Remote Sensing Image Classification Method Research Articles

Remote sensing image classification method based on improved ShuffleNet convolutional neural network

As a list of remotely sensed data sources is available, the effective processing of remote sensing images is of great significance in practical applications in various fields. This paper proposes a new lightweight network to solve the problem of remote sensing image processing by using the method of deep learning. Specifically, the proposed model employs ShuffleNet V2 as the backbone network, appropriately increases part of the convolution kernels to improve the classification accuracy of the network, and uses the maximum overlapping pooling layer to enhance the detailed features of the input images. Finally, Squeeze and Excitation (SE) blocks are introduced as the attention mechanism to improve the architecture of the network. Experimental results based on several multisource data show that our proposed network model has a good classification effect on the test samples and can achieve more excellent classification performance than some existing methods, with an accuracy of 91%, and can be used for the classification of remote sensing images. Our model not only has high accuracy but also has faster training speed compared with large networks and can greatly reduce computation costs. The demo code of our proposed method will be available at https://github.com/li-zi-qi.

Multi-scale fusion for few-shot remote sensing image classification

ABSTRACT Few-shot image classification is currently valuable research in the field of remote sensing image applications. The difficulty in obtaining training data for a large number of labelled remote sensing images leads to difficulties in applying traditional deep learning-based remote sensing image classification methods. Existing few-shot remote sensing image classification methods usually use a large number of base datasets to train network models in the pre-training phase and perform few-shot classification tasks after model fine-tuning on new datasets in the meta-testing phase. However, an insufficient number of remote sensing images lead to inaccurate features being extracted by the model, and the large amount of new class data for fine-tuning is also not easily accessible. In addition, using a pre-trained model to extract features from the new dataset will create a”negative transfer” problem. In this paper, we aim to address the above challenges in two ways. First, we use a subset of ImageNet, a readily available few-shot natural image dataset, for model pre-training, and eliminate the fine-tuning operation to simulate a real-world application scenario where remote sensing data is extremely scarce. Second, due to the significant differences in scale and style between the ImageNet dataset and the remote sensing dataset, which lead to a serious negative transfer problem in the model, we design a multi-scale feature fusion module to comprehensively decide the labels of query samples considering all scales to compensate for the scale differences and alleviate the”negative transfer” problem. We conducted experiments on four benchmark remote sensing datasets and achieved satisfactory performance.

Dynamic Loss Reweighting Method Based on Cumulative Classification Scores for Long-Tailed Remote Sensing Image Classification

Convolutional neural networks have been widely used in remote sensing classification and achieved quite good results. Most of these methods are based on datasets with relatively balanced samples, but such ideal datasets are rare in applications. Long-tailed datasets are very common in practice, and the number of samples among categories in most datasets is often severely uneven and leads to bad results, especially in the category with a small sample number. To address this problem, a novel remote sensing image classification method based on loss reweighting for long-tailed data is proposed in this paper to improve the classification accuracy of samples from the tail categories. Firstly, abandoning the general weighting approach, the cumulative classification scores are proposed to construct category weights instead of the number of samples from each category. The cumulative classification score can effectively combine the number of samples and the difficulty of classification. Then, the imbalanced information of samples from each category contained in the relationships between the rows and columns of the cumulative classification score matrix is effectively extracted and used to construct the required classification weights for samples from different categories. Finally, the traditional cross-entropy loss function is improved and combined with the category weights generated in the previous step to construct a new loss reweighting mechanism for long-tailed data. Extensive experiments with different balance ratios are conducted on several public datasets, such as HistAerial, SIRI-WHU, NWPU-RESISC45, PatternNet, and AID, to verify the effectiveness of the proposed method. Compared with other similar methods, our method achieved higher classification accuracy and stronger robustness.

Image classification of hyperspectral remote sensing using semi-supervised learning algorithm.

