Image Feature Fusion Based Remote Sensing Scene Zero-Shot Classification Algorithm

In view of the traditional change detection algorithm mainly depends on the spectral information image spot, failed to effectively mining and fusion of multi-image feature detection advantage, the article borrows the ideas of object oriented analysis proposed a multi feature fusion of remote sensing image change detection algorithm. First by the multi-scale segmentation of image objects based; then calculate the various objects of color histogram and linear gradient histogram; utilizes the color distance and edge line feature distance between EMD statistical operator in different periods of the object, using the adaptive weighted method, the color feature distance and edge in a straight line distance of combination is constructed object heterogeneity. Finally, the curvature histogram analysis image spot change detection results. The experimental results show that the method can fully fuse the color and edge line features, thus improving the accuracy of the change detection.

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Full Convolutional Neural Network Based on Multi-Scale Feature Fusion for the Class Imbalance Remote Sensing Image Classification

Remote Sensing ◽

10.3390/rs12213547 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3547 ◽

Cited By ~ 2

Author(s):

Yuanyuan Ren ◽

Xianfeng Zhang ◽

Yongjian Ma ◽

Qiyuan Yang ◽

Chuanjian Wang ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

High Resolution ◽

Spatial Information ◽

Feature Fusion ◽

Remote Sensing Image ◽

Machine Learning Algorithms ◽

Image Feature ◽

Remote Sensing Images ◽

Land Covers

Remote sensing image segmentation with samples imbalance is always one of the most important issues. Typically, a high-resolution remote sensing image has the characteristics of high spatial resolution and low spectral resolution, complex large-scale land covers, small class differences for some land covers, vague foreground, and imbalanced distribution of samples. However, traditional machine learning algorithms have limitations in deep image feature extraction and dealing with sample imbalance issue. In the paper, we proposed an improved full-convolution neural network, called DeepLab V3+, with loss function based solution of samples imbalance. In addition, we select Sentinel-2 remote sensing images covering the Yuli County, Bayingolin Mongol Autonomous Prefecture, Xinjiang Uygur Autonomous Region, China as data sources, then a typical region image dataset is built by data augmentation. The experimental results show that the improved DeepLab V3+ model can not only utilize the spectral information of high-resolution remote sensing images, but also consider its rich spatial information. The classification accuracy of the proposed method on the test dataset reaches 97.97%. The mean Intersection-over-Union reaches 87.74%, and the Kappa coefficient 0.9587. The work provides methodological guidance to sample imbalance correction, and the established data resource can be a reference to further study in the future.

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SAFFNet: Self-Attention-Based Feature Fusion Network for Remote Sensing Few-Shot Scene Classification

Remote Sensing ◽

10.3390/rs13132532 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2532

Author(s):

Joseph Kim ◽

Mingmin Chi

Keyword(s):

Remote Sensing ◽

Large Scale ◽

Feature Fusion ◽

Feature Weighting ◽

Training Dataset ◽

Scene Classification ◽

Remote Sensing Classification ◽

Scale Feature ◽

Multi Scale ◽

Shot Classification

In real applications, it is necessary to classify new unseen classes that cannot be acquired in training datasets. To solve this problem, few-shot learning methods are usually adopted to recognize new categories with only a few (out-of-bag) labeled samples together with the known classes available in the (large-scale) training dataset. Unlike common scene classification images obtained by CCD (Charge-Coupled Device) cameras, remote sensing scene classification datasets tend to have plentiful texture features rather than shape features. Therefore, it is important to extract more valuable texture semantic features from a limited number of labeled input images. In this paper, a multi-scale feature fusion network for few-shot remote sensing scene classification is proposed by integrating a novel self-attention feature selection module, denoted as SAFFNet. Unlike a pyramidal feature hierarchy for object detection, the informative representations of the images with different receptive fields are automatically selected and re-weighted for feature fusion after refining network and global pooling operation for a few-shot remote sensing classification task. Here, the feature weighting value can be fine-tuned by the support set in the few-shot learning task. The proposed model is evaluated on three publicly available datasets for few shot remote sensing scene classification. Experimental results demonstrate the effectiveness of the proposed SAFFNet to improve the few-shot classification accuracy significantly compared to other few-shot methods and the typical multi-scale feature fusion network.

