Abstract
Accurate and efficient extraction of cultivated land data is of great significance for agricultural resource monitoring and national food security. Deep-learning-based classification of remote-sensing images overcomes the two difficulties of traditional learning methods (e.g., support vector machine (SVM), K-nearest neighbors (KNN), and random forest (RF)) when extracting the cultivated land: (1) the limited performance when extracting the same land-cover type with the high intra-class spectral variation, such as cultivated land with both vegetation and non-vegetation cover, and (2) the limited generalization ability for handling a large dataset to apply the model to different locations. However, the “pooling” process in most deep convolutional networks, which attempts to enlarge the sensing field of the kernel by involving the upscale process, leads to significant detail loss in the output, including the edges, gradients, and image texture details. To solve this problem, in this study we proposed a new end-to-end extraction algorithm, a high-resolution U-Net (HRU-Net), to preserve the image details by improving the skip connection structure and the loss function of the original U-Net. The proposed HRU-Net was tested in Xinjiang Province, China to extract the cultivated land from Landsat Thematic Mapper (TM) images. The result showed that the HRU-Net achieved better performance (Acc: 92.81%; kappa: 0.81; F1-score: 0.90) than the U-Net++ (Acc: 91.74%; kappa: 0.79; F1-score: 0.89), the original U-Net (Acc: 89.83%; kappa: 0.74; F1-score: 0.86), and the Random Forest model (Acc: 76.13%; kappa: 0.48; F1-score: 0.69). The robustness of the proposed model for the intra-class spectral variation and the accuracy of the edge details were also compared, and this showed that the HRU-Net obtained more accurate edge details and had less influence from the intra-class spectral variation. The model proposed in this study can be further applied to other land cover types that have more spectral diversity and require more details of extraction.
Highlights
Accurate area and change of cultivated land is one of the fundamental types of data for precision agriculture, food security analysis, yields forecasting, and land-use/land-cover research [1]
The major contributions of this study can be summarized as: (1) we redesigned the skip connection structure of the U-Net to keep the high-resolution details for remote sensing image classification; (2) we modified the original U-Net loss function to achieve a higher extraction accuracy for the target with a high intra-class variation; (3) we proposed a new end-to-end cultivated land extraction algorithm, the high-resolution U-Net (HRU-Net), which demonstrated good performance in extracting the target with high edge details and high intra-class spectral variation
The results indicated that the modified loss function contributed nearly 4–5%, 5–16%, and 2–8% improvement of the overall accuracy, kappa, and F1 score over Thematic Mapper (TM)-NRG
Summary
Accurate area and change of cultivated land is one of the fundamental types of data for precision agriculture, food security analysis, yields forecasting, and land-use/land-cover research [1]. The frequently used traditional pixel-based classifiers, such as support vector machine (SVM), K-nearest neighbors (KNN), and random forest (RF) [8,9], and the object-based farmland extraction models, such as the stratified object-based farmland extraction [6], the superpixels and supervised machine-learning model [10], and the time-series-based methods [11], usually require the prior knowledge to model the high intra-class variation of the spatial or spectral features. The features learned by these methods are often limited to the specific datasets, time, and locations, which is known as limited model generalization ability. The re-training process is usually required when applying these models to different datasets, time, and locations
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