Hyperspectral images contain abundant spectral and spatial information of the surface of the earth, but there are more difficulties in processing, analyzing, and sample-labeling these hyperspectral images. In this paper, local binary pattern (LBP), sparse representation and mixed logistic regression model are introduced to propose a sample labeling method based on neighborhood information and priority classifier discrimination. A new hyperspectral remote sensing image classification method based on texture features and semi-supervised learning is implemented. The LBP is employed to extract features of spatial texture information from remote sensing images and enrich the feature information of samples. The multivariate logistic regression model is used to select the unlabeled samples with the largest amount of information, and the unlabeled samples with neighborhood information and priority classifier discrimination are selected to obtain the pseudo-labeled samples after learning. By making full use of the advantages of sparse representation and mixed logistic regression model, a new classification method based on semi-supervised learning is proposed to effectively achieve accurate classification of hyperspectral images. The data of Indian Pines, Salinas scene and Pavia University are selected to verify the validity of the proposed method. The experiment results have demonstrated that the proposed classification method is able to gain a higher classification accuracy, a stronger timeliness, and the generalization ability.

Remote Sensing Image Classification Method Based on Fusion of CNN and Transformer

Remote Sensing Image Classification Method Based on Fusion of CNN and Transformer

An Early Warning Intelligent Algorithm System for Forest Resource Management and Monitoring.

The development of remote sensing technology has passed an effective means for forest resource management and monitoring, but remote sensing technology is limited by sensor hardware equipment, and the quality of remote sensing image data is low, which is difficult to meet the needs of forest resource change monitoring. This paper presents a remote sensing image classification method based on the combination of the SSIF algorithm and wavelet denoising. Forest information is extracted from PALSAR/PALSAR-2 radar remote sensing data. The forest distribution map is generated by pixel level fusion algorithm, and the accuracy of the forest distribution map is evaluated by a confusion matrix. The remote sensing image is spatio-temporal fused by the SSIF algorithm to capture more details of forest distribution. The simulation analysis shows that the overall accuracy of the forest classification results obtained by the fusion algorithm is 96% ± 1, and the kappa coefficient is 0.66. The accuracy of forest recognition meets the requirements.

Landscape Classification Method Using Improved U-Net Model in Remote Sensing Image Ecological Environment Monitoring System.

Aiming at the problems of low classification accuracy and time-consuming properties in traditional remote sensing image classification methods, a remote sensing image classification method of ecological garden landscape based on improved U-Net model is proposed. Firstly, the remote sensing images of ecological garden landscape are collected by s185 multirotor unmanned aerial vehicle (UAV) system and preprocessed by min-max standardization and data enhancement. Then, the asymmetric convolution block and attention mechanism are used to improve the U-Net model to form the Att-Unet network model, so as to overcome the problems of easy overfitting of the model and incomplete small target detection. Finally, the fully connected conditional random field is introduced into the classification postprocessing to refine the segmentation results. Based on the Keras learning framework, the proposed method is experimentally demonstrated. The results show that the recall, precision, F1 value, and accuracy of the proposed method in the remote sensing image of ecological garden landscape are 0.854, 0.801, 0.836, and 0.982, respectively, and the classification test time is 8.9s. The overall performance is better than other comparison methods, which can provide theoretical support for the dynamic monitoring of the development of ecological garden.

Land Resource Use Classification Using Deep Learning in Ecological Remote Sensing Images

Aiming at the problems that the traditional remote sensing image classification methods cannot effectively integrate a variety of deep learning features and poor classification performance, a land resource use classification method based on a convolutional neural network (CNN) in ecological remote sensing images is proposed. In this study, a seven-layer convolution neural network is constructed, and then the two fully connected layer features of the improved CNN network training output are fused with the fifth layer pooled layer features after dimensionality reduction by principal component analysis (PCA), so as to obtain an effective remote sensing image feature of land resources based on deep learning. Further, the classification of land resources remote sensing images is completed based on a support vector machine classifier. The remote sensing images of Pingshuo mining area in Shanxi Province are used to analyze the proposed method. The results show that the edge of the recognized image is clear, the classification accuracy, misclassification rate, and kappa coefficient are 0.9472, 0.0528, and 0.9435, respectively, and the model has excellent overall performance and good classification effect.

