scholarly journals Remote Sensing Sea Ice Image Classification Based on Multilevel Feature Fusion and Residual Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yanling Han ◽  
Pengxia Cui ◽  
Yun Zhang ◽  
Ruyan Zhou ◽  
Shuhu Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Sea Ice ◽  
Image Classification ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Feature Fusion ◽  
Bohai Bay ◽  
Residual Network ◽  
Network Layers

Sea ice disasters are already one of the most serious marine disasters in the Bohai Sea region of our country, which have seriously affected the coastal economic development and residents’ lives. Sea ice classification is an important part of sea ice detection. Hyperspectral imagery and multispectral imagery contain rich spectral information and spatial information and provide important data support for sea ice classification. At present, most sea ice classification methods mainly focus on shallow learning based on spectral features, and the good performance of the deep learning method in remote sensing image classification provides a new idea for sea ice classification. However, the level of deep learning is limited due to the influence of input size in sea ice image classification, and the deep features in the image cannot be fully mined, which affects the further improvement of sea ice classification accuracy. Therefore, this paper proposes an image classification method based on multilevel feature fusion using residual network. First, the PCA method is used to extract the first principal component of the original image, and the residual network is used to deepen the number of network layers. The FPN, PAN, and SPP modules increase the mining between layer and layer features and merge the features between different layers to further improve the accuracy of sea ice classification. In order to verify the effectiveness of the method in this paper, sea ice classification experiments were performed on the hyperspectral image of Bohai Bay in 2008 and the multispectral image of Bohai Bay in 2020. The experimental results show that compared with the algorithm with fewer layers of deep learning network, the method proposed in this paper utilizes the idea of residual network to deepen the number of network layers and carries out multilevel feature fusion through FPN, PAN, and SPP modules, which effectively solves the problem of insufficient deep feature extraction and obtains better classification performance.

scholarly journals Hyperspectral Sea Ice Image Classification Based on the Spectral-Spatial-Joint Feature with Deep Learning

2019 ◽  
Vol 11 (18) ◽  
pp. 2170 ◽  
Author(s):  
Yanling Han ◽  
Yi Gao ◽  
Yun Zhang ◽  
Jing Wang ◽  
Shuhu Yang
Keyword(s):  
Deep Learning ◽  
Sea Ice ◽  
Image Classification ◽  
Spatial Information ◽  
Three Dimensional ◽  
Hyperspectral Data ◽  
Small Samples ◽  
Bohai Bay ◽  
Spatial Features ◽  
3D Cnn

Sea ice is one of the causes of marine disasters. The classification of sea ice images is an important part of sea ice detection. The labeled samples in hyperspectral sea ice image classification are difficult to acquire, which causes minor sample problems. In addition, most of the current sea ice classification methods mainly use spectral features for shallow learning, which also limits further improvement of the sea ice classification accuracy. Therefore, this paper proposes a hyperspectral sea ice image classification method based on the spectral-spatial-joint feature with deep learning. The proposed method first extracts sea ice texture information by the gray-level co-occurrence matrix (GLCM). Then, it performs dimensionality reduction and a correlation analysis of the spectral information and spatial information of the unlabeled samples, respectively. It eliminates redundant information by extracting the spectral-spatial information of the neighboring unlabeled samples of the labeled sample and integrating the information with the spectral and texture data of the labeled sample to further enhance the quality of the labeled sample. Lastly, the three-dimensional convolutional neural network (3D-CNN) model is designed to extract the deep spectral-spatial features of sea ice. The proposed method combines relevant textural features and performs spectral-spatial feature extraction based on the 3D-CNN model by using a large amount of unlabeled sample information. In order to verify the effectiveness of the proposed method, sea ice classification experiments are carried out on two hyperspectral data sets: Baffin Bay and Bohai Bay. Compared with the CNN algorithm based on a single feature (spectral or spatial) and other CNN algorithms based on spectral-spatial features, the experimental results show that the proposed method achieves better sea ice classification (98.52% and 97.91%) with small samples. Therefore, it is more suitable for classifying hyperspectral sea ice images.

scholarly journals Remote Sensing and Texture Image Classification Network Based on Deep Learning Integrated with Binary Coding and Sinkhorn Distance

