# A Building Segmentation Network Based on Improved Spatial Pyramid in Remote Sensing Images
###### Abstract
The extraction of any feature from an object, when conducted at both a certain scale and other scales, will produce dissimilar results. With the development of deep learning, techniques have been introduced to meet these requirements. To enlarge the receptive field of feature maps, the Pyramid Scene Parsing Network (PSPNet) [22] adopts Spatial Pyramid Pooling, which pools the feature maps into different sizes and concatenates them after up-sampling. DeepLab [23; 24; 25] adopts Atrous Spatial Pyramid Pooling (ASPP), which employs dilated/atrous convolution [26], in order to extract at a single scale and to perform accurate and effective classification of regions at any scale. To improve the resolution of the reconstructed image and, thus, object boundaries, Single-Image Super-Resolution (SISR) [27] has been proposed to generate a high-resolution image from low-resolution observations of the same scene. The Super-Resolution Convolutional Neural Network (SRCNN) [28] is the first end-to-end super-resolution algorithm that uses the CNN structure, which has achieved superior performance against traditional methods. Very Deep Convolutional Networks for Super Resolution (VDSR) [29] and Deeply-Recursive Convolutional Network (DRCN) [30] further improve SRCNN by using gradient clipping, skip connection, or recursive-supervision to ease the difficulty of training deep network. Accelerating the Super-Resolution Convolutional Neural Network (FSRCNN) [31] has been proposed to accelerate the training and testing of SRCNN. Super-Resolution Using a Generative Adversarial Network (SRGAN) has been submitted to construct a deeper network with perceptual losses [32] and a generative adversarial network (GAN) [33] for photo-realistic super-resolution. Efficient Sub-Pixel Convolutional Neural Network (ESPCN) [34] uses a shallow network (e.g., FCN) and only upscale the low-resolution image at the very last stage, in order to achieve real-time performance. DeepBack-Projection Networks For Super-Resolution (DBPN) [35] provides an error feedback mechanism for projection errors at each stage. Recent research has made improvements to the CNN structure, based on the characteristics of remote sensing images. Recent studies have also applied CNN networks for remote sensing images, such as FCN, Deeplabv3+, ENet [36], and ESPNet [37], which have been used to achieve excellent results in building detection. [PERSON] et al. used FCN to extract buildings [38]. [PERSON] et al. used the improved UNet [39] network with the guided filter to extract buildings [40]. [PERSON] [41] used SegNet for Unmanned Aerial Vehicle-Based Images in Riverbank Monitoring. [PERSON] [42] proposed a multi-constraint fully convolutional network (MC-FCN) model to perform end-to-end building segmentation. [PERSON] [43] improved the FCN using an exponential linear unit (ELU), in order to reduce the noise (i.e., falsely classified buildings) and sharpen the boundaries of the buildings. SPP-PCANet [44] has been designed to combine PCANet and spatial pyramid pooling, in order to reduce the amount of false positives and to improve the detection rate. [PERSON] [45] applied the ASPP and superpixel-based DenseCRF to perform dense semantic labeling on remote sensing images. [PERSON] [46] used an MR-SSD model to detect different objects in large-scale SAR images. WSF-Net [47] can achieve comparable results to fully supervised methods, when using only image-level annotations with weakly supervised binary segmentation. SPMF-Net [48] takes the image-level labels as supervision information in a classification network that combines superpixel pooling and multi-scale feature fusion structures for building segmentation. [PERSON] [49] combined semi-supervised training, Average Update of Pseudo-label (AUP) with pseudo-labels, and strong labels to improve the segmentation performance. [PERSON] [50] proposes a processing procedure in a geographic information system (GIS) environment which advocates the identification of roof surfaces based on the LiDAR point cloud.