José M. P. Nascimento's research while affiliated with Instituto Superior Técnico and other places
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Publications (55)
Wildfire early detection and prevention had become a priority. Detection using Internet of Things (IoT) sensors, however, is expensive in practical situations. The majority of present wildfire detection research focuses on segmentation and detection. The developed machine learning models deploy appropriate image processing techniques to enhance the...
Fire detection and prevention had become a high priority task, in the last decade, due to the higher number of forest fires. Automatic detection systems facilitate the intervention and reduce the cost of firefighters travel in case of false occurrences. Deep learning based systems has drawn promising high results in the field, in particular, Deepla...
In the last decade, the number of forest fires events is growing due to the fast change of earth’s climate. Hence, more automatized fire fighting actions had become necessary. Deep learning had drawn interesting results for pixel level classification for smoke detection, but few systems are proposed for fire flame detection. In this paper, a semant...
Advances in hyperspectral sensors have led to a significantly increased capability for high-quality data. This trend calls for the development of new techniques to enhance the way that such unprecedented volumes of data are stored, processed, and transmitted to the ground station. An important approach to deal with massive volumes of information is...
One of the main problems of hyperspectral data analysis is the presence of mixed pixels due to the low spatial resolution of such images. Linear spectral unmixing aims at inferring pure spectral signatures and their fractions at each pixel of the scene. The huge data volumes acquired by hyperspectral sensors put stringent requirements on processing...
Hyperspectral instruments have been incorporated in satellite missions, providing large amounts of data of high spectral resolution of the Earth surface. This data can be used in remote sensing applications that often require a real-time or near-real-time response. To avoid delays between hyperspectral image acquisition and its interpretation, the...
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 comprom...
Hyperspectral instruments have been incorporated in satellite missions, providing data of high spectral resolution of the Earth. This data can be used in remote sensing applications, such as, target detection, hazard prevention, and monitoring oil spills, among others. In most of these applications, one of the requirements of paramount importance i...
This letter presents a new parallel method for hyperspectral unmixing composed by the efficient combination of two popular methods: vertex component analysis (VCA) and sparse unmixing by variable splitting and augmented Lagrangian (SUNSAL). First, VCA extracts the endmember signatures, and then, SUNSAL is used to estimate the abundance fractions. B...
Parallel hyperspectral unmixing problem is considered in this paper. A semisupervised approach is developed under the linear mixture model, where the abundance’s physical constraints are taken into account. The proposed approach relies on the increasing availability of spectral libraries of materials measured on the ground instead of resorting to e...
In this paper, a new parallel method for sparse spectral unmixing of remotely sensed hyperspectral data on commodity graphics processing units (GPUs) is presented. A semi-supervised approach is adopted, which relies on the increasing availability of spectral libraries of materials measured on the ground instead of resorting to endmember extraction...
In hyperspectral imagery a pixel typically consists mixture of spectral signatures of reference substances, also called endmembers. Linear spectral mixture analysis, or linear unmixing, aims at estimating the number of endmembers, their spectral signatures, and their abundance fractions.
This paper proposes a framework for hyperpsectral unmixing. A...
Vertex component analysis (VCA) has become a very popular and useful tool to linear unmix large hyperspectral datasets without the use of any a priori knowledge of the constituent spectra. Although VCA is fast method, many hyperspectral imagery applications require a response in real time or near-real time. This paper proposes two different optimiz...
Endmember extraction (EE) is a fundamental and crucial task in hyperspectral unmixing. Among other methods vertex component analysis (VCA) has become a very popular and useful tool to unmix hyperspectral data. VCA is a geometrical based method that extracts endmember signatures from large hyperspectral datasets without the use of any a priori knowl...
This paper introduces a new unsupervised hyperspectral unmixing method conceived to linear but highly mixed hyperspectral data sets, in which the simplex of minimum volume, usually estimated by the purely geometrically based algorithms, is far way from the true simplex associated with the endmembers. The proposed method, an extension of our previou...
This paper addresses the unmixing of hyperspectral images, when intimate
mixtures are present. In these scenarios the light suffers multiple
interactions among distinct endmembers, which is not accounted for by
the linear mixing model. A two-step method to unmix hyperspectral
intimate mixtures is proposed: first, based on the Hapke intimate
mixture...
