Remote Sensing Image Classification Research Articles

A hybrid approach to remote sensing image classification based on pixel density distribution and adaptive neural networks

<span lang="EN-US">The proposed study employed a method for identifying the main contents (category/class) that a Remote Sensing Image (RSI) belongs to, as well as the percentage contribution if the image comprises a significant number of different content types. Histogram based approach has been used to extract the pixel density distribution and its normalized form helps to make solution independent from image physical characteristics. A multilayer feedforward Artificial Neural Network (ANN) design has been used to address the classification problem. The architecture included an adaptive form of transfer function, whose slope characteristics changes along with weights as learning progresses. The approach of solution design is computation efficient because it doesn’t apply extensive pre-processing.</span>

Scene classification for remote sensing image of land use and land cover using dual-model architecture with multilevel feature fusion

ABSTRACT Scene classification for remote sensing image (RSI) of land use and land cover (LULC) involves identifying discriminative features of interest in different classes. Spurred by the powerful feature extraction capability of Convolutional Neural Networks (CNNs), LULC classification for RSI has rapidly developed in recent years. Although multi-models have better classification performance than single-models, combining multi-models is still the key to maximizing classification accuracy. Thus, this paper proposes a dual-model architecture with multilevel feature fusion called XE-Net. Specifically, high, middle, and low-level features are extracted by the Xception and EfficientNet-V2, respectively, and through transfer learning, the model’s weighting parameters from the ImageNet data are shared. Moreover, the designed sibling feature fusion algorithm fuses the triple-level features extracted from the dual models sequentially according to the same level. Besides, the proposed multi-scale feature fusion method systematically enhances the fused three-scale features to improve the discriminative feature. Finally, the discriminative feature is input into the classifier to obtain the classification results. The maximum average overall accuracy obtained from sufficient experiments using XE-Net on the RSSCN-7 dataset is 96.84%, while WHU-19, UCM-21, OPTIMAL-31, NWPU-RESISC45, and AID attain 99.58%, 99.37%, 97.07%, 95.03%, and 95.78%, respectively, demonstrating our model’s superiority.

Remote Sensing Image Classification Based on Neural Networks Designed Using an Efficient Neural Architecture Search Methodology

Successful applications of machine learning for the analysis of remote sensing images remain limited by the difficulty of designing neural networks manually. However, while the development of neural architecture search offers the unique potential for discovering new and more effective network architectures, existing neural architecture search algorithms are computationally intensive methods requiring a large amount of data and computational resources and are therefore challenging to apply for developing optimal neural network architectures for remote sensing image classification. Our proposed method uses a differentiable neural architecture search approach for remote sensing image classification. We utilize a binary gate strategy for partial channel connections to reduce the sizes of the network parameters, creating a sparse connection pattern that lowers memory consumption and NAS computational costs. Experimental results indicate that our method achieves a 15.1% increase in validation accuracy during the search phase compared to DDSAS, although slightly lower (by 4.5%) than DARTS. However, we reduced the search time by 88% and network parameter size by 84% compared to DARTS. In the architecture evaluation phase, our method demonstrates a 2.79% improvement in validation accuracy over a manually configured CNN network.

Efficient Classification on Remote Sensing Image Using Transfer Learning

With a rapid development in aerial technology, applications of Remote Sensing Images (RSI) have become more diverse. Remote sensing image classification plays a crucial role in analyzing and interpreting Earth observation data for various applications, such as land cover mapping, environmental monitoring, and urban planning. However, accurately classifying remote sensing images poses significant challenges due to their complex spatial and spectral characteristics. In recent years, transfer learning has emerged as a promising technique to improve the classification accuracy by leveraging the knowledge learned from pre-trained models on large-scale datasets. The proposed model explores different transfer learning strategies employed in remote sensing image classification, including fine-tuning, feature extraction, and domain adaptation. It discusses popular pre-trained models, such as VGG16, VGG19, and Inceptionv3, and their applicability to remote sensing datasets. The advantages and limitations of each strategy are analyzed, providing insights into their suitability for various remote sensing applications. A comparative study is done on all these techniques to evaluate the performance measures like Accuracy and Loss.

Satellite remote sensing image classification based on multiple deep learning algorithms

This paper explores the use of satellite remote sensing technology to observe and measure the earth's surface and classify remote sensing images using various deep learning algorithms. By importing AlexNet, VggNet, GoogleNet and MobileNet models, 90% of the data are randomly selected for training and 10% for testing, and the changes in the loss and accuracy of the validation set during training are recorded, as well as the accuracy of the best round of epochs for training.During the training process, the validation set's loss gradually decreased and converged, and the accuracy of the validation set gradually increased and stabilised. The results show that all four models are able to classify remote sensing images well, among which the highest accuracy is MobileNet model, which reaches 99.8%, and Googlenet model is the second one, which reaches 97.9%.AlexNet and VggNet models also have higher accuracy, which are 93.3% and 95.4%, respectively. Overall, the results of this paper provide an effective solution for satellite remote sensing image classification and provide a reference for the application of deep learning algorithms in remote sensing.

