Farid Melgani

Farid Melgani
Università degli Studi di Trento | UNITN · Department of Information Engineering and Computer Science

Ph.D.

About

243
Publications
88,241
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
12,855
Citations
Citations since 2017
75 Research Items
7802 Citations
201720182019202020212022202302004006008001,0001,2001,400
201720182019202020212022202302004006008001,0001,2001,400
201720182019202020212022202302004006008001,0001,2001,400
201720182019202020212022202302004006008001,0001,2001,400

Publications

Publications (243)
Article
Deep learning represented an impressive advance in the field of machine learning and is continually breaking records in dozens of areas of artificial intelligence, such as image recognition. Nevertheless, the success of these architectures depends on a large amount of labeled data and the annotation of training data is a costly process that is ofte...
Article
Recent advances in satellite technology have led to a regular, frequent and high-resolution monitoring of Earth at the global scale, providing an unprecedented amount of Earth observation (EO) data. The growing operational capability of global Earth monitoring from space provides a wealth of information on the state of our planet Earth that waits t...
Article
Full-text available
A timely diagnosis of coronavirus is critical in order to control the spread of the virus. To aid in this, we propose in this paper a deep learning-based approach for detecting coronavirus patients using ultrasound imagery. We propose to exploit the transfer learning of a EfficientNet model pre-trained on the ImageNet dataset for the classification...
Article
Full-text available
Cross-Modal text-image retrieval in remote sensing (RS) provides a flexible retrieval experience for mining useful information from RS repositories. However, existing methods are designed to accept queries formulated in the English language only, which may restrict accessibility to useful information for non-English speakers. Allowing multi-languag...
Preprint
p>The increasing availability of quantum computers motivates researching their potential capabilities in enhancing the performance of data analysis algorithms. Similarly as in other research communities, also in Remote Sensing (RS) it is not yet defined how its applications can benefit from the usage of quantum computing. This paper proposes a form...
Preprint
p>The increasing availability of quantum computers motivates researching their potential capabilities in enhancing the performance of data analysis algorithms. Similarly as in other research communities, also in Remote Sensing (RS) it is not yet defined how its applications can benefit from the usage of quantum computing. This paper proposes a form...
Article
Full-text available
The steady spread of the 2019 Coronavirus disease has brought about human and economic losses, imposing a new lifestyle across the world. On this point, medical imaging tests such as computed tomography (CT) and X-ray have demonstrated a sound screening potential. Deep learning methodologies have evidenced superior image analysis capabilities with...
Article
Recently, vision-language models based on transformers are gaining popularity for joint modeling of visual and textual modalities. In particular, they show impressive results when transferred to several downstream tasks such as zero and few-shot classification. In this paper, we propose a visual question answering (VQA) approach for remote sensing...
Article
Change detection (CD) is among the most important applications in remote sensing (RS) that allows identifying the changes that occurred in a given geographical area across different times. Even though CD systems have seen a lot of progress in RS, their output is either a binary map highlighting the changing area or a semantic change map that indica...
Article
The increasing availability of quantum computers motivates researching their potential capabilities in enhancing the performance of data analysis algorithms. Similarly, as in other research communities, also in Remote Sensing (RS) it is not yet defined how its applications can benefit from the usage of quantum computing. This paper proposes a formu...
Article
Full-text available
Nowadays, remote sensing image analysis is needed in various important tasks such as city planning, land-use classification, agriculture monitoring, military surveillance, and many other applications. In this context, hyperspectral images can play a useful role, but require specific handling. This paper presents a convolutional neural network based...
Article
Objective In this paper, a stacked autoencoder deep neural network is proposed to extract the QRS complex from raw ECG signals without any conventional feature extraction phase. Methods A simple architecture has been deeply trained on many datasets to ensure the generalization of the network at inference. Results The proposed method achieved a QR...
Article
Most of the remote sensing image captioning (IC) models are based on encoder-decoder frameworks where a convolutional neural network (CNN) encodes the image information and a recurrent neural network (RNN) decodes the image information into a sentence description. In order to achieve good accuracies, encoder-decoder frameworks relying on RNNs typic...
Conference Paper
Full-text available
Recent developments in Quantum Computing (QC) have paved the way for an enhancement of computing capabilities. Quantum Machine Learning (QML) aims at developing Machine Learning (ML) models specifically designed for quantum computers. The availability of the first quantum processors enabled further research, in particular the exploration of possibl...
Article
Full-text available
Object detection is challenging in HSR remote sensing images which have a complex background and irregular object locations. To minimize manual annotation cost in supervised learning methods and achieve advanced detection performance, we proposed a point-based weakly supervised learning method to address the object detection challenge in HSR remote...
Article
In this work, we propose a new algorithm to improve existing techniques used in the field of spectroscopic data regression analysis. In particular, it combines the power of nonlinear kernel regressors (kernel ridge regression [KRR], kernel principal component regression [KPCR], and Gaussian process regression [GPR]) with an optimization based on no...
Article
Bi-LSTM) model has proposed for the analysis of Ground Penetrating Radar B scan (GPR B Scan) images. GPR characteristics, scanning frequency, and soil type make it very difficult to analyze GPR B Scan images. Also, noise and clutter in the image make this problem more challenging. The proposed method shows high performance in determining the scanni...
