Current sensor-gateway-cloud solutions for machine learning (ML) life cycle development require extensive coding, hardware, and software expertise. We present ST AIoT Craft, the first no-code/low-code online platform for training, deploying, and managing distributed artificial intelligence of things (AIoT) and big data. The framework provides user-interface dashboards and web applications for sensor data logging and management, dataset preprocessing and visualization, automatic tinyML for in-sensor processors and microcontrollers, and end-to-end Internet of things (IoT) system setup and monitoring. The tools allow developers to manage intelligent IoT devices, gateways, and cloud infrastructure directly from a web browser. ST AIoT Craft is accessible at https://staiotcraft.st.com/.

In this paper, a numerical method optimizer based on covariance matching is proposed for synthetic aperture radar (SAR) polarimetric calibration. The method makes use of the information provided by a distributed target and a corner reflector in order to jointly estimate the system polarimetric distortion parameters and the Faraday rotation. A preliminary analysis is conducted to show the expected accuracy values and to identify the intrinsic ambiguities of the problem. Results from simulations are shown to assess the accuracy and convergence of the method. Finally, tests have been conducted on stack of repeated full polarimetric ALOS PALSAR images to check the stability of the retrieved distortion parameters in a realistic case.

In this work, a numerical method optimizer for SAR polarimetric calibration is proposed. The method makes use of the information provided by a Distributed Target and a Corner Reflector in order to jointly estimate the system polarimetric distortion parameters and the Faraday Rotation. The different order of magnitude of cross-talks and Faraday Rotation is exploited to overcome the intrinsic ambiguity related to the problem. Tests have been conducted both on simulated data, confirming the ability of the proposed method to provide realistic estimation of the distortion parameters, even in case of low quality Corner Reflector.

In this paper we propose a new approach for the improvement of the spatial resolution of hyperspectral image classification maps combining both spectral unmixing and pansharpening approaches. The main idea is to use a spectral unmixing algorithm based on neural networks to retrieve the abundances of the endmembers present in the scene, and then use the spatial information retrieved from the pansharpened image to find the location of each endmember within the enhanced pixel according to the endmembers abundances. The proposed approach has been applied both to real and synthetic datasets.

A refinement technique of the antenna model considered to compute Synthetic Aperture Radar (SAR) data pattern is proposed in this work. The accurate knowledge of the antenna patterns of a SAR sensor is of main importance for precise SAR image processing. This requirement, coupled with tight needs for a short duration of the calibration process, have made necessary the investigation of innovative methods to calibrate complex SAR systems. When considering active arrays, the antenna patterns computed from homogeneous SAR data is often in disagreement with those obtained from a mathematical model (the antenna model). In this paper a correction factor is introduced within the antenna model, in order to overcome this discrepancy. This parameter is expressed as a corrected version of the matrix indicating the working status of the antenna. It is estimated through inversion by exploiting the patten information of SAR data, and it can be used for effective antenna model calibration. The effectiveness of the proposed method is assessed by considering ENVISAT ASAR rain forest-images.

A kernel adapted to the spectral dimension of hyperspectral images is proposed in this paper. A distance based on a statistical cluster model is used to construct a radial kernel. This class specific kernel realizes a compromise between a conventional Gaussian kernel and a Gaussian kernel on the first principal components of the considered class. An automatic gradient optimization is used to select the optimal hyperparameters. Experimental results on a real hyperspectral image show the kernel is effective compared to the conventional Gaussian kernel. Furthermoren the proposed kernel is less sensitive to one hyperparameter compared to the Gaussian kernel applied on the first principal components of the data.

Low spatial resolution is a major limitation for remote sensing classification, especially in a urban environment. In this work, we will focus on the simulation of urban area environment at a low spatial resolution, comparable to the new hyperspectral sensors that will be launched in the next few years. The aim is to better understand the possibility offered by the new sensors, in a challenging scenario like the one represented by a highly mixed image. Particular attention is placed on the characteristics of the sensor EnMap, produced by DLR. The experiments conducted on a real data set confirm the challenges posed by low spatial resolution when analyzing a urban environment.

The unsupervised classification of hyperspectral images containing mixed pixels is addressed in this paper. Hyperspectral images are characterized by a trade-off between the spectral and the spatial resolution, this leading to data sets containing mixed pixels, e.g. pixels jointly occupied by more than a single land cover class. In [1], a preliminary research based on spectral unmixing concepts was conducted, in order to handle mixed pixels and to obtain thematic maps at a finer spatial resolution. In this work, we extend the investigation by proposing a new methodology based on image clustering. Experiments conducted on real data show the comparative effectiveness of the proposed method, which provides good results in terms of accuracy and is less sensitive to pixels with extreme values of reflectance.

