Technical Program

MO2.1: Deep Learning I

Session Type: Oral
Time: Monday, March 9, 11:20 - 12:40
Location: Valetta
Session Chair: John Kerekes, Rochester Institute of Technology, USA
 
MO2.1.1: A NEW CNN-RNN FRAMEWORK FOR REMOTE SENSING IMAGE CAPTIONING
Genc Hoxha, Farid Melgani, Jacopo Slaghenauffi, University of Trento, Italy
 
MO2.1.2: WHICH CNN LAYER FOR WHICH CHANGE? A CNN ADAPTATION APPROACH FOR CHANGE DETECTION IN REMOTE SENSING DATA
Yacine Slimani, University Ferhat Abbas Setif 1, Algeria; Rachid Hedjam, Sultan Qaboos University, Oman
 
MO2.1.3: DEEP LEARNING MODELS PERFORMANCE FOR NDVI TIME SERIES PREDICTION: A CASE STUDY ON NORTH WEST TUNISIA
Manel Rhif, Laboratoire RIADI, École Nationale des Sciences de l’Informatique, Manouba, Tunisia; Ali Ben Abbes, Centre d’applications et de Recherches en Télédétection (CARTEL), Université de Sherbrooke, Canada; Beatriz Martinez, Departament de Física de la Terra i Termodinàmica, Universitat de València, Spain; Imed Riadh Farah, Laboratoire RIADI, École Nationale des Sciences de l’Informatique, Manouba, Tunisia
 
MO2.1.4: TLDCNN: A TRIPLET LOW DIMENSIONAL CONVOLUTIONAL NEURAL NETWORKS FOR HIGH-RESOLUTION REMOTE SENSING IMAGE RETRIEVAL
Yaakoub Boualleg, Mohamed Farah, Imed Riadh Farah, University of Manouba, Tunisia