Automatic shadow detection and removal from urban areas in high resolution multispectral images

Author(s)

Samara Calçado de Azevedo

• Document type: Doctoral Thesis
• Press: Presidente Prudente. 2018-09-17.
• Institution: Universidade Estadual Paulista (Unesp). Faculdade de Ciências e Tecnologia. Presidente Prudente
• Defense date: 2018-08-30
• Advisor: Erivaldo Antonio da Silva

Abstract

High resolution satellite images has played an important role in information extraction especially in urban areas, since valuable information and higher level of details from surface become available with these images. Nevertheless, the task of extracting information from complex urban environment can be hampered by shadows, which can occupy a significant part into the image and, thus negatively affecting the image analysis. Therefore, due to the complexity of the problem, shadow removal is a crucial as one of the first pre-processing steps to enhance the performance of many subsequent steps and applications. The main goal of this thesis is to present a new automatic method for shadow removal in high spatial resolution satellite multispectral images. The proposed method comprises three main steps: the first one is the pre-processing, which includesthe conversion of the target image to the top of atmosphere (TOA) reflectance and the image pansherpening to spectral indices generation. Secondly, a shadow pixels candidates’ identification is performed, combining black-top-hat (BTH) transformation with area injunction driven by the normalized saturation-value difference index (NSDVI) mask. The obtained output is a shadow mask, which is used to properly guide our automatic inpainting-inspired strategy in the restoration step. In the third step, a hybrid inpainting-based strategy specifically adapted for the multispectral imagery context is applied to recover shadow areas, which unifies anisotropic diffusion, filling-in priority based on cartoon image representations, transport equation and block-based pixel replication using local dynamic sampling. The performance of our approach has been evaluated by taking 215 subset images from WorldView-2 (WV-2) and Pléiades-1B (PL-1B) that encompasses the city of São Paulo. The method achieves an overall accuracy on shadow detection up to 94.2%, for WV-2, and 90.84%, for PL-1B. The comparative results indicate that the proposed method outperforms two existing state-of-the-art methods. Shadow effects are mitigated by local inpainting method in which the satisfactory outputs demonstrate good coherence for highway and building rooftops recovery. Moreover, when largely non-contaminated areas are available in the image, the proposed approach improves significantly more areas of the image than the intensity-based transformation techniques, such as the histogram matching method. Once only multispectral imagery is required as input data, the approach can be suitable to support other remote sensing applications as well. (AU)

• FAPESP's process: 13/25257-4 - AUTOMATIC DETECTION AND REMOVAL OF SHADOWS OF ITS EFFECTS ON SPATIAL HIGH RESOLUTION DIGITAL IMAGES
• Grantee: Samara Calçado de Azevedo
• Support Opportunities: Scholarships in Brazil - Doctorate