Prototype Calibration with Feature Generation for Few-Shot Remote Sensing Image Scene Classification

Few-shot classification of remote sensing images has attracted attention due to its important applications in various fields. The major challenge in few-shot remote sensing image scene classification is that limited labeled samples can be utilized for training. This may lead to the deviation of prototype feature expression, and thus the classification performance will be impacted. To solve these issues, a prototype calibration with a feature-generating model is proposed for few-shot remote sensing image scene classification. In the proposed framework, a feature encoder with self-attention is developed to reduce the influence of irrelevant information. Then, the feature-generating module is utilized to expand the support set of the testing set based on prototypes of the training set, and prototype calibration is proposed to optimize features of support images that can enhance the representativeness of each category features. Experiments on NWPU-RESISC45 and WHU-RS19 datasets demonstrate that the proposed method can yield superior classification accuracies for few-shot remote sensing image scene classification.

Download Full-text

Hierarchical Multi-View Semi-Supervised Learning for Very High-Resolution Remote Sensing Image Classification

Remote Sensing ◽

10.3390/rs12061012 ◽

2020 ◽

Vol 12 (6) ◽

pp. 1012 ◽

Cited By ~ 2

Author(s):

Cheng Shi ◽

Zhiyong Lv ◽

Xiuhong Yang ◽

Pengfei Xu ◽

Irfana Bibi

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Image Classification ◽

Supervised Learning ◽

Remote Sensing Image ◽

Classification Performance ◽

Remote Sensing Images ◽

Training Set ◽

Remote Sensing Image Classification ◽

Very High

Traditional classification methods used for very high-resolution (VHR) remote sensing images require a large number of labeled samples to obtain higher classification accuracy. Labeled samples are difficult to obtain and costly. Therefore, semi-supervised learning becomes an effective paradigm that combines the labeled and unlabeled samples for classification. In semi-supervised learning, the key issue is to enlarge the training set by selecting highly-reliable unlabeled samples. Observing the samples from multiple views is helpful to improving the accuracy of label prediction for unlabeled samples. Hence, the reasonable view partition is very important for improving the classification performance. In this paper, a hierarchical multi-view semi-supervised learning framework with CNNs (HMVSSL) is proposed for VHR remote sensing image classification. Firstly, a superpixel-based sample enlargement method is proposed to increase the number of training samples in each view. Secondly, a view partition method is designed to partition the training set into two independent views, and the partitioned subsets are characterized by being inter-distinctive and intra-compact. Finally, a collaborative classification strategy is proposed for the final classification. Experiments are conducted on three VHR remote sensing images, and the results show that the proposed method performs better than several state-of-the-art methods.

Download Full-text

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.

Download Full-text

U-net Network for Building Information Extraction of Remote-Sensing Imagery

International Journal of Online and Biomedical Engineering (iJOE) ◽

10.3991/ijoe.v14i12.9335 ◽

2018 ◽

Vol 14 (12) ◽

pp. 179

Author(s):

Jingtan Li ◽

Maolin Xu ◽

Hongling Xiu

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Information Extraction ◽

Image Data ◽

Semantic Segmentation ◽

Remote Sensing Image ◽

Remote Sensing Images ◽

Training Set ◽

Building Information ◽

The Face

With the resolution of remote sensing images is getting higher and higher, high-resolution remote sensing images are widely used in many areas. Among them, image information extraction is one of the basic applications of remote sensing images. In the face of massive high-resolution remote sensing image data, the traditional method of target recognition is difficult to cope with. Therefore, this paper proposes a remote sensing image extraction based on U-net network. Firstly, the U-net semantic segmentation network is used to train the training set, and the validation set is used to verify the training set at the same time, and finally the test set is used for testing. The experimental results show that U-net can be applied to the extraction of buildings.

