FG Remote Sensing Image Analysis Group

15 Items

Recent Submissions
A Weighted SVM-Based Approach to Tree Species Classification at Individual Tree Crown Level Using LiDAR Data

Nguyen, Hoang Minh ; Demir, Begüm ; Dalponte, Michele (2019-12-09)

Tree species classification at individual tree crowns (ITCs) level, using remote-sensing data, requires the availability of a sufficient number of reliable reference samples (i.e., training samples) to be used in the learning phase of the classifier. The classification performance of the tree species is mainly affected by two main issues: (i) an imbalanced distribution of the tree species class...

Deep Metric and Hash-Code Learning for Content-Based Retrieval of Remote Sensing Images

Roy, Subhankar ; Sangineto, Enver ; Demir, Begüm ; Sebe, Nicu (2018-11-05)

The growing volume of Remote Sensing (RS) image archives demands for feature learning techniques and hashing functions which can: (1) accurately represent the semantics in the RS images; and (2) have quasi real-time performance during retrieval. This paper aims to address both challenges at the same time, by learning a semantic-based metric space for content based RS image retrieval while simul...

Advanced Local Binary Patterns for Remote Sensing Image Retrieval

Tekeste, Issayas ; Demir, Begüm (2018-11-05)

The standard Local Binary Pattern (LBP) is considered among the most computationally efficient remote sensing (RS) image descriptors in the framework of large-scale content based RS image retrieval (CBIR). However, it has limited discrimination capability for characterizing high dimensional RS images with complex semantic content. There are several LBP variants introduced in computer vision tha...

Retrieving Images with Generated Textual Descriptions

Hoxha, Genc ; Melgani, Farid ; Demir, Begüm (2019-11-14)

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 convolutional neural network (CNN) and a recurrent ...

A Novel Multi-Attention Driven System for Multi-Label Remote Sensing Image Classification

Sumbul, Gencer ; Demir, Begüm (2019-11-14)

This paper presents a novel multi-attention driven system that jointly exploits Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) in the context of multi-label remote sensing (RS) image classification. The proposed system consists of four main modules. The first module aims to extract preliminary local descriptors of RS image bands that can be associated to different spatial...

Bigearthnet: A Large-Scale Benchmark Archive for Remote Sensing Image Understanding

Sumbul, Gencer ; Charfuelan, Marcela ; Demir, Begüm ; Markl, Volker (2019-11-14)

This paper presents the BigEarthNet that is a new large-scale multi-label Sentinel-2 benchmark archive. The BigEarthNet consists of 590, 326 Sentinel-2 image patches, each of which is a section of i) 120 × 120 pixels for 10m bands; ii) 60×60 pixels for 20m bands; and iii) 20×20 pixels for 60m bands. Unlike most of the existing archives, each image patch is annotated by multiple land-cover class...

Semantic-Fusion Gans for Semi-Supervised Satellite Image Classification

Roy, Subhankar ; Sangineto, Enver ; Demir, Begüm ; Sebe, Nicu (2018-09-06)

Most of the public satellite image datasets contain only a small number of annotated images. The lack of a sufficient quantity of labeled data for training is a bottleneck for the use of modern deep-learning based classification approaches in this domain. In this paper we propose a semi -supervised approach to deal with this problem. We use the discriminator (D) of a Generative Adversarial Netw...

A Novel Data Fusion Technique for Snow Cover Retrieval

De Gregorio, Ludovica ; Callegari, Mattia ; Marin, Carlo ; Zebisch, Marc ; Bruzzone, Lorenzo ; Demir, Begüm ; Strasser, Ulrich ; Marke, Thomas ; Günther, Daniel ; Nadalet, Rudi ; Notarnicola, Claudia (2019-06-27)

This paper presents a novel data fusion technique for improving the snow cover monitoring for a mesoscale Alpine region, in particular in those areas where two information sources disagree. The presented methodological innovation consists in the integration of remote-sensing data products and the numerical simulation results by means of a machine learning classifier (support vector machine), ca...

Approximating JPEG 2000 wavelet representation through deep neural networks for remote sensing image scene classification

Preethy Byju, Akshara ; Sumbul, Gencer ; Demir, Begüm ; Bruzzone, Lorenzo (2019-10-15)

This paper presents a novel approach based on the direct use of deep neural networks to approximate wavelet sub-bands for remote sensing (RS) image scene classification in the JPEG 2000 compressed domain. The proposed approach consists of two main steps. The first step aims to approximate the finer level wavelet sub-bands. To this end, we introduce a novel Deep Neural Network approach that util...

A novel active learning technique for multi-label remote sensing image scene classification

Teshome Zegeye, Bayable ; Demir, Begüm (2018-10-09)

This paper presents a novel multi-label active learning (MLAL) technique in the framework of multi-label remote sensing (RS) image scene classification problems. The proposed MLAL technique is developed in the framework of the multi-label SVM classifier (ML-SVM). Unlike the standard AL methods, the proposed MLAL technique redefines active learning by evaluating the informativeness of each image...

A novel coarse-to-fine remote sensing image retrieval system in JPEG-2000 compressed domain

Preethy Byju, Akshara ; Demir, Begüm ; Bruzzone, Lorenzo (2018-10-09)

This paper presents a novel content-based image search and retrieval (CBIR) system that achieves coarse to fine remote sensing (RS) image description and retrieval in JPEG 2000 compressed domain. The proposed system initially: i) decodes the code-streams associated to the coarse (i.e., the lowest) wavelet resolution, and ii) discards the most irrelevant images to the query image that are select...

From Big Data to Big Information and Big Knowledge - the Case of Earth Observation Data

Bereta, Konstantina ; Manolis, Koubarakis ; Manegold, Stefan ; Stamoulis, George ; Demir, Begüm (2018)

The tutorial is aimed at database, information retrieval and knowledge management researchers who would like to understand the state of the art and open problems in data science pipelines for EO data and linked geospatial data, and practitioners who would like to develop applications using existing tools. The tutorial assumes familiarity with RDF, SPARQL and geospatial data.

A Novel System for Content-Based Retrieval of Single and Multi-Label High-Dimensional Remote Sensing Images

Dai, Osman Emre ; Demir, Begüm ; Sankur, Bülent ; Bruzzone, Lorenzo (2018)

This paper presents a novel content-based remote sensing (RS) image retrieval system that consists of the following. First, an image description method that characterizes both spatial and spectral information content of RS images. Second, a supervised retrieval method that efficiently models and exploits the sparsity of RS image descriptors. The proposed image description method characterizes t...

An Unsupervised Multicode Hashing Method for Accurate and Scalable Remote Sensing Image Retrieval

Reato, Thomas ; Demir, Begüm ; Bruzzone, Lorenzo (2019)

Hashing methods have recently attracted great attention for approximate nearest neighbor search in massive remote sensing (RS) image archives due to their computational and storage effectiveness. The existing hashing methods in RS represent each image with a single-hash code that is usually obtained by applying hash functions to global image representations. Such an approach may not optimally r...

Capsule Networks for Object Detection in UAV Imagery

Mekhalfi, Mohamed Lamine ; Bejiga, Mesay Belete ; Soresina, Davide ; Melgani, Farid ; Demir, Begüm (2019-07-17)

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 in the framework of the complex object detecti...