Article
The method proposed in this paper belongs to the family of orthogonal non-negative matrix factorization (ONMF) methods designed to solve clustering problems. Unlike some existing ONMF methods that explicitly constrain the orthogonality of the coefficient matrix in the cost function to derive their clustering models, the proposed method integrates i...
Preprint
Full-text available
The method proposed in this paper belongs to the family of orthogonal non-negative matrix factorization (ONMF) methods designed to solve clustering problems. Unlike some existing ONMF methods that explicitly constrain the orthogonality of the coefficient matrix in the cost function to derive their clustering models, the proposed method integrates i...
Article
Full-text available
The performance of remote sensing image retrieval (RSIR) systems depends on the capability of the extracted features in characterizing the semantic content of images. Existing RSIR systems describe images by visual descriptors that model the primitives (such as different land-cover classes) present in the images. However, the visual descriptors may...
Article
Full-text available
Yield estimation is an essential preharvest practice among most large-scale farming companies, since it enables the predetermination of essential logistics to be allocated (i.e., transportation means, supplies, labor force, among others). An overestimation may thus incur further costs, whereas an underestimation entails potential crop waste. More i...
Article
Full-text available
In this article, we propose a new semi-supervised method to detect the changes occurring in a geographical area after a major damage. We detect the changes by processing a pair of optical remote sensing images. The proposed method adopts a patch-based approach, whereby we use a Siamese CNN (S-CNN), trained with augmented data, to compare successive...
Article
A recent work on retro-remote sensing (converting ancient text descriptions into images) was proposed using a multilabel encoding scheme in which an input text description is represented by a binary vector indicating the presence or absence of specific objects. However, this kind of encoding disregards information such as object attributes and spat...
Article
Full-text available
Detecting objects becomes an increasingly important task in very high resolution (VHR) remote sensing imagery analysis. With the development of GPU-computing capability, a growing number of deep convolutional neural networks (CNNs) have been designed to address the object detection challenge. However, compared with CPU, GPU is much more costly. The...
Article
In this paper, we propose a Convolutional Support Vector Machine (CSVM) network for the analysis of Ground Penetrating Radar B Scan (GPR B Scan) images. Similar to a Convolutional Neural Network (CNN) architecture, a CSVM is also a cascade of convolution and pooling layers. However, the main difference is that it utilizes linear Support Vector Mach...
Article
Solar radiation components assessment is a highly required parameter for solar energy applications. Due to the non-stationary behavior of solar radiation parameters and variety of atmosphere conditions, stand-alone forecasting models are insufficient for providing accurate estimation in some cases. In this respect, different hybrid models have been...
Conference Paper
This paper presents a novel remote sensing (RS) image retrieval system that is defined based on generation and exploitation of textual descriptions that model the content of RS images. The proposed RS image retrieval system is composed of three main steps. The first one generates textual descriptions of the content of the RS images combining a conv...
Article
Full-text available
In this paper, we present a portable camera-based method for helping visually impaired (VI) people to recognize multiple objects in images. This method relies on a novel multi-label convolutional support vector machine (CSVM) network for coarse description of images. The core idea of CSVM is to use a set of linear SVMs as filter banks for feature m...
Conference Paper
Full-text available
The classification of land-cover classes in remote sensing images can suit a variety of interdisciplinary applications such as the interpretation of natural and man-made processes on the Earth surface. The Convolutional Support Vector Machine (CSVM) network was recently proposed as binary classifier for the detection of objects in Unmanned Aerial V...
Article
Full-text available
Recent advances in Convolutional Neural Networks (CNNs) have attracted great attention in remote sensing due to their high capability to model high-level semantic content of Remote Sensing (RS) images. However, CNNs do not explicitly retain the relative position of objects in an image and, thus, the effectiveness of the obtained features is limited...
Article
In this letter, we discuss unsupervised feature extraction on hyperspectral imagery (HSI) and propose a novel approach based on autoencoder (AE) networks to extract spectral-spatial features from HSI. Our approach takes the data relations into consideration, i.e., the input dependency with adjacent inputs, which the normal AE-based feature extracto...
Article
Full-text available
Learning classification models require sufficiently labeled training samples, however, collecting labeled samples for every new problem is time-consuming and costly. An alternative approach is to transfer knowledge from one problem to another, which is called transfer learning. Domain adaptation (DA) is a type of transfer learning that aims to find...
Article
Full-text available
This work presents an approach to road network extraction in remote sensing images. In our earlier work, we worked on the extraction of the road network using a multi-agent approach guided by Volunteered Geographic Information (VGI). The limitation of this VGI-only approach is its inability to update the new road developments as it only follows the...
Article
The data available in the world come in various modalities, such as audio, text, image, and video. Each data modality has different statistical properties. Understanding each modality, individually, and the relationship between the modalities is vital for a better understanding of the environment surrounding us. Multimodal learning models allow us...
Article
Full-text available