(Retracted) Application of remote sensing image ecological land classification method in crop planting area extraction under the background of data fusion

Data mining is a common basic function in humans and many other intellectual systems. The popularization of computers has created a good research environment for the advanced application of remote sensing satellites. With China’s economic and population growth, the demand for food is also increasing. Obtaining the planting area is one of the most important tasks in calculating harvest costs. However, traditional cost analysis methods lack information. The traditional remote sensing image classification method is based on the pixel classification method, which cannot effectively extract the spatial texture information in an image. The pixel-based classification method also has the problem of salt and pepper phenomenon in the classification results, which generates a large number of invalid broken patches and ultimately leads to low classification accuracy. Here, we investigated the application of remote state system mapping for the extraction of the planting area. We analyzed the basic principles of remote image processing and remote image sharing methods and introduced an audio system and optical algorithm. Here, two separation methods are selected, long-distance traction measurements are performed in three training areas, and the two results are compared and analyzed. Experimental results show that the method of objective orientation increases the working speed compared with the method of manuscript translation. The benefit of speed measurement becomes more apparent in both seasons as the measuring range increases. Computer automatic classification and the method of artificial visual interpretation are tested sequentially, and the total time taken is 49 and 13.5 days, respectively.

Hyperspectral Data Classification Algorithm considering Spatial Texture Features

As a cutting-edge technology, hyperspectral remote sensing has been widely applied in many fields, including agricultural production, mineral identification, target detection, disaster warning, military reconnaissance, and urban planning. The collected hyperspectral data have high spectral resolution and spatial resolution and are characterized by a large amount of information, redundancy, and high dimension. At the same time, there is a strong correlation between the bands. Therefore, hyperspectral data not only provides rich information but also brings great challenges for subsequent processing. Hyperspectral image classification is a hot issue in remote sensing information processing. Traditional hyperspectral remote sensing image classification methods only use the spectral features of the image without considering the spatial features of each pixel in the hyperspectral remote sensing image. In this paper, a hyperspectral image classification method is proposed not only considering spectral features but also considering texture features. This method jointly considers both these features. Firstly, six texture features contributing a lot to each pixel of hyperspectral remote sensing image are extracted by using a gray level cooccurrence matrix, and then, the spectral features of each pixel in neighbor are combined to form the texture-spectral features. Finally, the classification experiment of the Indian Pines and Pavia University scene is carried out based on a support vector machine and extreme random tree algorithm, and the obtained results show that the proposed method achieves higher classification performance than the traditional method.

Dynamic Evolution Analysis of Desertification Images Based on BP Neural Network

In recent years, with the rise of artificial intelligence, deep neural network models have been used in various image recognition researches. Land desertification is a major environmental problem facing the world at present, and how to do a good job in dynamic monitoring is particularly important. For remote sensing images, this paper constructs a GA-PSO-BP analysis model based on BP neural network, genetic algorithm, and particle swarm algorithm and compares the classification training accuracies of the four models of BP, GA-BP, PSO-BP, and GA-PSO-BP; GA-PSO-BP was selected for dynamic analysis of desertification images, and the results showed the following: (1) By comparing the regional classification training accuracies of the four models of BP, GA-BP, PSO-BP, and GA-PSO-BP, the GA-PSO-BP neural network remote sensing image classification method proposed in this paper is simple and easy to operate. Compared with traditional remote sensing image classification methods and traditional neural network classification methods, the classification accuracy of remote sensing effects is improved. (2) Carrying out desertification analysis on remote sensing images of Horqin area, from 2010 to 2015, the desertified land area in the test area increased by 1.56 km2; from 2015 to 2020, the desertified land area in the test area decreased by 1.131 km2, and the desertified land in the test area from 2010 to 2020 showed a trend of increasing first and then decreasing, which is consistent with the actual situation. The GA-PSO-BP remote sensing image classification model has a good performance portability.

