Automatic shadow detection and effects removal in high spatial resolution digital images

• Grant number: 15/26743-5
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• Start date: May 01, 2016
• End date: April 30, 2017
• Field of knowledge: Physical Sciences and Mathematics - Geosciences
• Principal Investigator: Erivaldo Antonio da Silva
• Grantee: Samara Calçado de Azevedo
• Supervisor: Ramesh Pratap Singh
• Host Institution: Faculdade de Ciências e Tecnologia (FCT). Universidade Estadual Paulista (UNESP). Campus de Presidente Prudente. Presidente Prudente , SP, Brazil
• Institution abroad: Chapman University, United States
• Associated to the scholarship: 13/25257-4 - AUTOMATIC DETECTION AND REMOVAL OF SHADOWS OF ITS EFFECTS ON SPATIAL HIGH RESOLUTION DIGITAL IMAGES, BP.DR

Abstract

Interest in Remote Sensing (RS) data for various applications has motivated the development, construction and launch of satellite sensors with high spatial resolution by many countries. New methodologies, however, are needed to analyze in an efficient way this new generation of data, especially in urban applications, due to the great variety of objects and shadows formed by elevated objects. The presence of shadow leads to unwanted effects and significant problems for image analysis, since it causes omission or loss of feature information. Consequently, missing data can interfere in urban land cover classification, environmental application, automatic extraction and object recognition, three-dimensional reconstruction and many other things. Several research projects have set out to remove the effect of shadow as a necessary first preprocessing step in image processing techniques, in order to improve its outcome. Nevertheless, in the context high resolution RS imagery, there is room for improvement and issues that should be faced to address shadow removal. In general, effects from penumbra and mixed pixel problems between shadow regions and objects similar to the shadow should be handled to guarantee a suitable output, with effective visual quality, so as to avoid error propagation in the processing chain. Moreover, considerations concerning the radiometric accuracy of shadow restoration have not been further assessed after the shadow removal process. For these reasons, this project is justified by the need to find robust, fast, accurate and automatic alternatives to overcome the challenge of shadow in high resolution RS images. In this sense, the project will be undertaken at Chapman University with a supervisor from the Computational Science and Engineering Department, with experience of Earth System Science and RS applications. It will be very important to obtain experience to develop techniques for image analysis and reconstruction, such as shadow removal. (AU)

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)

CHAUHAN, AKSHANSHA; DE AZEVEDO, SAMARA C.; SINGH, RAMESH P.. Pronounced changes in air quality, atmospheric and meteorological parameters, and strong mixing of smoke associated with a dust event over Bakersfield, California. ENVIRONMENTAL EARTH SCIENCES, v. 77, n. 4, FEB 2018. (15/26743-5)

DE AZEVEDO, SAMARA CALCADO; CARDIM, GUILHERME PINA; PUGA, FERNANDA; SINGH, RAMESH P.; DA SILVA, ERIVALDO ANTONIO. Analysis of the 2012-2016 drought in the northeast Brazil and its impacts on the Sobradinho water reservoir. REMOTE SENSING LETTERS, v. 9, n. 5, p. 439-447, 2018. (13/25257-4, 15/26743-5)

DE AZEVEDO, SAMARA C.; SINGH, RAMESH P.; DA SILVA, ERIVALDO A.; IEEE. ASSESSING 2016 DROUGHT PROGRESSION OVER INDIA USING REMOTE SENSING DATA FOR THE PERIOD 2006-2015. 2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), v. N/A, p. 4-pg., 2017-01-01. (15/26743-5)

PEDROSA, MIRIAM MARIA; DE AZEVEDO, SAMARA CALCADO; DA SILVA, ERIVALDO ANTONIO; DIAS, MAURICIO ARAUJO. Improved automatic impact crater detection on Mars based on morphological image processing and template matching. GEOMATICS NATURAL HAZARDS & RISK, v. 8, n. 2, p. 1306-1319, 2017. (15/26743-5, 13/25257-4)