2019 ◽  
Vol 11 (23) ◽  
pp. 2870
Author(s):  
Chu He ◽  
Qingyi Zhang ◽  
Tao Qu ◽  
Dingwen Wang ◽  
Mingsheng Liao
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Feature Fusion ◽  
Texture Features ◽  
Texture Image ◽  
Binary Coding ◽  
Deep Feature ◽  
Feature Based ◽  
Texture Image Classification

In the past two decades, traditional hand-crafted feature based methods and deep feature based methods have successively played the most important role in image classification. In some cases, hand-crafted features still provide better performance than deep features. This paper proposes an innovative network based on deep learning integrated with binary coding and Sinkhorn distance (DBSNet) for remote sensing and texture image classification. The statistical texture features of the image extracted by uniform local binary pattern (ULBP) are introduced as a supplement for deep features extracted by ResNet-50 to enhance the discriminability of features. After the feature fusion, both diversity and redundancy of the features have increased, thus we propose the Sinkhorn loss where an entropy regularization term plays a key role in removing redundant information and training the model quickly and efficiently. Image classification experiments are performed on two texture datasets and five remote sensing datasets. The results show that the statistical texture features of the image extracted by ULBP complement the deep features, and the new Sinkhorn loss performs better than the commonly used softmax loss. The performance of the proposed algorithm DBSNet ranks in the top three on the remote sensing datasets compared with other state-of-the-art algorithms.

scholarly journals Hyperspectral Sea Ice Image Classification Based on the Spectral-Spatial-Joint Feature with the PCA Network

2021 ◽  
Vol 13 (12) ◽  
pp. 2253
Author(s):  
Yanling Han ◽  
Xi Shi ◽  
Shuhu Yang ◽  
Yun Zhang ◽  
Zhonghua Hong ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Sea Ice ◽  
Image Classification ◽  
Spatial Information ◽  
Gabor Filter ◽  
Principal Component ◽  
Component Analysis ◽  
Small Sample ◽  
Bohai Bay ◽  
Hybrid Strategy

Sea ice is one of the most prominent causes of marine disasters occurring at high latitudes. The detection of sea ice is particularly important, and the classification of sea ice images is an important part of sea ice detection. Traditional sea ice classification based on optical remote sensing mostly uses spectral information only and does not fully extract rich spectral and spatial information from sea ice images. At the same time, it is difficult to obtain samples and the resulting small sample sizes used in sea ice classification has limited the improvement of classification accuracy to a certain extent. In response to the above problems, this paper proposes a hyperspectral sea ice image classification method involving spectral-spatial-joint features based on the principal component analysis (PCA) network. First, the method uses the gray-level co-occurrence matrix (GLCM) and Gabor filter to extract textural and spatial information about sea ice. Then, the optimal band combination is extracted with a band selection algorithm based on a hybrid strategy, and the information hidden in the sea ice image is deeply extracted through a fusion of spectral and spatial features. Then, the PCA network is designed based on principal component analysis filters in order to extract the depth features of sea ice more effectively, and hash binarization maps and block histograms are used to enhance the separation and reduce the dimensions of features. Finally, the low-level features in the data form more abstract and invariant high-level features for sea ice classification. In order to verify the effectiveness of the proposed method, we conducted experiments on two different data collection points in Bohai Bay and Baffin Bay. The experimental results show that, compared with other single feature and spectral-spatial-joint feature algorithms, the proposed method achieves better sea ice classification results (94.15% and 96.86%) by using fewer training samples and a shorter training time.

scholarly journals Combining 3D-CNN and Squeeze-and-Excitation Networks for Remote Sensing Sea Ice Image Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Yanling Han ◽  
Cong Wei ◽  
Ruyan Zhou ◽  
Zhonghua Hong ◽  
Yun Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Image Classification ◽  
Classification Accuracy ◽  
Spatial Information ◽  
Spatial Spectrum ◽  
Small Samples ◽  
Support Vector ◽  
Spectral Features ◽  
3D Cnn

