# Fully Convolutional Neural Network with Augmented
## 1 Introduction
Merging Fast R-CNN, which use a multi-task loss in a single network training stage, with Region Proposal Network (RPN) allowing for the sharing of their convolution features, thus achieving much faster object detection [8]. To decrease the complexity of the network, a number of techniques could be employed, such as to use energy-driven sampling to segment the image into homogeneous superpixels thereby decreasing the number of processing units [42; 43]. [PERSON] et al. [4] use self-organizing Kohonen maps (SOMs) as data pre-processing step for image segmentation and restoration of missing data, which are further processed with 1-D CNN with spectral domain convolutions. [PERSON] et al. [44] propose a scale-robust FCN (SR-FCN). The architecture concatenates the multi-layer features extracted in VGG-16 network to the similar scale features in decoding, in a different way from FCN and DeepLab, where features in encoding are not fully assimilated into features in decoding. [PERSON] et al. [7] use DeconvNet with a novel local smooth regularization that makes segmentation spatially consistent. An integration of edge network with DeconvNet allows for achieving more accurate edge results as compared with common methods. [PERSON] et al. [45] used a global convolutional network (GCN) with the modification of backbone architecture with more layers for higher resolution RS images, additional channel attention block to select the most discriminative filters (features), and domain-specific transfer learning to deal with the scarcity problem by employing other RS datasets with varying resolutions as pre-trained data. [PERSON] and [PERSON] [46] proposed an enhanced fully CNN (EFCN) that uses the exponential linear unit (ELU), to boost the network performance for more accurate prediction, while Conditional Random Fields (CRFs) are used as a last stage to reduce noise and sharpen the edges of objects. [PERSON] et al. [47]proposed replacing a convolution layer with a deformable convolution layer in order to obtain the Deformable ConvNet network that is able to recognize the RS objects with a complex shape and visual appearance. [PERSON] et al. [48] integrated FCN with simple linear iterative clustering (SLIC) in order to utilize the superpixel information for accurate identification of semantic information and precise positioning of small edges. Because of the improvement in resolution of the RS images, the high resolution RS (HRRS) image contains a large amount of information, which expands the application scope of RS image, and the size of recognized objects of interest is relatively smaller. The existing CNN is directly applied for RS image segmentation, which has some problems, such as the poor segmentation effect of small objects and fuzzy boundary. Based on the DeepLabv3 network [28] and the properties of RS image data, we suggest a new method for HRRS image segmentation. The main contributions of this paper are: (1) that the parallel dilated convolution in the ASPP structure uses varying dilation factors, obtains denser sampling, gathers local information at higher level, and improves the segmentation performance for small objects; (2) fully connected (FC) fusion path is added to ensure the information propagation ability of the model, and the information complementary to the full convolution path is employed to capture diversity of information as well as to further improve segmentation accuracy. The remainder of this manuscript is organized as follows. In Section 2, we introduced atrous convolution and DeepLab v3 network [28] structure, pointing out the problem of existing ASPP structure applied to small object segmentation. Then we proposed improved structure A-ASPP and a fully connected path fusion method, which is proposed to improve the segmentation effect of remote sensing images. Section 3 is the experimental part, wherein we detail the improvement of some experimental processes, and share the experience of training the proposed model, and compare it with the existing model. Finally, in Section 4 we summarize the full text.