Roberto Sarmiento

University of Las Palmas de Gran Canaria | ULPGC · Instituto Universitario de Microelectrónica Aplicada (IUMA)

Hyperspectral Imaging (HSI) techniques have demonstrated potential to provide useful information in a broad set of applications in different domains, from precision agriculture to environmental science. A first step in the preparation of the algorithms to be employed outdoors starts at a laboratory level, capturing a high amount of samples to be an...

The integration of video sensors on-board satellites is becoming a trend in the space industry, since they provide extra information in the temporal domain when compared with traditional remote sensing imaging acquisition equipment. The inclusion of the temporal dimension together with the constant increase in the sensor resolution supposes a chall...

Over the last years, convolutional neural networks (CNNs) have been widely used in remote sensing applications, such as marine surveillance, traffic management, or road networks detection. However, since CNNs have extremely high computational, bandwith, and memory requirements, the hardware implementation of a CNN on space-grade devices like field-...

On-board multi- and hyperspectral instruments acquire large volumes of data that need to be processed with the limited computational and storage resources. In this context, the Consultative Committee for Space Data Systems (CCSDS) 123.0-B-2 standard emerges as an interesting option to compress multi- and hyperspectral images on-board satellites, su...

The increment in the use of high-resolution imaging sensors on-board satellites motivates the use of on-board image compression, mainly due to restrictions in terms of both hardware (computational and storage resources) and downlink bandwidth with the ground. This work presents a compression solution based on the CCSDS 123.0-B-2 near-lossless compr...

The use of next-generation and high-resolution imaging sensors is gaining interest for space missions, because of their properties for identification and exploration purposes. It is expected that the demand of video sensors in the space industry will increase during the next years, mainly for monitorization and exploration missions. In this context...

Hyperspectral images can comprise hundreds of spectral bands, which means that they can represent a large volume of data difficult to manage with the available on-board resources. Lossless compression solutions are interesting for reducing the amount of information stored or transmitted while preserving it at the same time. The Hyperspectral Lossle...

This paper describes a novel hardware implementation of a lossy multispectral and hyperspectral image compressor for on-board operation in space missions. The compression algorithm is a lossy extension of the Consultative Committee for Space Data Systems (CCSDS) 123.0-B-1 lossless standard that includes a bit-rate control stage, which in turn manag...

The on-board processing of remotely sensed hyperspectral images is gaining momentum for applications that demand a quick response as an alternative to conventional approaches where the acquired images are off-line processed once they have been transmitted to the ground segment. However, the adoption of this on-board processing strategy brings furth...

In this paper, we present the design, implementation and results of a set of IP cores that perform on-board hyperspectral image compression according to the CCSDS 123.0-B-1 lossless standard, specifically designed to be suited for on-board systems and for any kind of hyperspectral sensor. As entropy coder, the sample-adaptive entropy coder defined...

Currently, high-level synthesis (HLS) methods and tools are a highly relevant area in the strategy of several leading companies in the field of system-on-chips (SoCs) and field programmable gate arrays (FPGAs). HLS facilitates the work of system developers, who benefit from integrated and automated design workflows, considerably reducing the design...

Hyperspectral sensors are able to provide information that is useful for many different applications. However, the huge amounts of data collected by these sensors are not exempt of drawbacks, especially in remote sensing environments where the hyperspectral images are collected on-board satellites and need to be transferred to the Earths surface. I...

Currently, the use of hyperspectral imaging (HSI) for the inspection of microscopic samples is an emerging trend in different fields. The use of push-broom hyperspectral (HS) cameras against other HSI technologies is motivated by their high spectral resolution and their capabilities to exploit spectral ranges beyond 1000 nm. Nevertheless, using pus...

This paper presents the modelling, design and implementation of two IP cores that are compliant with the CCSDS 121.0-B-2 and CCSDS 123.0-B-1 lossless satellite image compression standards. The CCSDS 121.0-B-2 describes a lossless universal compressor based on a Rice adaptive encoding. The CCSDS 123.0-B-1 standard describes a lossless algorithm spec...

In recent years, anomaly detection (AD) has enjoyed a growing interest in hyperspectral data analysis. However, most state-of-the-art detectors need to work with the entire hyperspectral cube, what prevents their use for applications under real-time constraints, especially when the hyperspectral data are collected by push-broom scanners that acquir...

The increment in the data rate of the new-generation hyperspectral sensors is making more critical the necessity of lossy compression solutions able to achieve higher compression ratios (CRs). In this paper, a transform-based lossy compressor, namely lossy compression algorithm for hyperspectral image systems (HW-HyperLCA), is proposed as a modific...

Real-time hyperspectral imaging on-board compression represents a critical processing step in many remote sensing applications where the acquired hyperspectral data need to be efficiently stored and/or transferred. However, the complexity of the compression algorithms as well as the volume of data to be compressed and the limited computational reso...