Download Full-text

Superpixel-Guided Layer-Wise Embedding CNN for Remote Sensing Image Classification

Remote Sensing ◽

10.3390/rs11020174 ◽

2019 ◽

Vol 11 (2) ◽

pp. 174 ◽

Cited By ~ 4

Author(s):

Han Liu ◽

Jun Li ◽

Lin He ◽

Yu Wang

Keyword(s):

Remote Sensing ◽

Image Classification ◽

Remote Sensing Data ◽

Sampling Strategy ◽

Remote Sensing Image ◽

Fine Tuning ◽

Spatial Dependency ◽

Remote Sensing Images ◽

Training Set ◽

Remote Sensing Image Classification

Irregular spatial dependency is one of the major characteristics of remote sensing images, which brings about challenges for classification tasks. Deep supervised models such as convolutional neural networks (CNNs) have shown great capacity for remote sensing image classification. However, they generally require a huge labeled training set for the fine tuning of a deep neural network. To handle the irregular spatial dependency of remote sensing images and mitigate the conflict between limited labeled samples and training demand, we design a superpixel-guided layer-wise embedding CNN (SLE-CNN) for remote sensing image classification, which can efficiently exploit the information from both labeled and unlabeled samples. With the superpixel-guided sampling strategy for unlabeled samples, we can achieve an automatic determination of the neighborhood covering for a spatial dependency system and thus adapting to real scenes of remote sensing images. In the designed network, two types of loss costs are combined for the training of CNN, i.e., supervised cross entropy and unsupervised reconstruction cost on both labeled and unlabeled samples, respectively. Our experimental results are conducted with three types of remote sensing data, including hyperspectral, multispectral, and synthetic aperture radar (SAR) images. The designed SLE-CNN achieves excellent classification performance in all cases with a limited labeled training set, suggesting its good potential for remote sensing image classification.

Download Full-text

Scene Classification of Optical Remote Sensing Images Based on CNN Automatic Transfer

2018 IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) ◽

10.1109/auteee.2018.8720785 ◽

2018 ◽

Author(s):

Jicheng Quan ◽

Chen Wu ◽

Hongwei Wang ◽

Zhiqiang Wang

Keyword(s):

Remote Sensing ◽

Optical Remote Sensing ◽

Scene Classification ◽

Remote Sensing Images

Download Full-text

An Efficient and Lightweight Convolutional Neural Network for Remote Sensing Image Scene Classification

Sensors ◽

10.3390/s20071999 ◽

2020 ◽

Vol 20 (7) ◽

pp. 1999 ◽

Cited By ~ 6

Author(s):

Donghang Yu ◽

Qing Xu ◽

Haitao Guo ◽

Chuan Zhao ◽

Yuzhun Lin ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Convolutional Neural Network ◽

Visual Recognition ◽

Feature Fusion ◽

Remote Sensing Image ◽

Classification Performance ◽

Image Features ◽

Training Dataset ◽

Scene Classification

Classifying remote sensing images is vital for interpreting image content. Presently, remote sensing image scene classification methods using convolutional neural networks have drawbacks, including excessive parameters and heavy calculation costs. More efficient and lightweight CNNs have fewer parameters and calculations, but their classification performance is generally weaker. We propose a more efficient and lightweight convolutional neural network method to improve classification accuracy with a small training dataset. Inspired by fine-grained visual recognition, this study introduces a bilinear convolutional neural network model for scene classification. First, the lightweight convolutional neural network, MobileNetv2, is used to extract deep and abstract image features. Each feature is then transformed into two features with two different convolutional layers. The transformed features are subjected to Hadamard product operation to obtain an enhanced bilinear feature. Finally, the bilinear feature after pooling and normalization is used for classification. Experiments are performed on three widely used datasets: UC Merced, AID, and NWPU-RESISC45. Compared with other state-of-art methods, the proposed method has fewer parameters and calculations, while achieving higher accuracy. By including feature fusion with bilinear pooling, performance and accuracy for remote scene classification can greatly improve. This could be applied to any remote sensing image classification task.

