Large-scale land use/land cover extraction from Landsat imagery using feature relationships matrix based deep-shallow learning

Deep learning has demonstrated its effectiveness in capturing high-level features, with convolutional neural networks (CNNs) excelling in remote sensing classification. However, CNNs encounter challenges when applied to Landsat images with limited multi-spectral bands, as they struggle to learn stable features from the spectral domain and integrate them with spatial features to enhance accuracy. Additionally, most CNN applications focus on learning features directly from the raw image, making them susceptible to spectral environment changes. To overcome these limitations, this paper introduces a novel approach for large-scale Land Use/Land Cover (LULC) extraction from Landsat OLI images. The proposed classification architecture comprises two modules. The first module utilizes a feature relationships matrix to generate an extent feature map (EFM), and a specifically designed CNN structure learns deep features from the EFM and spatial domain. In the second module, a multiple classifiers system (MCS) is employed to obtain shallow learning features, which are further enhanced by another CNN structure through continued learning. The combined features from these modules contribute to improved classification of remote sensing images. Experimental results demonstrate that our proposed method effectively acquires stable features for training deep learning models with strong generalization ability. It exhibits significant advancements in accuracy improvement and large-scale LULC extraction in the Yangtze River Economic Belt (YREB) in China, surpassing comparative approaches based on deep learning and non-deep learning methods.