The use of hyperspectral imaging for medical applications is becoming more common in recent years. One of the main obstacles that researchers find when developing hyperspectral algorithms for medical applications is the lack of specific, publicly available, hyperspectral medical data. The work described in this paper was developed within the framew...

Brain cancer surgery has the goal of performing an accurate resection of the tumor and preserving as much as possible the quality of life of the patient. There is a clinical need to develop non-invasive techniques that can provide reliable assistance for tumor resection in real-time during surgical procedures. Hyperspectral imaging (HSI) arises as...

The main goal of brain cancer surgery is to perform an accurate resection of the tumor, preserving as much normal brain tissue as possible for the patient. The development of a non-contact and label-free method to provide reliable support for tumor resection in real-time during neurosurgical procedures is a current clinical need. Hyperspectral imag...

Hyperspectral data processing is a computationally intensive task that is usually performed in high-performance computing clusters. However, in remote sensing scenarios, where communications are expensive, a compression stage is required at the edge of data acquisition before transmitting information to ground stations for further processing. Moreo...

The work presented in this paper is focused on the use of spectroscopy to identify the type of tissue of human brain samples employing support vector machine classifiers. Two different spectrometers were used to acquire infrared spectroscopic signatures in the wavenumber range between 1200–3500 cm−1. An extensive analysis was performed to find the...

In the ELTs era, where the need for versatile and innovative solutions to produce very high spatial resolution images has become a major issue, the search of synergies with other science fields seems a logic step. One of the considered alternatives to reach high-resolution images is the use of several frames of the same target, this approach is kno...

Space missions are facing disruptive innovation since the appearance of small, lightweight, and low-cost satellites (e.g., CubeSats). The use of commercial devices and their limitations in cost usually entail a decrease in available on-board computing power. To face this change, the on-board processing paradigm is advancing towards the clustering o...

In this paper, a Field Programmable Gate Array (FPGA) implementation of the CCSDS 123.0-B-1 Lossless Multispec-tral and Hyperspectral Image Compression Algorithm is presented. This recommended standard provides a tradeoff between compression performance and computational complexity , which makes it suitable for onboard applications. A High-Level Sy...

This work presents an implementation of a lossy extension of the CCSDS (Consultative Committee for Space Data Systems) 123.0-B-1 lossless compression standard, specifically thought for multispectral and hyperspectral images. This standard is intended for space applications, looking for a trade-off between its compression efficiency and the design c...

Super-resolution (SR) covers a set of techniques whose objective is to improve the spatial resolution of a video sequence or a single frame. In this scope, fusion SR techniques obtain high-resolution (HR) frames taking as a reference several low-resolution (LR) frames contained in a video sequence. This paper is based on a selective filter to decid...

The use of hyperspectral imaging (HSI) in the medical field is an emerging approach to assist physicians in diagnostic or surgical guidance tasks. However, HSI data processing involves very high computational requirements due to the huge amount of information captured by the sensors. One of the stages with higher computational load is the K-Nearest...

Dimensionality reduction represents a critical preprocessing step in order to increase the efficiency and the performance of many hyperspectral imaging algorithms. However, dimensionality reduction algorithms, such as the Principal Component Analysis (PCA), suffer fromtheir computationally demanding nature, becoming advisable for their implementati...

ENABLE-S3 is a use-case driven European research project focusing on the implementation and validation of autonomous cyber-physical systems (CPS) in different application domains. This work describes the efforts done so far in the development of infrastructure and tools to make improved validation concepts in agriculture, being part of one of the t...

Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. Howe...

Confusion matrix results of the SVM supervised classification with polynomial kernel applying the 10-fold cross validation method to each patient. (DOCX)

Confusion matrix results of the SVM supervised classification with linear kernel applying the 10-fold cross validation method to each patient. (DOCX)

Confusion matrix results of the SVM supervised classification with RBF kernel applying the 10-fold cross validation method to each patient. (DOCX)

Confusion matrix results of the SVM supervised classification with sigmoid kernel applying the 10-fold cross validation method to each patient. (DOCX)

Hyperspectral sensors are able to provide information that is useful for many different applications. However, the huge amounts of data collected by these sensors are not exempt of drawbacks, especially in remote sensing environments where the hyperspectral images are collected on-board satellites and need to be transferred to the earth’s surface....

Hyperspectral imaging (HSI) allows for the acquisition of large numbers of spectral bands throughout the electromagnetic spectrum (within and beyond the visual range) with respect to the surface of scenes captured by sensors. Using this information and a set of complex classification algorithms, it is possible to determine which material or substan...

Hyperspectral imaging (HSI) is an emerging technology for medical diagnosis. This research work presents a proof-of-concept on the use of HSI data to automatically detect human brain tumor tissue in pathological slides. The samples, consisting of hyperspectral cubes collected from 400 nm to 1000 nm, were acquired from ten different patients diagnos...

