Mono- and Dual-Regulated Contractive-Expansive-Contractive Deep Convolutional Networks for Classification of Multispectral Remote Sensing Images

Deep convolutional neural networks (CNNs) are the state-of-the-art methods in the domain of classification of remote sensing (RS) data. However, traditional CNN models suffer from huge computational costs in learning land-use and land-cover features, particularly in large-scale RS problems. To address this issue, we propose a reliable mono- and dual-regulated contractive-expansive-contractive (MRCEC/DRCEC) CNN for scene based multispectral (MS) image classification. The proposed technique increases the accuracy of learning and minimizes the loss in the feature maps by incorporating the CEC approach in the classification. Extensive experiments conducted on the Sentinel-2 EuroSATallbands dataset pointed out that the proposed model outperforms the state-of-the-art models such as EfficientNet-B0, RESNet-50, and EfficientNet-B7.