Download Full-text

Deep Discriminative Representation Learning with Attention Map for Scene Classification

Remote Sensing ◽

10.3390/rs12091366 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1366 ◽

Cited By ~ 5

Author(s):

Jun Li ◽

Daoyu Lin ◽

Yang Wang ◽

Guangluan Xu ◽

Yunyan Zhang ◽

...

Keyword(s):

Remote Sensing ◽

Feature Fusion ◽

Representation Learning ◽

Classification Performance ◽

Great Success ◽

Scene Classification ◽

Remote Sensing Images ◽

Discriminative Ability ◽

Feature Representations ◽

Benchmark Datasets

In recent years, convolutional neural networks (CNNs) have shown great success in the scene classification of computer vision images. Although these CNNs can achieve excellent classification accuracy, the discriminative ability of feature representations extracted from CNNs is still limited in distinguishing more complex remote sensing images. Therefore, we propose a unified feature fusion framework based on attention mechanism in this paper, which is called Deep Discriminative Representation Learning with Attention Map (DDRL-AM). Firstly, by applying Gradient-weighted Class Activation Mapping (Grad-CAM) algorithm, attention maps associated with the predicted results are generated in order to make CNNs focus on the most salient parts of the image. Secondly, a spatial feature transformer (SFT) is designed to extract discriminative features from attention maps. Then an innovative two-channel CNN architecture is proposed by the fusion of features extracted from attention maps and the RGB (red green blue) stream. A new objective function that considers both center and cross-entropy loss are optimized to decrease the influence of inter-class dispersion and within-class variance. In order to show its effectiveness in classifying remote sensing images, the proposed DDRL-AM method is evaluated on four public benchmark datasets. The experimental results demonstrate the competitive scene classification performance of the DDRL-AM approach. Moreover, the visualization of features extracted by the proposed DDRL-AM method can prove that the discriminative ability of features has been increased.

Download Full-text

Scene classification of remote sensing images based on hierarchical sparse coding

The Journal of Engineering ◽

10.1049/joe.2018.8268 ◽

2018 ◽

Vol 2018 (16) ◽

pp. 1650-1657

Author(s):

Xu Jiaqing ◽

Lv Qi ◽

Liu Hongjun ◽

He Jie

Keyword(s):

Remote Sensing ◽

Sparse Coding ◽

Scene Classification ◽

Remote Sensing Images

Download Full-text

Novel Multi-Scale Filter Profile-Based Framework for VHR Remote Sensing Image Classification

Remote Sensing ◽

10.3390/rs11182153 ◽

2019 ◽

Vol 11 (18) ◽

pp. 2153

Author(s):

Zhiyong Lv ◽

Guangfei Li ◽

Yixiang Chen ◽

Jón Atli Benediktsson

Keyword(s):

Remote Sensing ◽

Principal Component ◽

Remote Sensing Image ◽

Classification Performance ◽

Remote Sensing Images ◽

Multi Scale ◽

Remote Sensing Image Classification ◽

Very High Spatial Resolution ◽

Layer Stacking ◽

Initial Classification

Filter is a well-known tool for noise reduction of very high spatial resolution (VHR) remote sensing images. However, a single-scale filter usually demonstrates limitations in covering various targets with different sizes and shapes in a given image scene. A novel method called multi-scale filter profile (MFP)-based framework (MFPF) is introduced in this study to improve the classification performance of a remote sensing image of VHR and address the aforementioned problem. First, an adaptive filter is extended with a series of parameters for MFP construction. Then, a layer-stacking technique is used to concatenate the MPFs and all the features into a stacked vector. Afterward, principal component analysis, a classical descending dimension algorithm, is performed on the fused profiles to reduce the redundancy of the stacked vector. Finally, the spatial adaptive region of each filter in the MFPs is used for post-processing of the obtained initial classification map through a supervised classifier. This process aims to revise the initial classification map and generate a final classification map. Experimental results performed on the three real VHR remote sensing images demonstrate the effectiveness of the proposed MFPF in comparison with the state-of-the-art methods. Hard-tuning parameters are unnecessary in the application of the proposed approach. Thus, such a method can be conveniently applied in real applications.

Download Full-text