Anomaly detection (AD) is an important technique in hyperspectral data analysis that permits to distinguish rare objects with unknown spectral signatures that are particularly not abundant in a scene. In this paper, a novel algorithm for an accurate detection of anomalies in hyperspectral images with a low computational complexity, named ADALOC², i...

Hyperspectral imaging systems are a powerful tool for obtaining surface information in many different spectral channels that can be used in many different applications. Nevertheless, the huge amount of information provided by hyperspectral images also has a downside, since it has to be processed and analyzed. For such purpose, parallel hardware dev...

Image spatial resolution is critical in several fields such as medicine, communications or satellite, and underwater applications. While a large variety of techniques for image restoration and enhancement has been proposed in the literature, this paper focuses on a novel Super-Resolution fusion algorithm based on a Multi-Camera environment that per...

The HELICoiD project is a European FP7 FET Open funded project. It is an interdisciplinary work at the edge of the biomedical domain, bringing together neurosurgeons, computer scientists and electronic engineers. The main target of the project was to provide a working demonstrator of an intraoperative image-guided surgery system for real-time brain...

Introduction: Hyperspectral imaging (HSI) is an emerging technology for medical diagnosis. In this research work, a multidisciplinary team, made up of pathologists and engineers, presents a proof of concept on the use of HSI analysis in order to automatically detect human brain tumour tissue from pathological slides. The samples were acquired from...

Trying to reach diffraction-limited astronomical observations from ground-based telescopes is very challenging due to the atmospheric effects contributing to a general blurring of the images. However, astronomy is not the only science facing turbulence problems; obtaining quality images of the undersea world is as ambitious as it is on the sky. One...

In the ELTs era, where the need for versatile and innovative solutions to produce very high spatial resolution images has become a major issue, the search of synergies with other science fields seems a logic step. One of the considered alternatives to reach high-resolution images is the use of several frames of the same target, this approach is kno...

Linear spectral unmixing is one of the nowadays hottest research topics within the hyperspectral imaging community, being a proof of this fact the vast amount of papers that can be found in the scientific literature about this challenging task. A subset of these works is devoted to the acceleration of previously published unmixing algorithms for ap...

In this paper, we perform the Electronic System Level (ESL) modelling and verification of two lossless compression standard algorithms for space applications using the SystemC language. In particular we present the architectures and a description in SystemC of the CCSDS-121 universal lossless compressor and the CCSDS-123 lossless compressor for hyp...

Remote sensing systems equipped with multispectral and hyperspectral sensors are able to capture images of the surface of the Earth at different wavelengths. In these systems, hyperspectral sensors typically provide images with a high spectral resolution but a reduced spatial resolution, while on the contrary, multispectral sensors are able to prod...

Hyperspectral images allow obtaining large amounts of information about the surface of the scene that is captured by the sensor. Using this information and a set of complex classification algorithms is possible to determine which material or substance is located in each pixel. The HELICoiD (HypErspectraL Imaging Cancer Detection) project is a Europ...

Hyperspectral Imaging is an emerging technology for medical diagnosis issues due to the fact that it is a non-contact, non-ionizing and non-invasive sensing technique. The work presented in this paper tries to establish a novel way in the use of hyperspectral images to help neurosurgeons to accurately determine the tumour boundaries in the process...

Linear spectral unmixing is nowadays an essential tool to analyze remotely sensed hyperspectral images. Although many different contributions have been uncovered during the last two decades, the majority of them are based on dividing the whole process of linearly unmixing a given hyperspectral image into three sequential steps: 1) estimation of the...

Hyperspectral imaging is an active research field for remote sensing applications. These images provide a lot of information about the characteristics of the materials due to the high spectral resolution. This work is focused in the use of this kind of information to detect tumour tissue, particularly brain cancer tissue. In recent years, the study...

One of the first problems that a hardware designer needs to solve when facing a new and complex electronic design, is to know in advance where the critical parts of the design are, and how many resources the design will require. This information will ease the developing of feasible systems and will help in the design of well suited architectures. T...

Linear unmixing of hyperspectral images has rapidly become one of the most widely utilized tools for analyzing the content of hyperspectral images captured by state-of-the-art remote hyperspectral sensors. The aforementioned unmixing process consists of the following three sequential steps: dimensionality estimation, endmember extraction and abunda...

Spectral unmixing is an important task for remotely sensed hyperspectral data exploitation. It amounts the identification of pure spectral signatures (endmembers) in the data, and the estimation of the abundance of each endmember in each (possibly mixed) pixel. A challenging problem in spectral unmixing is how to determine the number of endmembers...

It is highly desirable to know in advance the transaction of data in the design of any electronic embedded system. It is of especial interest for data-intensive applications, such as complex video systems, when the options available in the video decoder continuously change and/or the features of the input video sequences are different. This paper e...