Buildings are important infrastructures in geodatabase. Extracting buildings effectively and accurately is significant in urban planning

population distribution

land analysis

and environmental surveys. However

buildings cannot be extracted using a unified model because of the common phenomenon of "same object with different spectra and same spectrum with different objects" and given that the spectrum

shape

and texture of a building are not exactly the same. Considering that most buildings are in rectangular shapes and that the spectral information of a building is generally more uniform than that of other objects

some buildings can be extracted with shape and gradient information. On this basis

the open space and waters are removed using the shadow as a context feature. The texture of the imagery within buildings usually presents a good consistency within a certain area. Thus

other buildings can be extracted by the probability model

which is established by textural features from the initial extraction of buildings as the training samples. On the basis of the analysis above

a hierarchical building extraction method based on spectral

spatial

contextual

and textural features is proposed. The proposed method includes three main parts

namely

image preprocessing

initial extraction of buildings

and final extraction of buildings

and is applied to single high-resolution remote sensing imagery. High-frequency noise is removed and edge information is effectively retained by imagery preprocessing based on bilateral filter. Vegetation and shadow are extracted using super green and morphological shadow indexes. The building index is constructed using multi-scale and multi-orientation gradient operators

and some rectangle buildings are extracted using the building index and shape features. The extracted buildings are expanded with the multi-direction linear structural elements. The voting matrix is calculated by the number of pixels included in the intersection of the expansion result and the shadow to determine the light direction. The extracted buildings are eliminated on the basis of the shadow feature and the light direction. Thus

the initial extraction of the building is completed. Finally

pixel-level building extraction result is obtained by the probability model

which is established by utilizing the textural feature vector of the initial extraction result. The final extraction result is achieved by judging the number of pixels and the proportion of the objects generated by imagery segmentation. Two high-resolution remote sensing imageries from Okinawa and Chicago are used in verifying the validity of the proposed method. Results before and after using the shadow feature are compared

and the proposed initial extraction method is compared with neighborhood total variation method and Sobel operator. Experimental results show that the proposed method can recognize buildings in different directions and provide highly reliable building samples for post-processing. Shadow as a context feature can effectively avoid the confusion of open space

road

and other objects. In the building extraction experiment

the proposed method is compared with morphological building index + morphological shadow index algorithm and neighborhood total variation + mixed Gaussian model + Bayesian decision algorithm. The experiments are carried out using precision rate

recall rate

and F-score

and the final accuracies of our experiments are significantly improved. Precision rate increases by 2.90 percentage points

recall rate increases by 12.49 percentage points

and F-score increases by 8.84. This study presents a method that combines spectral

shape

contextual

and textural features for building extraction. The proposed method adopts the hierarchical extraction strategy. First

building samples are extracted on the basis of the characteristics of spectral information and their shape (i.e.

rectangular). Then

the probability model is established using the textural feature of samples to complete the building extraction. The proposed method is based on the characteristics of high-resolution remote sensing imagery. The method exhibits significant advantages in the area where buildings are dense and imagery is complex and can extract the building targets with various shapes and spectral characteristics. The proposed method can also extract the building objects with strong anti-jamming capability

high accuracy

and great applicability. However

effectively determining the optimal scale in post-extraction and treating the obstructed buildings remain to be further studied. Given that textural feature is mainly dependent on post-extraction

the recognition of buildings with different textural features from samples is poor and should therefore be enhanced in future works.