Advanced image segmentation for precision agriculture using CNN-GAT fusion and fuzzy C-means clustering

Abstract

In recent years, the use of convolutional neural networks (CNNs) and graph convolutional networks (GCNs) has significantly advanced hyperspectral image classification (HSIC). Despite these achievements, the challenge of limited labeled samples remains a critical obstacle when employing CNNs and GCNs for hyperspectral image classification. Agricultural images often face challenges due to high spectral variability and complex spatial patterns, making accurate classification difficult. Additionally, the presence of noise and limited labeled data further complicates the analysis and interpretation of these images. Although graph convolution networks and their predecessors have greatly advanced the use of spatial relationship features, deep learning algorithms, such as convolutional neural networks (CNNs), have reduced the need for and reliance on a high number of samples through spatial feature learning and consideration. Therefore, to advance the field, a novel approach termed “dimension reduction fuzzy graph network” (DRFG) was designed. This approach is a combination of deep fuzzy-based DR, enhanced with 3D-CNN and GATs, with the application of principal component analysis (PCA) for optimized DR. The DRFG model entails two major processing stages. The initial stage involves the classification of the raw data cube using the 3D-CNN. In the second stage, the results are processed by means of an algorithm enriched by lightweight GAT-based modules. The DRFG model combines morphological features selection from fuzzy C-means (FCM) clustering and optimized DR by using PCA. Thus, the model employs the best of PCA and GATs in order to allow for optimized classification. At high-performance optimal DR, the DRFG model offers optimal multispectral imaging as well as the analysis and classification of hyperspectral data, which is sufficiently promising so as to advance the field’s needs for precision agriculture.