms have been applied to a well-known dataset (2013 IEEE Geoscience and Remote Sensing Society Data Fusion Contest). One key observation is that some algorithms can achieve better land cover classification performance by using only four bands as compared to that of using all 144 bands in the original hyperspectral data with the help of synthetic bands generated by Extended Multi-attribute Profiles (EMAP). Moreover, LiDAR data do improve the land cover classification performance even further.

Accurate vegetation detection is important for many applications, such as crop yield estimation, land cover land use monitoring, urban growth monitoring, drought monitoring, etc. Popular conventional approaches to vegetation detection incorporate the normalized difference vegetation index (NDVI), which uses the red and near infrared (NIR) bands, and enhanced vegetation index (EVI), which uses red, NIR, and the blue bands. Although NDVI and EVI are efficient, their accuracies still have room for further improvement. In this paper, we propose a new approach to vegetation detection based on land cover classification. That is, we first perform an accurate classification of 15 or more land cover types. The land covers such as grass, shrub, and trees are then grouped into vegetation and other land cover types such as roads, buildings, etc. are grouped into non-vegetation. Similar to NDVI and EVI, only RGB and NIR bands are needed in our proposed approach. If Laser imaging, Detection, and Ranging (LiDAR) data are available, our approach can also incorporate LiDAR in the detection process. Results using a well-known dataset demonstrated that the proposed approach is feasible and achieves more accurate vegetation detection than both NDVI and EVI. In particular, a Support Vector Machine (SVM) approach performed 6% better than NDVI and 50% better than EVI in terms of overall accuracy (OA).

In the last decades, the problem of target detection has received considerable attention in remote sensing applications. When this problem is tackled using hyperspectral images with hundreds of bands, the use of high-performance computing (HPC) is essential. One of the most popular algorithms in the hyperspectral image analysis community for this purpose is the automatic target detection and classification algorithm (ATDCA). Previous research has already investigated the mapping of ATDCA on HPC platforms such as multicore processors, graphics processing units (GPUs), and field-programmable gate arrays (FPGAs), showing impressive speedup factors (after careful fine-tuning) that allow for its exploitation in time-critical scenarios. However, the lack of standardization resulted in most implementations being too specific to a given architecture, eliminating (or at least making extremely difficult) code reusability across different platforms. In order to address this issue, we present a portability study of an implementation of ATDCA developed using the open computing language (OpenCL). We focus on cross-platform parameters such as performance, energy consumption, and code design complexity, as compared to previously developed (hand-tuned) implementations. Our portability study analyzes different strategies to expose data parallelism as well as enable the efficient exploitation of complex memory hierarchies in heterogeneous devices. We also conduct an assessment of energy consumption and discuss metrics to analyze the quality of our code. The conducted experimentsusing synthetic and real hyperspectral data sets collected by the Hyperspectral Digital Imagery Collection Experiment (HYDICE) and NASAs Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS)demonstrate, for the first time in the literature, that portability across different HPC platforms can be achieved for real-time target detection in hyperspectral missions.

Soil can be used as a damage indicator of landslides and flooding, which expose soil from vegetation canopy. It can also be used as an indirect indicator of illegal tunnel digging activity. This letter presents a sparsity-based approach to soil detection using multispectral satellite images, where both original and synthetic bands have been used. Spatial and spectral information has then been jointly used in soil detection. Extensive experiments clearly demonstrated the feasibility of our approach.

The probabilistic Latent Semantic Analysis (pLSA) model has recently shown a great potential to uncover highly descriptive semantic features from limited amounts of remote sensing data. Nonetheless, the high computational cost of this algorithm often constraints its operational application for land cover categorization tasks. In this scenario, this paper presents an Open Multi-Processing (OpenMP) implementation of the pLSA algorithm for unsupervised Synthetic Aperture Radar (SAR) and Multi-Spectral Imaging (MSI) image categorization. The experimental results suggest that multi-core systems are an important architecture for the efficient processing of both SAR and MSI datasets. Specifically, the proposed approach is able to cover a real scenario exhibiting good results in both accuracy and performance terms.

We present detection performance of ten change detection algorithms with and without the use of Extended Multi-Attribute Profiles (EMAPs). Heterogeneous image pairs (also known as multimodal image pairs), which are acquired by different imagers, are used as the pre-event and post-event images in the investigations. The objective of this work is to examine if the use of EMAP, which generates synthetic bands, can improve the detection performances of these change detection algorithms. Extensive experiments using five heterogeneous image pairs and ten change detection algorithms were carried out. It was observed that in 34 out of 50 cases, change detection performance was improved with EMAP. A consistent detection performance boost in all five datasets was observed with EMAP for Homogeneous Pixel Transformation (HPT), Chronochrome (CC), and Covariance Equalization (CE) change detection algorithms.

