HFCC-Net: A Dual-Branch Hybrid Framework of CNN and CapsNet for Land-Use Scene Classification

Abstract

Land-use scene classification (LUSC) is a key technique in the field of remote sensing imagery (RSI) interpretation. A convolutional neural network (CNN) is widely used for its ability to autonomously and efficiently extract deep semantic feature maps (DSFMs) from large-scale RSI data. However, CNNs cannot accurately extract the rich spatial structure information of RSI, and the key information of RSI is easily lost due to many pooling layers, so it is difficult to ensure the information integrity of the spatial structure feature maps (SSFMs) and DSFMs of RSI with CNNs only for LUSC, which can easily affect the classification performance. To fully utilize the SSFMs and make up for the insufficiency of CNN in capturing the relationship information between the land-use objects of RSI, while reducing the loss of important information, we propose an effective dual-branch hybrid framework, HFCC-Net, for the LUSC task. The CNN in the upper branch extracts multi-scale DSFMs of the same scene using transfer learning techniques; the graph routing-based CapsNet in the lower branch is used to obtain SSFMs from DSFMs in different scales, and element-by-element summation achieves enhanced representations of SSFMs; a newly designed function is used to fuse the top-level DSFMs with SSFMs to generate discriminant feature maps (DFMs); and, finally, the DFMs are fed into classifier. We conducted sufficient experiments using HFCC-Net on four public datasets. The results show that our method has better classification performance compared to some existing CNN-based state-of-the-art methods.

Noisy Remote Sensing Scene Classification via Progressive Learning Based on Multiscale Information Exploration

“…The positive results counted on different RS scene data sets demonstrate the model's usefulness. Similarly, other CNN-based models have also been presented to explore the multiscale information from RS scenes for the final tasks, such as [28][29][30][31]. Some few-shot CNN-based models were constructed to reduce the demands for the labeled data.…”

A Novel Deep Learning Architecture for Agriculture Land Cover and Land Use Classification from Remote Sensing Images Based on Network-Level Fusion of Self-Attention Architecture

“…For remote sensing community researchers, new challenges are always opened based on the high-resolution satellite images [1]. Computer vision researchers have extensively utilized remote sensing images for many semantic tasks, including but not limited to road segmentation, building extraction, land cover classification, IOT, and agricultural land classification [2][3][4]. The land cover classification achieved remarkable attention in computer vision due to its essential applications such as urban planning, crop fields, and landslide hazards [3].…”

“…Computer vision researchers have extensively utilized remote sensing images for many semantic tasks, including but not limited to road segmentation, building extraction, land cover classification, IOT, and agricultural land classification [2][3][4]. The land cover classification achieved remarkable attention in computer vision due to its essential applications such as urban planning, crop fields, and landslide hazards [3]. The RS data is not easy to use because several things fall under the same category and might be seen in the same image, i.e., the vegetation category includes forest regions, herbaceous plants, and permanent crops [5].…”