DKDFN: Domain Knowledge-Guided deep collaborative fusion network for multimodal unitemporal remote sensing land cover classification

Land use and land cover maps provide fundamental information that has been used in different types of studies, ranging from public health to carbon cycling. However, the existing remote sensing image classification methods thus far suffer from the insufficient usage of multiple modalities, underconsideration of prior domain knowledge, and poor performance on minority classes. To alleviate these problems, we propose a novel domain knowledge-guided deep collaborative fusion network (DKDFN) with performance boosting for minority categories for land cover classification. More specifically, the DKDFN adopts a multihead encoder and a multibranch decoder structure. The architecture of the encoder probablizes sufficient mining of complementary information from multiple modalities, which are Sentinel-2, Sentinel-1, and SRTM Digital Elevation Data (SRTM) in our case. The multibranch decoder enables land cover classification in a multitask learning setup, performing semantic segmentation and reconstructing multimodal remote sensing indices, which are selected as representatives of domain knowledge. This design incorporates domain knowledge in an effective end-to-end manner. The training stage of our DKDFN is supervised by our proposed asymmetry loss function (ALF), which boosts performance on nearly all categories, especially the categories with a low frequency of occurrence. Ablation studies of the network suggest that our design logic is worth testing in any network with an encoder-decoder structure. The study is conducted in Hunan, China and is verified using a self-labeled multimodal unitemporal remote sensing image dataset. The comparative experiments between DKDFN and 6 state-of-the-art models (U-Net, SegNet, PSPNet, DeepLab, HRNet, MP-ResNet) testify to the superiority of our method and suggest its potential to be applied more widely to map land cover in other geographical areas given the availability of Sentinel-2, Sentinel-1, and SRTM data. The dataset can be downloaded by https://github.com/LauraChow/HunanMultimodalDataset.

Damage characteristics of the Qinghai-Tibet Highway in permafrost regions based on UAV imagery

ABSTRACT For the particular engineering-geological conditions and natural environment in permafrost regions, the highway has a high damage ratio during the operation. In warm and ice-rich permafrost regions, highway pavement and embankment have poor serviceability, which threatens driving the vehicle and safe operation. Traditional field surveys on highway damage cannot entirely, rapidly and precisely obtain the distress information along the whole road. In this paper, the Qinghai-Tibet Highway (QTH) pavement distress was investigated in 550-km-long permafrost regions based on the field surveys in 2014 and 2019 and the unmanned aerial vehicle (UAV) imagery. The type and damage ratio of distress were extracted by the remote sensing image classification method. The pavement roughness and embankment height were acquired by the spatial analysis of geographical information system. This paper provides a novel insight and efficient method for the distress investigation and exploration of distress formation in permafrost region, which can be applied in other similar engineering projects in the cold and inclement permafrost region. Furthermore, this paper also presents valuable and first-hand field data for evaluating highway serviceability and prevention of road damage during operation and maintenance stages.

Big data classification of remote sensing image based on cloud computing and convolutional neural network

In recent years, with the continuous development of cloud computing and big data technology, information technology is penetrating all corners of enterprise development at a speed that ordinary people cannot imagine. Today's remote sensing image classification plays an important role in many applications. This article is based on cloud computing and convolutional neural network to study the remote sensing image big data classification. The article aims to fully focus on cloud computing technology and convolutional neural network technology to carry out specific application research, on this basis, to obtain a more convenient, accurate and efficient remote sensing image classification method, and to prove the feasibility of this method. This article takes cloud computing technology and convolutional neural network technology as the technical points. First, it introduces cloud computing technology in more detail from the basic concepts, characteristics and classification of cloud computing and then compares traditional methods from the selection of feature extraction methods and classifiers. The remote sensing image classification method is described, and finally, the convolutional neural network model that needs to be applied in this research is explained. In the experiment, the features extracted by multiple pre-training networks are fused. Through the study of the three feature fusion methods, it is found that the classification accuracy rate has been further improved on the three data sets. The experimental results in this paper show that the accuracy of the traditional method is below 80%, while the feature extraction of the pre-trained network has an accuracy of more than 90% on the UCMERCE data; it combines the three pre-trained networks of ResNet-50, ResNet-101 and ResNet-152, an increase of more than 2.5%.