Sea ice is one of the most prominent marine disasters in high latitudes. Remote sensing technology provides an effective means for sea ice detection. Remote sensing sea ice images contain rich spectral and spatial information. However, most traditional methods only focus on spectral information or spatial information, and do not excavate the feature of spectral and spatial simultaneously in remote sensing sea ice images classification. At the same time, the complex correlation characteristics among spectra and small sample problem in sea ice classification also limit the improvement of sea ice classification accuracy. For this issue, this paper proposes a new remote sensing sea ice image classification method based on squeeze-and-excitation (SE) network, convolutional neural network (CNN), and support vector machines (SVMs). The proposed method designs 3D-CNN deep network so as to fully exploit the spatial-spectrum features of remote sensing sea ice images and integrates SE-Block into 3D-CNN in-depth network in order to distinguish the contributions of different spectra to sea ice classification. According to the different contributions of spectral features, the weight of each spectral feature is optimized by fusing SE-Block in order to further enhance the sample quality. Finally, information-rich and representative samples are chosen by combining the idea of active learning and input into SVM classifier, and this achieves superior classification accuracy of remote sensing sea ice images with small samples. In order to verify the effectiveness of the proposed method, we conducted experiments on three different data from Baffin Bay, Bohai Bay, and Liaodong Bay. The experimental results show that compared with other classical classification methods, the proposed method comprehensively considers the correlation among spectral features and the small samples problems and deeply excavates the spatial-spectrum characteristics of sea ice and achieves better classification performance, which can be effectively applied to remote sensing sea ice image classification.

scholarly journals EMAP-DCNN: A NOVEL MATHEMATICAL MORPHOLOGY AND DEEP LEARNING COMBINED FRAMEWORK FOR HYPERSPECTRAL IMAGE CLASSIFICATION

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 479-486
Author(s):  
H. Teffahi ◽  
N. Teffahi
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Deep Learning ◽  
Image Classification ◽  
Mathematical Morphology ◽  
Hyperspectral Image ◽  
Hyperspectral Image Classification ◽  
Remote Sensing Images ◽  
Spatial Classification

Abstract. The classification of hyperspectral image (HSI) with high spectral and spatial resolution represents an important and challenging task in image processing and remote sensing (RS) domains due to the problem of computational complexity and big dimensionality of the remote sensing images. The spatial and spectral pixel characteristics have crucial significance for hyperspectral image classification and to take into account these two types of characteristics, various classification and feature extraction methods have been developed to improve spectral-spatial classification of remote sensing images for thematic mapping purposes such as agricultural mapping, urban mapping, emergency mapping in case of natural disasters... In recent years, mathematical morphology and deep learning (DL) have been recognized as prominent feature extraction techniques that led to remarkable spectral-spatial classification performances. Among them, Extended Multi-Attribute Profiles (EMAP) and Dense Convolutional Neural Network (DCNN) are considered as robust and powerful approaches such as the work in this paper is based on these two techniques for the feature extraction stage and used in two combined manners and constructing the EMAP-DCNN frame. The experiments were conducted on two popular datasets: “Indian Pines” and “Huston” hyperspectral datasets. Experimental results demonstrate that the two proposed approaches of the EMAP-DCNN frame denoted EMAP-DCNN 1, EMAP-DCNN 2 provide competitive performances compared with some state-of-the-art spectral-spatial classification methods based on deep learning.

scholarly journals I3D: An Improved Three-Dimensional CNN Model on Hyperspectral Remote Sensing Image Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Qian Haizhong
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Three Dimensional ◽  
Spectral Feature ◽  
Real Life ◽  
Hyperspectral Image Classification ◽  
Accuracy Rate ◽  
Spatial Coupling

Hyperspectral image data are widely used in real life because it contains rich spectral and spatial information. Hyperspectral image classification is to distinguish different functions based on different features. The computer performs quantitative analysis through the captured image and classifies each pixel in the image. However, the traditional deep learning-based hyperspectral image classification technology, due to insufficient spatial-spectral feature extraction, too many network layers, and complex calculations, leads to large parameters and optimizes hyperspectral images. For this reason, I proposed the I3D-CNN model. The number of classification parameters is large, and the network is complex. This method uses hyperspectral image cubes to directly extract spectral-spatial coupling features, adds depth separable convolution to 3D convolution to reextract spatial features, and extracts the parameter amount and calculation time at the same time. In addition, the model removes the pooling layer to achieve fewer parameters, smaller model scale, and easier training effects. The performance of the I3D-CNN model on the test datasets is better than other deep learning-based methods after comparison. The results show that the model still exhibits strong classification performance, reduces a large number of learning parameters, and reduces complexity. The accuracy rate, average classification accuracy rate, and kappa coefficient are all stable above 95%.