This paper is an elaboration of the DECA algorithm to blindly unmix hyperspectral data. The underlying mixing model is linear, meaning that each pixel is a linear mixture of the endmembers signatures weighted by the correspondent abundance fractions. The proposed method, as DECA, is tailored to highly mixed mixtures in which the geometric based app...
This paper addresses the problem of unmixing hyperspectral images, when the light suffers multiple interactions among distinct endmembers. In these scenarios, linear unmixing has poor accuracy since the multiple light scattering effects are not accounted for by the linear mixture model. Herein, a nonlinear scenario composed by a single layer of veg...
Hyperspectral sensors are being developed for remote sensing applications. These sensors produce huge data volumes which require faster processing and analysis tools. Vertex component analysis (VCA) has become a very useful tool to unmix hyperspectral data. It has been successfully used to determine endmembers and unmix large hyperspectral data set...
Signal subspace identification is a crucial first step in many hyperspectral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction, yielding gains in algorithm performance and complexity and in data storage. This paper introduces...
Hyperspectral unmixing methods aim at the decomposition of a hyperspectral image into a collection endmember signatures, i.e., the radiance or reflectance of the materials present in the scene, and the correspondent abundance fractions at each pixel in the image. This paper introduces a new unmixing method termed dependent component analysis (DECA)...
This paper introduces a new method to blindly unmix hyperspectral data, termed dependent component analysis (DECA). This method decomposes a hyperspectral images into a collection of reflectance (or radiance) spectra of the materials present in the scene (end member signatures) and the corresponding abundance fractions at each pixel. DECA assumes t...
Given an hyperspectral image, the determination of the number of end members and the subspace where they live without any prior knowledge is crucial to the success of hyperspectral image analysis. This paper introduces a new minimum mean squared error based approach to infer the signal subspace in hyperspectral imagery. The method, termed hyperspec...
Linear unmixing decomposes a hyperspectral image into a collection of reflectance spectra of the materials present in the scene, called endmember signatures, and the corresponding abundance fractions at each pixel in a spatial area of interest.
This paper introduces a new unmixing method, called Dependent Component Analysis (DECA), which overcomes...
ABSTRACT Dimensionality reduction plays a crucial role in many,hyperspectral data processing and analysis algorithms. This paper proposes a new mean squared error based approach to determine the signal subspace in hyperspectral imagery. The method flrst estimates the signal and noise correlations matrices, then it selects the subset of eigenvalues...
Hyperspectral applications in remote sensing are often focused on determining the so-called spectral signatures, i.e., the
reflectances of materials present in the scene (endmembers) and the corresponding abundance fractions at each pixel in a spatial
area of interest. The determination of the number of endmembers in a scene without any prior knowl...
Given a set of mixed spectral (multispectral or hyperspectral) vectors, linear spectral mixture analysis, or linear unmixing, aims at estimating the number of reference substances, also called endmembers, their spectral signatures, and their abundance fractions. This paper presents a new method for unsupervised endmember extraction from hyperspectr...
Independent component analysis (ICA) has recently been proposed as a tool to unmix hyperspectral data. ICA is founded on two assumptions: 1) the observed spectrum vector is a linear mixture of the constituent spectra (endmember spectra) weighted by the correspondent abundance fractions (sources); 2)sources are statistically independent. Independent...
Given a set of mixed spectral (multispectral or hy- perspectral) vectors, linear spectral mixture analysis, or linear unmixing, aims at estimating the number of reference substances, also called endmembers, their spectral signatures, and their abundance fractions. This paper presents a new method for unsupervised endmember extraction from hyperspec...
Linear unmixing decomposes an hyperspectral image into a collection of reflectance spectra, called endmember signatures, and a set corresponding abundance fractions from the respective spatial coverage. This paper introduces vertex component analysis , an unsupervised algorithm to unmix linear mixtures of hyperpsectral data. VCA exploits the fact t...
One of the most challenging task underlying many hyperspectral imagery applications is the spectral unmixing, which decomposes a mixed pixel into a collection of reflectance spectra, called endmember signatures, and their corresponding fractional abundances. Independent Component Analysis (ICA) have recently been proposed as a tool to unmix hypersp...
Linear unmixing decomposes an hyperspectral image into a collection of reflectance spectra, called endmember signatures, and a set corresponding abundance fractions from the respective spatial coverage. This paper introduces vertex component analysis, an unsupervised algorithm to unmix linear mixtures of hyperpsectral data. VCA exploits the fact th...
