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Interactive Annotation of Remote Sensing Images

Grant number: 16/14760-5
Support type:Scholarships in Brazil - Doctorate
Effective date (Start): November 01, 2016
Effective date (End): June 30, 2019
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:Alexandre Xavier Falcão
Grantee:John Edgar Vargas Muñoz
Home Institution: Instituto de Computação (IC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Associated scholarship(s):17/10086-0 - Interactive rural building detection and delineation using remote sensing images, BE.EP.DR

Abstract

Several remote sensing image classification methods have been proposed to generate thematic maps of land cover and urban areas. However, photo-interpreters still manually annotate remote sensing images - an exhaustive and error-prone task, especially when it relies on some artificial color image composition with hardly distinguishable classes. In land cover mappings, the problem not rare makes necessary to visit the geographical location for correct image annotation. In this context, the quality of the selected image features and training samples is crucial to minimize the user effort and time in image annotation. The use of regions as samples and active learning techniques can mitigate the problem by reducing the numbers of training samples and iterations for the expert's label supervision. However, the development of active learning methods often relies on the simulation of the expert's actions using pre-annotated data sets. Such practice ignores possible annotation errors and disregard the user as part of the learning loop. Moreover, the methods usually ignore that non-interest regions in the image must also be treated. We propose to investigate active learning methods that do not suffer from those shortcomings in order to develop a more realistic framework for remote sensing image annotation. The proposal relies on effective prior superpixel segmentation, feature extraction, and data organization in order to facilitate the active learning loop, where the apprentice classifier must select the most effective training samples for its learning process. For development and validation, we will use natural color images to facilitate label supervision with the expert being part of the active learning loop. The simulation of the expert's actions on color composition images, pre-annotated by specialists, will also be considered for a more extensive evaluation.

Scientific publications (4)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
VARGAS MUNOZ, JOHN E.; TUIA, DEVIS; FALCAO, ALEXANDRE X. Deploying machine learning to assist digital humanitarians: making image annotation in OpenStreetMap more efficient. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE, AUG 2020. Web of Science Citations: 0.
SRIVASTAVA, SHIVANGI; VARGAS-MUNOZ, JOHN E.; TUIA, DEVIS. Understanding urban landuse from the above and ground perspectives: A deep learning, multimodal solution. REMOTE SENSING OF ENVIRONMENT, v. 228, p. 129-143, JUL 2019. Web of Science Citations: 2.
VARGAS-MUNOZ, JOHN E.; CHOWDHURY, ANANDA S.; ALEXANDRE, EDUARDO B.; GALVAO, FELIPE L.; VECHIATTO MIRANDA, PAULO A.; FALCAO, ALEXANDRE X. An Iterative Spanning Forest Framework for Superpixel Segmentation. IEEE Transactions on Image Processing, v. 28, n. 7, p. 3477-3489, JUL 2019. Web of Science Citations: 0.
VARGAS-MUNOZ, JOHN E.; LOBRY, SYLVAIN; FALCAO, ALEXANDRE X.; TUIA, DEVIS. Correcting rural building annotations in OpenStreetMap using convolutional neural networks. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, v. 147, p. 283-293, JAN 2019. Web of Science Citations: 2.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.