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Research on Environmental Sound Classification Algorithm Based on Multi-feature Fusion

2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC) ◽

10.1109/itaic49862.2020.9338926 ◽

2020 ◽

Author(s):

Ruixue Li ◽

Bo Yin ◽

Yongchao Cui ◽

Zehua Du ◽

Kexin Li

Keyword(s):

Feature Fusion ◽

Classification Algorithm ◽

Environmental Sound ◽

Sound Classification

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Attention and Feature Fusion SSD for Remote Sensing Object Detection

IEEE Transactions on Instrumentation and Measurement ◽

10.1109/tim.2021.3052575 ◽

2021 ◽

Vol 70 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Xiaocong Lu ◽

Jian Ji ◽

Zhiqi Xing ◽

Qiguang Miao

Keyword(s):

Remote Sensing ◽

Object Detection ◽

Feature Fusion

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A Multi-Branch Feature Fusion Strategy Based on an Attention Mechanism for Remote Sensing Image Scene Classification

Remote Sensing ◽

10.3390/rs13101950 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1950

Author(s):

Cuiping Shi ◽

Xin Zhao ◽

Liguo Wang

Keyword(s):

Remote Sensing ◽

Feature Extraction ◽

Classification Accuracy ◽

Feature Fusion ◽

State Of The Art ◽

Rapid Development ◽

Remote Sensing Image ◽

Classification Performance ◽

Attention Mechanism ◽

Scene Classification

In recent years, with the rapid development of computer vision, increasing attention has been paid to remote sensing image scene classification. To improve the classification performance, many studies have increased the depth of convolutional neural networks (CNNs) and expanded the width of the network to extract more deep features, thereby increasing the complexity of the model. To solve this problem, in this paper, we propose a lightweight convolutional neural network based on attention-oriented multi-branch feature fusion (AMB-CNN) for remote sensing image scene classification. Firstly, we propose two convolution combination modules for feature extraction, through which the deep features of images can be fully extracted with multi convolution cooperation. Then, the weights of the feature are calculated, and the extracted deep features are sent to the attention mechanism for further feature extraction. Next, all of the extracted features are fused by multiple branches. Finally, depth separable convolution and asymmetric convolution are implemented to greatly reduce the number of parameters. The experimental results show that, compared with some state-of-the-art methods, the proposed method still has a great advantage in classification accuracy with very few parameters.

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DFFAN: Dual Function Feature Aggregation Network for Semantic Segmentation of Land Cover

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10030125 ◽

2021 ◽

Vol 10 (3) ◽

pp. 125

Author(s):

Junqing Huang ◽

Liguo Weng ◽

Bingyu Chen ◽

Min Xia

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Spatial Information ◽

Feature Fusion ◽

Semantic Segmentation ◽

Dual Function ◽

Context Information ◽

Remote Sensing Images ◽

Feature Aggregation ◽

Image Context

Analyzing land cover using remote sensing images has broad prospects, the precise segmentation of land cover is the key to the application of this technology. Nowadays, the Convolution Neural Network (CNN) is widely used in many image semantic segmentation tasks. However, existing CNN models often exhibit poor generalization ability and low segmentation accuracy when dealing with land cover segmentation tasks. To solve this problem, this paper proposes Dual Function Feature Aggregation Network (DFFAN). This method combines image context information, gathers image spatial information, and extracts and fuses features. DFFAN uses residual neural networks as backbone to obtain different dimensional feature information of remote sensing images through multiple downsamplings. This work designs Affinity Matrix Module (AMM) to obtain the context of each feature map and proposes Boundary Feature Fusion Module (BFF) to fuse the context information and spatial information of an image to determine the location distribution of each image’s category. Compared with existing methods, the proposed method is significantly improved in accuracy. Its mean intersection over union (MIoU) on the LandCover dataset reaches 84.81%.

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