Remote Sensing Scene Classification is a challenging and valuable research
topic, in which Convolutional Neural Network (CNN) has played a crucial role.
CNN can extract hierarchical convolutional features from remote sensing
imagery, and Feature Fusion of different layers can enhance CNN's performance.
Two successful Feature Fusion methods, Add and Concat, are employed in certain
state-of-the-art CNN algorithms. In this paper, we propose a novel Feature
Fusion algorithm, which unifies the aforementioned methods using the Kronecker
Product (KPFF), and we discuss the Backpropagation procedure associated with
this algorithm. To validate the efficacy of the proposed method, a series of
experiments are designed and conducted. The results demonstrate its
effectiveness of enhancing CNN's accuracy in Remote sensing scene
classification.

远程感知场景分类是一个具有挑战性和有价值的研究课题，其中卷积神经网络在其中发挥着至关重要的作用。本文提出了一种利用 Kronecker 积的新型特征融合算法，并讨论了与该算法相关的反向传播过程，实验证明了该方法在远程感知场景分类中提高卷积神经网络准确性的有效性。

卷积神经网络在遥感场景分类中的 Kronecker 乘积特征融合

Kronecker Product Feature Fusion for Convolutional Neural Network in  Remote Sensing Scene Classification

Due to their ability to offer more comprehensive information than data from a
single view, multi-view (multi-source, multi-modal, multi-perspective, etc.)
data are being used more frequently in remote sensing tasks. However, as the
number of views grows, the issue of data quality becomes more apparent,
limiting the potential benefits of multi-view data. Although recent deep neural
network (DNN) based models can learn the weight of data adaptively, a lack of
research on explicitly quantifying the data quality of each view when fusing
them renders these models inexplicable, performing unsatisfactorily and
inflexible in downstream remote sensing tasks. To fill this gap, in this paper,
evidential deep learning is introduced to the task of aerial-ground dual-view
remote sensing scene classification to model the credibility of each view.
Specifically, the theory of evidence is used to calculate an uncertainty value
which describes the decision-making risk of each view. Based on this
uncertainty, a novel decision-level fusion strategy is proposed to ensure that
the view with lower risk obtains more weight, making the classification more
credible. On two well-known, publicly available datasets of aerial-ground
dual-view remote sensing images, the proposed approach achieves
state-of-the-art results, demonstrating its effectiveness. The code and
datasets of this article are available at the following address:
this https URL.

本研究使用证据深度学习模型对多角度遥感数据进行了分类，该模型可有效地计算每个角度数据的可信度，并据此进行决策级融合，从而提高分类结果的准确性。实验结果表明，该方法在公开数据集上取得了最佳效果。

利用空中和地面双视图图像的证据融合进行可信遥感场景分类

Credible Remote Sensing Scene Classification Using Evidential Fusion on Aerial-Ground Dual-view Images

Designing discriminative powerful texture features robust to realistic
imaging conditions is a challenging computer vision problem with many
applications, including material recognition and analysis of satellite or
aerial imagery. In the past, most texture description approaches were based on
dense orderless statistical distribution of local features. However, most
recent approaches to texture recognition and remote sensing scene
classification are based on Convolutional Neural Networks (CNNs). The d facto
practice when learning these CNN models is to use RGB patches as input with
training performed on large amounts of labeled data (ImageNet). In this paper,
we show that Binary Patterns encoded CNN models, codenamed TEX-Nets, trained
using mapped coded images with explicit texture information provide
complementary information to the standard RGB deep models. Additionally, two
deep architectures, namely early and late fusion, are investigated to combine
the texture and color information. To the best of our knowledge, we are the
first to investigate Binary Patterns encoded CNNs and different deep network
fusion architectures for texture recognition and remote sensing scene
classification. We perform comprehensive experiments on four texture
recognition datasets and four remote sensing scene classification benchmarks:
UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with
7 categories and the recently introduced large scale aerial image dataset (AID)
with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary
information to standard RGB deep model of the same network architecture. Our
late fusion TEX-Net architecture always improves the overall performance
compared to the standard RGB network on both recognition problems. Our final
combination outperforms the state-of-the-art without employing fine-tuning or
ensemble of RGB network architectures.

论文利用二进制模式编码卷积神经网络模型以及不同的深度网络融合架构进行纹理识别和遥感场景分类，并在四个纹理识别数据集和四个遥感场景分类基准测试中进行全面实验，证明该模型提高了识别性能，并超过了现有技术的表现。