# Fully Convolutional Neural Network with Augmented
## 1 Introduction  
Remote sensing (RS) image segmentation technology plays a key role in the fields of urban planning [1], RS mapping [2; 3], precision agriculture [4; 5], landscape classification [6; 7], traffic monitoring [8], environmental protection [9], climate change [10] and forest vegetation [11], and therefore provides important decision support for human work and life. Traditional image segmentation methods are mainly based on spectral statistical features, such as minimum distance, maximum likelihood, and K-means clustering [12; 13]. Although these methods achieved good results, with the improvement of RS image resolution, segmentation and recognition accuracy can no longer meet the requirements [14]. Motivated by the ongoing success of deep learning methods in computer vision, the RS image segmentation tasks have been tackled by ConvolutionalNeural Networks (CNNs) [15], which greatly improved the precision. In the classical CNN model, the image blocks composed of a pixel point and its adjacent pixels were input into the network to extract the feature, which are used for the classification of each pixel [16]. This approach introduces much redundant computation in the batch operation, and leads to large memory consumption and low partition efficiency. [PERSON] et al. [17] proposed Fully CNN (FCN), which can accept image of any size as input, extract the features by the convolutional layer, followed by deconvolution upsampling, and output a segmentation image with the same size, with accurate target object edges and assigned label. At present, the FCN model has been widely used in image segmentation [18; 19; 20; 21; 22; 23]. In addition, since the image resolution is decreasing in the convolution and pooling operations of CNN, the segmentation result generated by the last layer is often low in resolution. Many subsequent models for image segmentation further extend the idea of FCN. The representative models include SegNet [24], U-Net [25], DeepUNet [26], Y-Net [27] and DeepLab [28; 29]. Among them, DeepLabv3 network is one of the most excellent methods for image segmentation. It acquires a larger receptive field by migrating learning to initialize the encoder ResNet [12] in ImageNet [30; 31; 32; 33] pre-trained weights and frame capture of the Atrous Spatial Pyramid Pooling (ASPP) composed of parallel convolution with different expansion rates. Similarly, in [34], the pyramid pooling module is used to extract feature maps at multiple scales. Other recent state-of-the art ideas include attention structure to deal with different scale information and select features, akin to learning in the discriminative feature network [35]; the refinement residual block [36], which can aggregate the information across different channels and refine the feature map to improve the recognition ability of each stage; and maximum fusion strategy to combine information from deep and shallow layers to avert the loss of detailed information because of downsampling in FCN [37]; and controlling network training strategies using multi-threading [38]. Gates (or entropy maps), introduced in Gated CNN (GCNN) [39], allow to ascribe adaptive weights to feature maps depending on their importance. As a result, the gates can train the network to target pixels with high uncertainty to enhance the separability of these pixels. The use of pre-trained semantic segmentation network layers can improve the representability of the low-level features, while allowing the effective use of CNNs for smaller datasets [40]. In Intersection of Union (IoU)-Adaptive deformable region-based CNN (IAD R-CNN) [41], the number of dilated convolutions and the IoU threshold of the detectors for training is defined by the IoU value corresponding to a small object, while cascade R-CNN architecture is used to achieve a better overall detection performance.