scholarly journals Approaches for Hyperspectral Image Classification Detailed Review

2021 ◽  
Vol 11 (1) ◽  
pp. 13-22
Author(s):  
Kushalatha M R ◽  
◽  
Prasantha H S ◽  
Beena R. Shetty ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Image Classification ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Support Vector ◽  
Electromagnetic Spectrum ◽  
Hyperspectral Image Classification ◽  
Sensing Applications ◽  
Remote Sensing Applications

Hyperspectral Image (HSI) processing is the new advancement in image / signal processing field. The growth over the years is appreciable. The main reason behind the successful growth of the Hyperspectral imaging field is due to the enormous amount of spectral and spatial information that the imagery contains. The spectral band that the HSI which contains is also more in number. When an image is captured through the HSI cameras, it contains around 200-250 images of the same scene. Nowadays HSI is used extensively in the fields of environmental monitoring, Crop-Field monitoring, Classification, Identification, Remote sensing applications, Surveillance etc. The spectral and spatial information content present in Hyperspectral images are with high resolutions.Hyperspectral imaging has shown significant growth and widely used in most of the remote sensing applications due to its presence of information of a scene over hundreds of contiguous bands In. Hyperspectral Image Classification of materials is the critical application of HSI using Hyperspectral sensors. It collects hundreds of spectrum channels, where each channel consists of a sharp point of Electromagnetic Spectrum. The paper mainly focuses on Deep Learning techniques such as Convolutional Neural Network (CNN), Artificial Neural Network (ANN), and Support Vector machines (SVM), K-Nearest Neighbour (KNN) for the accuracy in classification. Finally in the summary the current state-of-the-art scheme, a critical discussion after reviewing the research work by other professionals and organizing it into review-based paper, also implying about the present status on classification accuracy using neural networks is carried out.

scholarly journals Crops Fine Classification in Airborne Hyperspectral Imagery Based on Multi-Feature Fusion and Deep Learning

2021 ◽  
Vol 13 (15) ◽  
pp. 2917
Author(s):  
Lifei Wei ◽  
Kun Wang ◽  
Qikai Lu ◽  
Yajing Liang ◽  
Haibo Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Learning ◽  
Random Field ◽  
Deep Neural Network ◽  
Spatial Information ◽  
Feature Fusion ◽  
Conditional Random Field ◽  
Hyperspectral Imagery ◽  
Fine Classification

Hyperspectral imagery has been widely used in precision agriculture due to its rich spectral characteristics. With the rapid development of remote sensing technology, the airborne hyperspectral imagery shows detailed spatial information and temporal flexibility, which open a new way to accurate agricultural monitoring. To extract crop types from the airborne hyperspectral images, we propose a fine classification method based on multi-feature fusion and deep learning. In this research, the morphological profiles, GLCM texture and endmember abundance features are leveraged to exploit the spatial information of the hyperspectral imagery. Then, the multiple spatial information is fused with the original spectral information to generate classification result by using the deep neural network with conditional random field (DNN+CRF) model. Specifically, the deep neural network (DNN) is a deep recognition model which can extract depth features and mine the potential information of data. As a discriminant model, conditional random field (CRF) considers both spatial and contextual information to reduce the misclassification noises while keeping the object boundaries. Moreover, three multiple feature fusion approaches, namely feature stacking, decision fusion and probability fusion, are taken into account. In the experiments, two airborne hyperspectral remote sensing datasets (Honghu dataset and Xiong’an dataset) are used. The experimental results show that the classification performance of the proposed method is satisfactory, where the salt and pepper noise is decreased, and the boundary of the ground object is preserved.

Sign in / Sign up

Export Citation Format

Share Document