Statistical Texture Learning Method for Monitoring Abandoned Suburban Cropland Based on High-Resolution Remote Sensing and Deep Learning

Abstract

Cropland abandonment is crucial in agricultural management and has a profound impact on crop yield and food security. In recent years, many cropland abandonment identification methods based on remote sensing observation data have been proposed, but most of these methods are based on coarse-resolution images and use traditional machine learning methods for simple identification. To this end, we perform abandonment recognition on high-resolution remote sensing images. According to the texture features of the abandoned land, we combine the method of statistical texture learning and propose a new deep learning framework called PSPNet-STL. The model integrates high-level semantic feature extraction and deep mining of low-level texture features to identify cropland abandonment. First, we labeled the abandoned cropland area and built the HRAC dataset, a high-resolution cropland abandonment dataset. Secondly, we improved PSPNet by fusing statistical texture learning modules to learn multiple texture information on lowlevel feature maps, and combined high-level semantic features for cropland abandonment recognition. Experiments are performed on the HRAC dataset. Compared with other methods, the proposed model has the best performance on this dataset, both in terms of accuracy and visualization, proving that deep mining of low-level statistical texture features is beneficial for crop abandonment recognition.

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

“…Feature fusion-based approaches [5,[17][18][19][20][21][22][23][24][25][26] enhance the representation of cropland information by supplementing additional feature information to the model. Considering the difficulty in labeling the existing high-resolution remote sensing image samples, [17] utilized the existing medium-resolution remote sensing images as a priori knowledge to provide cross-scale relocatable samples for HR images, thus obtaining more effective high-resolution farmland samples.…”

“…[22] proposed a boundary-enhanced segmentation network, HBRNet, with Swing-Transformer as the backbone of the pyramid hierarchy to obtain contextual information while enhancing boundary details. Aiming at the different texture features of plots, [24] proposed a pyramid scene parsing network-statistical texture learning deep learning framework that combines high-level semantic feature extraction with low-level texture feature deep mining to achieve more accurate farmland recognition. [5] proposed to encode parcel features by transformer module and null convolution module, which operates on multi-scale features at the feature extraction order, which in turn improves the ability to capture the details and boundaries of farmland parcels.…”

“…The dataset includes five foreground classes (impervious surfaces, buildings, low vegetation, trees, and cars) and one background class (clutter). For the specific experiments, we tested using image patches with IDs 2,4,6,8,10,12,14,16,20,22,24,27,29,31,33,35,and 38 for training, and used image patch with ID 30 for validation. The remaining 15 image patches were used for training.…”

“…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. (2) Feature-based approaches [5,[17][18][19][20][21][22][23][24][25][26] enhance the representation and understanding of cropland information by supplementing the model with additional features. However, some redundant feature representations do not provide positive images to the model while increasing the computational burden of the model.…”

Object Identification in Land Parcels Using a Machine Learning Approach

“…One might think that the detection of NEAs i cusing on parcel delineation. A number of articles (e.g., [15][16][17][18][19][20][21][22]) hav lem, instituting the detection of a parcel boundary utilizing differe pecially CNN [15][16][17][18][19][20][21]. However, these studies focused on the outer cels and paid minor attention to the objects located on the parcels (w aries).…”