Convolutional neural networks have emerged as an excellent tool for remotely sensed hyperspectral image (HSI) classification. Nonetheless, the high computational complexity and energy requirements of these models typically limit their application in on-board remote sensing scenarios. In this context, low-power consumption architectures are promising platforms that may provide acceptable on-board computing capabilities to achieve satisfactory classification results with reduced energy demand. For instance, the new NVIDIA Jetson Tegra TX2 device is an efficient solution for on-board processing applications using deep-learning (DL) approaches. So far, very few efforts have been devoted to exploiting this or other similar computing platforms in on-board remote sensing procedures. This letter explores the use of low-power consumption architectures and DL algorithms for HSI classification. The conducted experimental study reveals that the NVIDIA Jetson Tegra TX2 device offers a good choice in terms of performance, cost, and energy consumption for on-board HSI classification tasks.

Solving the mixture problem in remotely sensed hyperspectral images remains a challenging task. In particular, solutions are needed in order to obtain a response for applications with real-time constraints. In the last decade, several efforts have been developed, many of them using graphics processing units (GPUs) and focused on the exploitation of spectral information alone. However, a few spectral unmixing chains have been developed using other architectures such as multicore processors, field programmable gate arrays, or Intel Xeon Phi coprocessors. In this letter, we develop a new parallel unmixing chain for multicore processors. Compared with other approaches, the proposed spatial-spectral alternative takes advantage of the complementary information provided by the spatial correlation of the pixels in the image in addition to the spectral information. Our implementation has been optimized using the application program interface OpenMP and the Intel Math Kernel Library on two multicore architectures, and using real analysis scenarios. The results reveal competitive real-time performance compared with another compute unified device architecture implementation previously developed for GPUs.

Spectral unmixing is an important task for remotely sensed hyperspectral data exploitation. Due to the fact that the spatial resolution of the sensor may not be able to separate different spectrally pure components (endmembers), spectral unmixing faces important challenges in order to characterize mixed pixels. As a result, several hyperspectral unmixing chains have been proposed to find the spectral signatures for each endmember and their associated abundance fractions. However, unmixing algorithms can be computationally expensive, which compromises their use in applications under real-time constraints. In this paper, we describe a new parallel hyperspectral unmixing chain based on three stages: 1) estimation of the number of endmembers using the geometry-based estimation of number of endmembers algorithm; 2) automatic identification of the spectral signatures of the endmembers using the simplex growing algorithm; and 3) estimation of the fractional abundance of each endmember in each pixel of the scene using the sum-to-one constrained least-squares unmixing algorithm. These algorithms have been specifically selected due to their successful performance in different applications. We have developed new parallel implementations of the aforementioned algorithms and assembled them in a fully operative unmixing chain using an hybrid implementation with the OpenCL framework and clMAGMA library. As a result, this is one of the first real-time implementations of a full unmixing chain in an open computing language. This methodology can be executed on different heterogeneous platforms such as CPU (multicore) and GPU platforms, in which accuracy, performance, and power consumption terms have been considered.

Over the last few years, several new strategies for spectral unmixing of remotely sensed hyperspectral data have been proposed. Many of them have been developed to solve the most time-consuming and relevant step: endmember extraction. However, unmixing algorithms can be computationally very expensive in terms of processing time and energy consumption, a fact that compromises their use in applications under real-time and energy/power constraints. In this letter, we present a new parallel simplex growing algorithm (SGA) for hyperspectral data which exploits the memory hierarchy with operations in single-precision floating point. Those optimizations accelerate the most time-consuming parts of this method using the open computing language (OpenCL) standard. We have evaluated the performance versus energy consumption using the same open standard for parallel programming over a diverse set of heterogeneous platforms. Experiments have been conducted using real hyperspectral images collected by NASA's Airborne Visible Infrared Imaging Spectrometer and a collection of 24 synthetic hyperspectral images simulated with different sizes and number of endmembers (10-30). Considering the power consumption and OpenCL across all the proposed devices, the analysis presented indicates that the SGA can now be executed in computationally efficient fashion, which was not possible before introducing the parallel implementation described in this letter.

