Gestalt is a psychology term that means unified whole. This term refers to the theory which describes how people tend to group visual elements when certain principles are fulfilled. Gestalt concludes principles

such as similarity

row continuation

proximity

and closure

which are based on the overall cognition of an object which can be obtained through perceiving its parts. However

quantizing these principles in mathematics is difficult in practice because they are abstract psychology concepts. Actually

only few principles

such as similarity and proximity

are used in the literature

and these principles are interpreted in a simple way. Building extraction based on Gestalt laws is challenging and demanding. In this paper

the idea of Gestalt is applied to designing a building extraction algorithm from remote sensing image by testing the candidate edge points

defining the relationship of edge points according to Gestalt principles

finding the edge points

and fitting edges of buildings. This paper proposes a new method for extracting buildings from high resolution remote sensing image based on Gestalt rules. First

the scale invariant feature transform algorithm is used to extract the key points as candidate edge points in a given remote sensing image

wherein each key point is respectively attributed with features

such as position

orientation

scale

and assessment. Gestalt space

which is a 4-D domain containing the preceding features mentioned

is established

and the subsequent operations are completed in the space. The Gestalt principle known as row continuation determines whether or not all candidate edge points are on the edges of buildings. Each candidate edge point involved in the operation is regarded as an integral Gestalt

and a new integral Gestalt is obtained after the operation according to the principle of row continuation. If the assessment of the new integral Gestalt is larger than the given threshold

then the integral Gestalts

namely the candidate edge points involved in operation

fulfill the rule of row continuation and are all on the edges of buildings. Consequently

the set of edge points can be obtained. Finally

the edges of buildings are fitted with different sets of edge points

and silhouettes of buildings are formed by combining their extracted edges. Experiments are carried on with the WorldView-2 images compared with other building extraction algorithms

such as mean shift algorithm

multi-scale segmentation

and region merge-based building extraction algorithm to quantitatively and qualitatively test the proposed method. The proposed method can better correctly and completely extract the buildings from the image. We use the evaluation measures extensively accepted for building extraction to quantify the accuracy of the building extraction results. The extracted buildings and manually delineated buildings are compared pixel-by-pixel. All pixel in the image are categorized into four types: true positive()

manual and automated methods label the pixel belonging to the buildings; true negative()

manual and automated methods label the pixel belonging to the background; false positive()

the automated method incorrectly labels the pixel as belonging to a building; false negative()

the automated method incorrectly labels a pixel truly belonging to a building. The number of pixels that fall into each of the four categories mentioned are determined. In addition

the branching factor()

miss factor()

detection percentage()

and quality percentage() are computed. The is a measure of the commission error in which the system incorrectly labels background pixels as buildings. The measures the omission error in which the system incorrectly labels building pixels as background. The denotes the percentage of building pixels correctly labeled by the automated process. The measures the absolute quality of the extraction and is the most stringent measure. The and of the proposed method are greater than the comparison method

and all of the proposed method is greater than 95%. The proposed method is more accurate than other building extraction methods. Moreover

all experiments demonstrate the feasibility and effectiveness of the proposed method. Experimental results show that the proposed method can well extract the buildings from high resolution remote sensing image. Thus

the proposed method is demonstrated to be a feasible and effective method for building extraction. For remote sensing images

the proposed method is suitable for edge extraction with linear features of the building on an image. The accuracy will be decreased with low resolution when the proposed method is used for building extraction. Therefore

the suggested resolution of the experimental image is above five meters.