Building Detection from VHR Remote Sensing Imagery Based on the Morphological Building Index

Abstract

Automatic detection of buildings from very high resolution (VHR) satellite images is a current research hotspot in remote sensing and computer vision. However, many irrelevant objects with similar spectral characteristics to buildings will cause a large amount of interference to the detection of buildings, thus making the accurate detection of buildings still a challenging task, especially for images captured in complex environments. Therefore, it is crucial to develop a method that can effectively eliminate these interferences and accurately detect buildings from complex image scenes. To this end, a new building detection method based on the morphological building index (MBI) is proposed in this study. First, the local feature points are detected from the VHR remote sensing imagery and they are optimized by the saliency index proposed in this study. Second, a voting matrix is calculated based on these optimized local feature points to extract built-up areas. Finally, buildings are detected from the extracted built-up areas using the MBI algorithm. Experiments confirm that our proposed method can effectively and accurately detect buildings in VHR remote sensing images captured in complex environments.

Técnicas y usos en la clasificación automática de imágenes

“…Raj y SivaSathya, 2016 Clasificación mediante diferentes formas espectrales, índices de vegetación, información contextual, imágenes multitemporales o datos multisensor Rezaeian, 2012 Detección y clasificación de edificios dañados por terremotos Senthilnath, Kulkarni, Benediktsson, y Yang, 2016 Clasificación de tipos de cultivo: áreas problemáticas y tamaño de zona afectada; cambios estacionales y anormalidades en la vegetación; estimación temprana del rendimiento del cultivo. Shen, 2009 Identificación automática del autor de una pintura de los siglos XVI al XVIII Yang, Huang, Kuo, Tsai, y Lin, 2017 Estimación de la cantidad de arroz en cultivos de arrozales para tasaciones tras catástrofes You, Wang, Ma, Chen, Wang, Shen y Liu, 2018 Detección y delimitación de edificios en zonas urbanas Zhang & Zhang, 2011 Clasificación de hojas de tabaco para agilizar los procesos de selección Zhang, Hu, Jing, Woods, y Courville, 2008 Clasificación de especies forestales Tabla 6: Usos y aplicaciones de la clasificación automática…”

Mapping with Pléiades—End-to-End Workflow

“…Mapping, in general, can be described as the process of generating 3D maps of a region of interest on the Earth's surface consisting of 3D coordinates linked with the spectral information that was observed by the underlying sensor. Exemplary applications in the domain of remote sensing are, for instance, city modeling [1][2][3], forest assessment and biomass estimation [4][5][6], change detection [7,8], land cover and land use classification [9,10], carbon reporting [11], farm land monitoring [12], glacier observation [13,14], disaster damage mapping [15,16] and mapping in general [17,18]. To allow semantic analysis those applications need mapping products in form of digital surface models (DSM), digital terrain models (DTM), their difference, that is, normalized digital surface models (nDSM) and the according multi-spectral ortho-rectified images.…”

An Automatic Morphological Attribute Building Extraction Approach for Satellite High Spatial Resolution Imagery

“…In recent years, a combination of the morphological building index (MBI) [21] with the morphological shadow index (MSI) [22] has been proposed to automatically detect buildings in VHR images. By modeling the local contrast, building-directivity, and granulometry with a series of multiscale morphological profiles (MPs) [23], the MBI and its variants [24][25][26] have proven to be effective tools for building detection tasks. However, MPs do not fully exploit spectral information, which restricts the extraction performance to some extent.…”