The integration of spatial information into spectral unmixing process has attracted much attention in recent years. Several approaches have been developed to incorporate spatial considerations into the endmember extraction/estimation procedure. Spatial preprocessing algorithms are one of the most commonly adopted techniques to guide endmember identification algorithms in terms of the spatial characteristics of the hyperspectral data. Particularly, spatial preprocessing algorithm (SPP) consists on a preprocessing technique that can be used prior to most of existing spectral-based endmember extraction process, thus promoting the selection of endmembers from the most spatially homogeneous regions of the data set. This paper presents a parallel implementation of SPP algorithm which is tested over two different graphic processing units (GPUs) architectures: NVidiaTMGeForce GTX 580 and NVidiaTMGeForce GTX 870M. Experimental validation using a hyperspectral data set collected by AVIRIS sensor shows that it is possible to achieve real-time performance.

Timely detection of targets continues to be a relevant challenge for hyperspectral remote sensing capability. The automatic target-generation process using an orthogonal projection operator (ATGP-OSP) has been widely used for this purpose. Hyperspectral target-detection applications require timely responses for swift decisions, which depend upon (near) real-time performance of algorithm analysis. Reconfigurable field-programmable gate arrays (FPGAs) are promising platforms that allow hardware/software codesign and the potential to provide powerful onboard computing capabilities and flexibility at the same time. In this paper, we present an FPGA implementation for the ATGP-OSP algorithm. Our system includes a direct memory access module and implements a prefetching technique to hide the latency of the input/output communications. The proposed method has been implemented on a Virtex-7 XC7VX690T FPGA and tested using real hyperspectral data collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the Cuprite mining district in Nevada and the World Trade Center in New York. Experimental results demonstrate that our hardware version of the ATGP-OSP algorithm can significantly outperform a software version, which makes our reconfigurable system appealing for onboard hyperspectral data processing.

The application of compressive sensing (CS) to hyperspectral images is an active area of research over the past few years, both in terms of the hardware and the signal processing algorithms. However, CS algorithms can be computationally very expensive due to the extremely large volumes of data collected by imaging spectrometers, a fact that compromises their use in applications under real-time constraints. This paper proposes four efficient implementations of hyperspectral coded aperture (HYCA) for CS, two of them termed P-HYCA and P-HYCA-FAST and two additional implementations for its constrained version (CHYCA), termed P-CHYCA and P-CHYCA-FAST on commodity graphics processing units (GPUs). HYCA algorithm exploits the high correlation existing among the spectral bands of the hyperspectral data sets and the generally low number of endmembers needed to explain the data, which largely reduces the number of measurements necessary to correctly reconstruct the original data. The proposed P-HYCA and P-CHYCA implementations have been developed using the compute unified device architecture (CUDA) and the cuFFT library. Moreover, this library has been replaced by a fast iterative method in the P-HYCA-FAST and P-CHYCA-FAST implementations that leads to very significant speedup factors in order to achieve real-time requirements. The proposed algorithms are evaluated not only in terms of reconstruction error for different compressions ratios but also in terms of computational performance using two different GPU architectures by NVIDIA: 1) GeForce GTX 590; and 2) GeForce GTX TITAN. Experiments are conducted using both simulated and real data revealing considerable acceleration factors and obtaining good results in the task of compressing remotely sensed hyperspectral data sets.

In the last years, hyperspectral analysis have been applied in many remote sensing applications. In fact, hyper spectral unmixing has been a challenging task in hyperspectral data exploitation. This process consists of three stages: (i) estimation of the number of pure spectral signatures or endmembers, (ii) automatic identification of the estimated endmembers, and (iii) estimation of the fractional abundance of each endmember in each pixel of the scene. However, unmixing algorithms can be computationally very expensive, a fact that compromises their use in applications under real-time constraints. In recent years, several techniques have been proposed to solve the aforementioned problem but until now, most works have focused on the second and third stages. The execution cost of the first stage is usually lower than the other stages. Indeed, it can be optional if we known a priori this estimation. However, its acceleration on parallel architectures is still an interesting and open problem. In this paper we have addressed this issue focusing on the GENE algorithm, a promising geometry-based proposal introduced in.(1) We have evaluated our parallel implementation in terms of both accuracy and computational performance through Monte Carlo simulations for real and synthetic data experiments. Performance results on a modern GPU shows satisfactory 16x speedup factors, which allow us to expect that this method could meet real-time requirements on a fully operational unmixing chain.