Remote Sensing Image Scene Classification Based on Fusion Method

Abstract

Remote sensing image scene classification is a hot research area for its wide applications. More recently, fusion-based methods attract much attention since they are considered to be an useful way for scene feature representation. This paper explores the fusion-based method for remote sensing image scene classification from another viewpoint. First, it is categorized as front side fusion mode, middle side fusion mode, and back side fusion mode. For each fusion mode, the related methods are introduced and described. Then, classification performances of the single side fusion mode and hybrid side fusion mode (combinations of single side fusion) are evaluated. Comprehensive experiments on UC Merced, WHU-RS19, and NWPU-RESISC45 datasets give the comparison result among various fusion methods. The performance comparisons of various modes, and interactions among different fusion modes are also discussed. It is concluded that (1) fusion is an effective way to improve model performance, (2) back side fusion is the most powerful fusion mode, and (3) method with random crop+multiple backbone+average achieves the best performance.

LPIN: A Lightweight Progressive Inpainting Network for Improving the Robustness of Remote Sensing Images Scene Classification

“…Remote Sens. 2022, 14, x FOR PEER REVIEW 2 of 31 vision and pattern recognition [6,7], the classification methods based on Convolutional Neural Network (CNN) [8][9][10][11][12][13][14][15][16][17][18][19] have been widely investigated for they can learn and extract image features automatically. Hu et al [9] and Du et al [10] used a pretrained CNN to extract image features.…”

“…One is the traditional machine learning-based methods with hand-crafted features, such as models based on Bag of Visual Words (BoVW) [2], Randomized Spatial Partition (RSP) [3], Hierarchical Coding Vector (HCV) [4] and Fisher vectors (FVs) [5]. As deep learning technology has been proved to have excellent performance in computer vision and pattern recognition [6,7], the classification methods based on Convolutional Neural Network (CNN) [8][9][10][11][12][13][14][15][16][17][18][19] have been widely investigated for they can learn and extract image features automatically. Hu et al [9] and Du et al [10] used a pretrained CNN to extract image features.…”

“…Hu et al [9] and Du et al [10] used a pretrained CNN to extract image features. Yin et al [11] defined three types of fusion-based models for RSISC and gave typical fusion methods. Li et al [12] proposed a hybrid architecture of a pre-trained model combined with an unsupervised encoding method.…”

Fuzzy Cognitive Maps with Bird Swarm Intelligence Optimization-Based Remote Sensing Image Classification

“…Next, in the meta-training stage, a task-adoptive attention method was developed for producing the task-specific attention that could adoptively choose embedding features amongst the entire task. Yin and others [ 16 ] examined the fusion-based model for RSI scene classification from other viewpoints. First, it is classified into front, middle, and back side fusion modes.…”

Fusion of the Multisource Datasets for Flood Extent Mapping Based on Ensemble Convolutional Neural Network (CNN) Model

“…Advances in remote sensing (RS) technologies and evolution in smart sensors and charge-coupled device cameras providing higher resolution, wider coverage, lower cost, continuous information, and timely revisiting mean that earth observation and its regular monitoring become viable [15][16][17][18]. RS is deployed in many applications such as disaster mapping [19][20][21][22], environment monitoring [23,24], land Journal of Sensors use/cover mapping [25][26][27][28][29][30], and forest mapping [31,32]. Due to improvement of spatial and temporal resolution of satellite imagery and availability of synthetic aperture radar (SAR) dataset, disaster mapping based on RS data has been converted into a hot topic [33,34].…”