Independent Component Analysis (ICA) have recently been proposed as a tool to unmix hyperspectral data. ICA is founded on
two assumptions: i) The observed data vector is a linear mixture of the sources (abundance fractions); ii) sources are independent.
Concerning hyperspectral data, the first assumption is valid whenever the constituent substances...
Linear spectral mixture analysis, or linear unmixing, has proven to be a useful tool in hyperspectral remote sensing applications.
It aims at estimating the number of reference substances, also called endmembers, their spectral signature and abundance fractions,
using only the observed data (mixed pixels). This paper presents new method that perfor...
Citations
... Introduced by Google researchers in 2018 [29], DeepLabv3+ is a state-of-the-art CNN model specifically designed for semantic segmentation tasks and represents the latest upgraded version of the DeepLab series models. It has achieved remarkable performances and found wide application in the domain of semantic segmentation [30][31][32][33]. As depicted in Figure 3, DeepLabv3+ comprises two primary components, an encoder and decoder [34]. ...
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... This localization can be used for georeferencing the events from aerial images. Deeplabv3+ network and Squeeze-net have been employed in [11,23], respectively, to segment the fire regions. Similar methods based on CNNs have also been proposed for smoke segmentation. ...
... The obtained results (accuracy of 97.67%) are very promising and demonstrated the efficiency of DeepLab v3+ in forest fire segmenting tasks. Harkat et al. [130] also applied DeepLab v3+ with an Xception model as a backbone to detect fire on RGB and Infrared (IR) images. Three loss functions (Dice, Cross entropy, and Tversky loss), three learning rates (10 −1 , 10 −2 , and 10 −3 ), and the CorsicanFire dataset (1135 RGB images and 640 IR images) were used to train and evaluate this model. ...
... With its third version, called DeepLabv3+ (Chen et al., 2018), the DeepLab architecture reached the DNN state-of-the-art for semantic segmentation. It achieved impressive results on many benchmark datasets and in various research fields (Harkat et al., 2020;Wang and Liu, 2021;Wu et al., 2021;Kong et al., 2021;Gruosso et al., 2021aGruosso et al., , 2023 surpassing among others, the previously mentioned approaches. ...
... This paper is a prospective study that brings out ways to accelerate the hyperspectral reconstruction method and determine its performance on an embedded device. This is original in the field of HS imaging in embedded systems, since most of the literature focuses on either HS data compression [17,18] or unmixing [19,20]. Our analysis presents the computational complexity of the method but also includes an estimation of the memory footprint and bandwidth, which are often overlooked in the literature but are crucial when designing a device for real use. ...
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... The need for nonlinear methods emerges from real-world situations such as a scene having complex vegetated surfaces with multiple light scattering. 7 Many methods have been suggested for nonlinear unmixing, such as kernel methods, neural networks, support vector machine (SVM), etc. A detailed review of frequently used methods was published by Heylen et al. 8 ...
... The performance, power and energy consumption of the proposed SoC architecture was compared to other embedded computing platforms, namely the embedded GPU of the Jetson TX2 platform [44] and the ARM processor of the ZYNQ7020 SoC FPGA, in the execution of the compressive sensing algorithm. ...
... There has been a lot of research on the theory of compressive sensing owing to its possible usefulness in retrieving images and signals from their compressed equivalents [96,97]. It has been shown that high-spatialresolution hyperspectral images can be recovered from hyperspectral and LRM images using the principles of compressive sensing [98][99][100][101][102]. Under the premise of sparsity, it creates a powerful and efficient algorithm for solving the associated models [103]. ...
... Other studies [40][41][42][43][44][45][46][47] use the Spectral Angle Difference (SAD) while the Signal-to-Noise Ratio (SNR) is omnipresent in [44,[48][49][50][51]. Further, the Peak Signal-to-Noise Ratio (PSNR) is adopted in [49,50,[52][53][54][55][56][57][58][59][60][61]. PSNR is the most frequently used quality metric, yet it is more content specific. ...
... This is a drawback since the degradation of the image quality is not caused by an external factor but by the model itself [62]. Also, the Mean Square Error (MSE) and Root MSE (RMSE) metrics are employed in [46,49,51,55,[63][64][65][66] while the Normalized MSE (NMSE) is used in [56,[67][68][69]. The use of normalization facilitates comparison between different datasets. ...