Supervised classification of hyperspectral images is a very challenging task due to the generally unfavorable ratio between the number of spectral bands and the number of training samples available a priori, which results in the Hughes phenomenon. For this purpose, several feature extraction methods have been investigated in order to reduce the dimensionality of the data to the right subspace without significant loss of the original information that allows for the separation of classes. In this letter, we explore the use of spectral unmixing for feature extraction prior to supervised classification of hyperspectral data using support vector machines. The proposed feature extraction strategy has been implemented in the form of four different unmixing chains and evaluated using two different scenes collected by National Aeronautics and Space Administration Jet Propulsion Laboratory's Airborne Visible/Infrared Imaging Spectrometer. The experiments suggest competitive results but also show that the definition of the unmixing chains plays an important role in the final classification accuracy. Moreover, differently from most feature extraction techniques available in the literature, the features obtained using linear spectral unmixing are potentially easier to interpret due to their physical meaning.

In this letter, a technique based on independent component analysis (ICA) and extended morphological attribute profiles (EAPs) is presented for the classification of hyperspectral images. The ICA maps the data into a subspace in which the components are as independent as possible. APs, which are extracted by using several attributes, are applied to each image associated with an extracted independent component, leading to a set of extended EAPs. Two approaches are presented for including the computed profiles in the analysis. The features extracted by the morphological processing are then classified with an SVM. The experiments carried out on two hyperspectral images proved the effectiveness of the proposed technique.

In this paper, the use of Independent Component (IC) Discriminant Analysis (ICDA) for remote sensing classification is proposed. ICDA is a nonparametric method for discriminant analysis based on the application of a Bayesian classification rule on a signal composed by ICs. The method uses IC Analysis (ICA) to choose a transform matrix so that the transformed components are as independent as possible. When the data are projected in an independent space, the estimates of their multivariate density function can be computed in a much easier way as the product of univariate densities. A nonparametric kernel density estimator is used to compute the density functions of each IC. Finally, the Bayes rule is applied for the classification assignment. In this paper, we investigate the possibility of using ICDA for the classification of hyperspectral images. We study the influence of the algorithm used to enforce independence and of the number of IC retained for the classification, proposing an effective method to estimate the most suitable number. The proposed method is applied to several hyperspectral images, in order to test different data set conditions (urban/agricultural area, size of the training set, and type of sensor). Obtained results are compared with one of the most commonly used classifier of hyperspectral images (support vector machines) and show the comparative effectiveness of the proposed method in terms of accuracy.

The definition of the Mahalanobis kernel for the classification of hyperspectral remote sensing images is addressed. Class specific covariance matrices are regularized by a probabilistic model which is based on the data living in a subspace spanned by the p first principal components. The inverse of the covariance matrix is computed in a closed form and is used in the kernel to compute the distance between two spectra. Each principal direction is normalized by a hyperparameter tuned, according to an upper error bound, during the training of an SVM classifier. Results on real data sets empirically demonstrate that the proposed kernel leads to an increase of the classification accuracy by comparison to standard kernels.

International audience

Independent component analysis (ICA) is a very popular method that has shown success in blind source separation, feature extraction and unsupervised recognition. In recent years ICA has been largely studied by researchers from the signal processing community. This paper addresses a more in-depth study on the use of this method, applied to hyper-spectral images used for remote sensing purposes. In a first part, source separation is addressed. Since the independence of sources is usually not verified in hyperspectral real data images, ICA, if used alone, is not a suitable tool to unmix sources. We propose a hierarchical approximation for the use of ICA as a pre-processing step for a Bayesian Positive Source Separation method. In a second part, the use of ICA for dimensionality reduction is studied in the frame of hyperspectral data classification. Experimental results show the effectiveness of ICA when used for hyperspectral image pre-processing for the two considered applications.

The subject of this work is the model selection of kernels with multiple parameters for support vector machines (SVM), with the purpose of classifying hyperspectral remote sensing data. During the training process, the kernel parameters need to be tuned properly. In this work a gradient descent based algorithm is used to estimate the parameters. The selection of multiple parameters is addressed, and an approach based on the analysis of the variance values of individual bands was proposed. Several state of the art kernels were tested. Experiments were conducted on real hyperspectral data. Results obtained with the different approaches/kernels were compared statistically, and showed good results in terms classification accuracies and processing time.