Research on remote sensing image classification method using two-stream convolutional neural network

Research on remote sensing image classification method using two-stream convolutional neural network

Research on remote sensing image classification method using two-stream convolutional neural network

Research on remote sensing image classification method using two-stream convolutional neural network

Landslide Extraction from High-Resolution Remote Sensing Imagery Using Fully Convolutional Spectral–Topographic Fusion Network

Considering the complexity of landslide hazards, their manual investigation lacks efficiency and is time-consuming, especially in high-altitude plateau areas. Therefore, extracting landslide information using remote sensing technology has great advantages. In this study, comprehensive research was carried out on the landslide features of high-resolution remote sensing images on the Mangkam dataset. Based on the idea of feature-driven classification, the landslide extraction model of a fully convolutional spectral–topographic fusion network (FSTF-Net) based on a deep convolutional neural network of multi-source data fusion is proposed, which takes into account the topographic factor (slope and aspect) and the normalized difference vegetation index (NDVI) as multi-source data input by which to train the model. In this paper, a high-resolution remote sensing image classification method based on a fully convolutional network was used to extract the landslide information, thereby realizing the accurate extraction of the landslide and surrounding ground-object information. With Mangkam County in the southeast of the Qinghai–Tibet Plateau China as the study area, the proposed method was evaluated based on the high-precision digital elevation model (DEM) generated from stereoscopic images of Resources Satellite-3 and multi-source high-resolution remote sensing image data (Beijing-2, Worldview-3, and SuperView-1). Results show that our method had a landslide detection precision of 0.85 and an overall classification accuracy of 0.89. Compared with the latest DeepLab_v3+, our model increases the landslide detection precision by 5%. Thus, the proposed FSTF-Net model has high reliability and robustness.

Research on Classification of Remote Sensing Images Based on Artificial Intelligence

With the rapid development of image processing technology, remote sensing technology has received increasing attention. Relying on artificial intelligence technology and using the advantages of principal component analysis (PCA) to reduce the dimensionality of features, this paper proposes a remote sensing image classification method based on SVM. First, LBP operator is used to extract remote sensing image features, and then PCA is used to perform remote sensing image features. The dimensionality reduction process reduces the feature dimensionality and eliminates feature redundant information, and obtains features that have a large contribution to the classification result. Finally, SVM is used for remote sensing image classification. The results show that PCA-SVM improves the efficiency and accuracy of remote sensing image classification.

Simulation of urban pattern evolution trend based on satellite GIS and remote sensing

Existing hyperspectral remote sensing image classification methods have separate feature extraction and classifiers. At the same time, separate training will increase network complexity and be very time-consuming. Therefore, a deep learning space-spectrum combined with hyperspectral remote sensing image classification algorithm is proposed. First, mathematical transformation is used to stabilize the growth rate of remote sensing image radiation value, and reduce the standard deviation of spatial distribution characteristics of GIS information DN value, so as to increase the growth rate of remote sensing spectral radiation value in suburban areas. Then, a band combination strategy LSTM algorithm that focuses on global features is selected to regroup the bands of the spectral vector of each pixel of the hyperspectral data, which can effectively extract the context features between adjacent spectra. Finally, principal component analysis is performed on the satellite remote sensing image, and retain the first few principal components to achieve dimensionality reduction; the multiscale convolutional neural network is applied to extract the spatial features of the satellite remote sensing image after dimensionality reduction. The end-to-end structure is used to extract spectral features and spatial features simultaneously to realize satellite remote sensing image analysis. The simulation experiment proves that the calculation accuracy of the urban pattern change trend of the algorithm in this paper is high, and the calculation results are more convergent, which can provide more accurate trends of the urban pattern change and play a guiding role in the future construction of the city.

Improved U-Net model for remote sensing image classification method based on distributed storage

Aiming at the low efficiency of traditional methods for the management and classification of massive remote sensing image data, a mass remote sensing image classification method based on distributed storage is proposed. The aim is to obtain near real-time image classification in mobile devices or internet applications. In this paper, we designed two levels of an image processing structure. A distributed file system is taken as the underlying storage architecture to efficiently manage and query massive remote sensing images. The upper layer uses a GPU server to train the remote sensing image classification model to improve the classification accuracy. To improve the classification accuracy, we add two parameters to adjust the data of the current layer in U-Net. The experimental results show that the proposed method based on distributed storage has a high degree of scalability, and it has a short processing time while maintaining a high classification accuracy for remote sensing images.