Abstract
In the final feature map obtained using a convolutional neural network for remote sensing image segmentation, there are great differences between the feature values of the pixels near the edge of the block and those inside the block; ensuring consistency between these feature values is the key to improving the accuracy of segmentation results. The proposed model uses an edge feature branch and a semantic feature branch called the edge assistant feature network (EFNet). The EFNET model consists of one semantic branch, one edge branch, one shared decoder, and one classifier. The semantic branch extracts semantic features from remote sensing images, whereas the edge branch extracts edge features from remote sensing images and edge images. In addition, the two branches extract five-level features through five sets of feature extraction units. The shared decoder sets up five levels of shared decoding units, which are used to further integrate edge features and deep semantic features. This strategy can reduce the feature differences between the edge pixels and the inner pixels of the object, obtaining a per-pixel feature vector with high inter-class differentiation and intra-class consistency. Softmax is used as the classifier to generate the final segmentation result. We selected a representative winter wheat region in China (Feicheng City) as the study area and established a dataset for experiments. The comparison experiment included three original models and two models modified by adding edge features: SegNet, UNet, and ERFNet, and edge-UNet and edge-ERFNet, respectively. EFNet’s recall (91.01%), intersection over union (81.39%), and F1-Score (91.68%) were superior to those of the other methods. The results clearly show that EFNET improves the accuracy of winter wheat extraction from remote sensing images. This is an important basis not only for crop monitoring, yield estimation, and disaster assessment but also for calculating land carrying capacity and analyzing the comprehensive production capacity of agricultural resources.
Highlights
Image semantic segmentation assigns a label value to each pixel in an image.[1]
The edge feature branch proposed in this research uses an edge detection map combined with semantic features to extract improved edge features
The edge assistant feature network (EFNet) model was proposed based on this methodology
Summary
Image semantic segmentation assigns a label value to each pixel in an image.[1]. Researchers use the shallow features such as image color, grayness, and texture along with deep semantic features obtained via deep learning to assign each pixel in the image to its category.[2]. Image semantic segmentation algorithms extracted features primarily based on manual design; they include scale-invariant feature conversion,[9] directional gradient histogram [histogram of oriented gradients(HOG)],10 and histogram back projection.[11] More recently, machine learning methods based on probabilistic map models have been proposed, such as the Markov random field,[12] Bayesian network,[13] and conditional random field (CRF).[14] these methods rely excessively on manually labeled feature libraries.[15,16] a large amount of manpower is required, which greatly restricts the practical application of these methods
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