Edge Detection With Direction Guided Postprocessing for Farmland Parcel Extraction

Abstract

Farmland is a significant resource for human survival and development. Rapid acquisition of farmland information is the basis for dynamic crop detection and sustainable land development. The continuous development of high resolution remote sensing imagery makes it possible to make a wide range of refined earth observation. With better image interpretation ability, image segmentation method based on deep learning can bring specific results from high resolution imagery and is widely used in remote sensing. However, existing image segmentation methods based on semantic segmentation have difficulties to extracting refined farmland parcels. Deep neural network is used to detect farmland edge. We use HRNet to achieve feature extraction that retains high resolution features, strengthens the feature representation of network context information based on Object-Contextual Representations module, and carries out more complete interpretation of farmland and its boundary. Finally, we design a farmland edge post-processing method to connect the disconnected boundary based on the direction information generated by the connectivity attention module, and finally obtained the farmland boundary which is complete enough to be closed for generating farmland parcels. To verify our method, we used Google Earth image to label farmland boundaries and conduct experimental verification. The results show that our proposed model has a higher precision for farmland edge detection, and the postprocessing method of boundary connection can effectively close the boundary lines and achieve more detailed and complete farmland parcels.

BAFormer: A Novel Boundary-Aware Compensation UNet-like Transformer for High-Resolution Cropland Extraction

“…(3) Proposed a parcel extraction method based on the combination of edge detection and semantic segmentation, which has led to significant progress in image understanding. These [4,[11][12][13][14] take the task of detecting hard or soft boundaries to guide the model to impose further attention constraints on the boundaries, enabling it to obtain better image decoding capabilities. For example, [14] designed frequency attention to topic emphasize key high-frequency components in the feature map to improve the accuracy of boundary detection.…”

“…In recent years, many studies have proposed the combination of deep learning and edge detection to guide the model to better perceive the cropland information, improve the local segmentation score accuracy, and maintain the global morphology land continuity. Existing approaches [2,4,[7][8][9][10][11][12][13][14][15][16] designspecific network structures based on the characteristics of Cropland to guide the model to focus on key features. However, most of them focus on specific geographic regions or a single cropland type, ignoring the regional differences of cropland parcels, and failing to achieve the purpose of generalized extraction.…”

DSCA-PSPNet: Dynamic spatial-channel attention pyramid scene parsing network for sugarcane field segmentation in satellite imagery

“…An edge detection model premised on a connectivity attention-based approach and a high-resolution structure network has been designed for farmland parcel extraction. The model introduces a post-processing method to connect disconnected boundaries, thereby enabling the generation of more refined farmland parcels ( Xie et al., 2023 ). Similarly, a technique called the Multiple Attention Encoder-Decoder Network (MAENet) was proposed for farmland segmentation, yielding an impressive IoU score of 93.74% and a Kappa coefficient of 96.74% ( Huan et al., 2021 ).…”