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
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MO2.1.1: A NEW CNN-RNN FRAMEWORK FOR REMOTE SENSING IMAGE CAPTIONING |
| Genc Hoxha, Farid Melgani, Jacopo Slaghenauffi, University of Trento, Italy |
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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 |
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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 |
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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 |
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