Building Extraction from High–Resolution Remote Sensing Images by Adaptive Morphological Attribute Profile under Object Boundary Constraint

Abstract

A novel adaptive morphological attribute profile under object boundary constraint (AMAP–OBC) method is proposed in this study for automatic building extraction from high-resolution remote sensing (HRRS) images. By investigating the associated attributes in morphological attribute profiles (MAPs), the proposed method establishes corresponding relationships between AMAP–OBC and building characteristics in HRRS images. In the preprocessing step, the candidate object set is extracted by a group of rules for screening of non-building objects. Second, based on the proposed adaptive scale parameter extraction and object boundary constraint strategies, AMAP–OBC is conducted to obtain the initial building set. Finally, a further identification strategy with adaptive threshold combination is proposed to obtain the final building extraction results. Through experiments of multiple groups of HRRS images from different sensors, the proposed method shows outstanding performance in terms of automatic building extraction from diverse geographic objects in urban scenes.

Automatic Building Detection from High-Resolution Remote Sensing Images Based on Joint Optimization and Decision Fusion of Morphological Attribute Profiles

“…Hu et al [17] proposed a method of combining the new alternating sequential filters (NASFs) strategy with MAPs for building detection from high-resolution synthetic aperture radar (SAR) images. Wang et al [18] proposed a novel adaptive morphological attribute profile under the object boundary constraint (AMAP-OBC) method. By investigating the associated attributes in MAPs, this method established corresponding relationships between AMAP-OBC and building characteristics in HRRS images.…”

“…At present, scholars have proposed many preliminary screening strategies for nonbuilding objects. This article adopted the four discriminant rules proposed in the literature [18]: shadow index, normalized difference vegetation index (NDVI), area index and rectangularity. The objects rejected in the initial screening are not considered in the subsequent building detection, while the remaining objects constitute the candidate building set R cdi .…”

“…To this end, according to other suggestions [22,23], the area, diagonal, standard deviation, and NMI values were set to [500, 28000], [10,100], [10,70], and [0.2, 0.5]: each of the four attributes extracted 50 scale parameters at equal intervals, resulting in a total of 200 scales of MAPs for the four attributes [18]. On this basis, the initial DAPs set was obtained by applying those differences to all adjacent scales, as defined by DAPs cdi .…”

“…To evaluate the performance of the proposed method comprehensively and objectively, we used six advanced building detection methods for comparative experiments: based on adaptive MAP S method (Method 1) [18]; the grey-level co-occurrence matrix (GLCM) and support vector machine (SVM) based method (Method 2) [26]; the top-hat filter and K-means classification based method (method 3) [27]; based on a DeepLab V3+ network method (Methods 4 and 5) [13], combine the Otsu method and the evidence fusion strategy proposed in this paper, respectively, to obtain the object-level building detection results. By comparing this with Method 1, it is helpful when analyzing the optimization strategies of different DAPs.…”

Extraction of Building from Remote Sensing Imagery Base on Multi-Attention L-CAFSFM

“…Zhang et al [2] proposed a novel rotation-invariant uniform local binary pattern algorithm to obtain low-density feature maps and utilized mean-shift segmentation to extract building edges from the segmented feature maps. Under the constraint of building boundaries, Wang et al [3] proposed an adaptive morphological attribute profile contour (AMAP-OBC) method by combining the morphological attribute profile (MAPs) technique.…”