An up-to-date knowledge of water depth is essential for a wide range of coastal activities, such as navigation, fishing, study of coastal erosion, or the observation of the rise of water levels due to climate change. This paper presents a coastal bathymetry estimation method that takes a single satellite acquisition as input, aimed at scenarios whe...
Article
Full-text available
Employing computer vision to extract useful information from images and videos is becoming a key technique for identifying phenotypic changes in plants. In this report, we review the emerging aspects of computer vision for automated plant phenotyping. Recent advances in image analysis empowered by machine learning-based techniques, including convol...
Article
We present a new multi-modal technique for assisting visually-impaired people in recognizing objects in public indoor environment. Unlike common methods which aim to solve the problem of multi-class object recognition in a traditional single-label strategy, a comprehensive approach is developed here allowing samples to take more than one label at a...
Article
We describe a novel multilabel classification approach based on a support vector machine (SVM) for the extremely high-resolution remote sensing images. Its underlying ideas consist to: 1) exploit inter-label relationships by means of a structured SVM and 2) incorporate spatial contextual information by adding to the cost function a term that encour...
Article
Full-text available
In this article, we address the problem of recognizing an event from a single related picture. Given the large number of event classes and the limited information contained in a single shot, the problem is known to be particularly hard. To achieve a reliable detection, we propose a combination of multiple classifiers, and we compare three alternati...
Article
Nowadays, unmanned aerial vehicles (UAVs) are viewed as effective acquisition platforms for several civilian applications. They can acquire images with an extremely high level of spatial detail compared to standard remote sensing platforms. However, these images are highly affected by illumination, rotation, and scale changes, which further increas...
Article
Full-text available
In this letter, we formulate the multilabeling classification problem of unmanned aerial vehicle (UAV) imagery within a conditional random field (CRF) framework with the aim of exploiting simultaneously spatial contextual information and cross-correlation between labels. The pipeline of the framework consists of two main phases. First, the consider...
Chapter
Full-text available
One of the exceptional advantages of spaceborne remote sensors is their regular scanning of the Earth surface, resulting thus in Satellite Image Time Series (SITS), extremely useful to monitor natural or man-made phenomena on the ground. In this chapter, after providing a brief overview of the most recent methods proposed to process and/or analyze...
Article
The accurate reconstruction of areas obscured by clouds is among the most challenging topics for the remote sensing community since a significant percentage of images archived throughout the world are affected by cloud covers which make them not fully exploitable. The purpose of this paper is to propose new methods to recover missing data in multis...
Article
This paper proposes a novel approach for driving chemometric analyses from spectroscopic data and based on a convolutional neural network (CNN) architecture. For such purpose, the well-known 2-D CNN is adapted to the monodimensional nature of spectroscopic data. In particular, filtering and pooling operations as well as equations for training are r...
Article
Full-text available
This paper describes three coarse image description strategies, which are meant to promote a rough perception of surrounding objects for visually impaired individuals, with application to indoor spaces. The described algorithms operate on images (grabbed by the user, by means of a chest-mounted camera), and provide in output a list of objects that...
Article
Full-text available
In this paper, we present a domain adaptation network to deal with classification scenarios subjected to the data shift problem (i.e., labeled and unlabeled images acquired with different sensors and over completely different geographical areas). We rely on the power of pretrained convolutional neural networks (CNNs) to generate an initial feature...
Conference Paper
Full-text available
This paper describes a fast multilabel classification method for unmanned aerial vehicle (UAV) images acquired over urban areas. It starts by subdividing a given query image into a set of equal tiles, which are successively processed and analyzed separately. In particular, each tile is described by extracting opportune features which are then furth...
Article
Full-text available
Precision agriculture represents a promising technological trend in which governments and local authorities are increasingly investing. In particular, optimising the use of pesticides and having localised models of plant disease are the most important goals for the farmers of the future. The Trentino province in Italy is known as a strong national...
Article
Object recognition forms a substantial need for blind and visually impaired individuals. This paper proposes a new multiobject recognition framework. It consists of coarsely checking the presence of multiple objects in a portable camera-grabbed image at a considered indoor site. The outcome is a list of objects that likely appear in the indoor scen...
Article
Full-text available
Following an avalanche, one of the factors that affect victims' chance of survival is the speed with which they are located and dug out. Rescue teams use techniques like trained rescue dogs and electronic transceivers to locate victims. However, the resources and time required to deploy rescue teams are major bottlenecks that decrease a victim's ch...
Poster
Gaussian process classifiers(GPCs) are Bayesian probabilistic kernel classifiers.
Chapter
Full-text available
One of the major limitations of passive sensors is their high sensitivity to weather conditions during the image acquisition process. The resulting images are frequently subject to the presence of clouds, which makes the image partly useless for assessing landscape properties. The common approach to cope with this problem attempts to remove the clo...
Article
Full-text available
We propose a method based on combining wave tracing and linear wave theory for the estimation of wave period and bathymetry in coastal areas from satellite images. The method depends on several parameters for which we provide ranges of variations adapted to the instrument. Experimental results are conducted on several sites located around the Hawai...