An improved semantic segmentation algorithm for high-resolution remote sensing images based on DeepLabv3+

High-precision and high-efficiency Semantic segmentation of high-resolution remote sensing images is a challenge. Existing models typically require a significant amount of training data to achieve good classification results and have numerous training parameters. A novel model called MST-DeepLabv3+ was suggested in this paper for remote sensing image classification. It’s based on the DeepLabv3+ and can produce better results with fewer train parameters. MST-DeepLabv3+ made three improvements: (1) Reducing the number of model parameters by substituting MobileNetV2 for the Xception in the DeepLabv3+’s backbone network. (2) Adding the attention mechanism module SENet to increase the precision of semantic segmentation. (3) Increasing Transfer Learning to enhance the model's capacity to recognize features, and raise the segmentation accuracy. MST-DeepLabv3+ was tested on international society for photogrammetry and remote sensing (ISPRS) dataset, Gaofen image dataset (GID), and practically applied to the Taikang cultivated land dataset. On the ISPRS dataset, the mean intersection over union (MIoU), overall accuracy (OA), Precision, Recall, and F1-score are 82.47%, 92.13%, 90.34%, 90.12%, and 90.23%, respectively. On the GID dataset, these values are 73.44%, 85.58%, 84.10%, 84.86%, and 84.48%, respectively. The results were as high as 90.77%, 95.47%, 95.28%, 95.02%, and 95.15% on the Taikang cultivated land dataset. The experimental results indicate that MST-DeepLabv3+ effectively improves the accuracy of semantic segmentation of remote sensing images, recognizes the edge information with more completeness, and significantly reduces the parameter size.

Quantitative regularization in robust vision transformer for remote sensing image classification

AbstractVision Transformers (ViTs) are exceptional at vision tasks. However, when applied to remote sensing images (RSIs), existing methods often necessitate extensive modifications of ViTs to rival convolutional neural networks (CNNs). This requirement significantly impedes the application of ViTs in geosciences, particularly for researchers who lack the time for comprehensive model redesign. To address this issue, we introduce the concept of quantitative regularization (QR), designed to enhance the performance of ViTs in RSI classification. QR represents an effective algorithm that adeptly manages domain discrepancies in RSIs and can be integrated with any ViTs in transfer learning. We evaluated the effectiveness of QR using three ViT architectures: vanilla ViT, Swin‐ViT and Next‐ViT, on four datasets: AID30, NWPU45, AFGR50 and UCM21. The results reveal that our Next‐ViT model surpasses 39 other advanced methods published in the past 3 years, maintaining robust performance even with a limited number of training samples. We also discovered that our ViT and Swin‐ViT achieve significantly higher accuracy and robustness compared to other methods using the same backbone. Our findings confirm that ViTs can be as effective as CNNs for RSI classification, regardless of the dataset size. Our approach exclusively employs open‐source ViTs and easily accessible training strategies. Consequently, we believe that our method can significantly lower the barriers for geoscience researchers intending to use ViT for RSI applications.

Object-Based Semi-Supervised Spatial Attention Residual UNet for Urban High-Resolution Remote Sensing Image Classification

Object-Based Semi-Supervised Spatial Attention Residual UNet for Urban High-Resolution Remote Sensing Image Classification

A MobileNetV2 model of transfer learning is employed for remote sensing image classification

A MobileNetV2 model of transfer learning is employed for remote sensing image classification

A multi-attention residual integrated network with enhanced fireworks algorithm for remote sensing image classification

This research examines a multi-attention residual integrated network with an enhanced fireworks algorithm for remote sensing image classification. Remote sensing (RS) picture classification is important for land cover mapping, environmental monitoring, and urban planning. Remote sensing image classification is important in earth observation since the military and commercial sectors have focused on it. Due to RS data's high complexity and limited labelled examples, classifying RS pictures is difficult. Deep Learning (DL) techniques have made great strides in RS image categorization, expanding this field's potential. This research introduces Multi-Attention Residual Integrated Network with Enhanced Fireworks Algorithm (MAR-EFA) to improve hyper spectral image identification. MARIN-EFA improves feature fusion and removes unneeded features to overcome technique constraints. The suggested method weights features using different attention models. These characteristics are then carefully extracted and integrated using a residual network. Final contextual semantic integration on deeply fused features is done with a Bi-LSTM network. Our population-based Enhanced Fireworks Algorithm (EFA) is inspired by fireworks' explosive performance and optimises MARIN parameters. Attention techniques and an improved optimisation algorithm improve performance over current systems. Numerous Eurosat dataset studies were assessed using various performance indicators. The simulation results show that MARIN-EFA outperforms current methods. The suggested technique shows promise for improving RS picture classification and allowing more accurate and reliable data categorization.

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.

LDS2AE: Local Diffusion Shared-Specific Autoencoder for Multimodal Remote Sensing Image Classification with Arbitrary Missing Modalities

Recent research on the joint classification of multimodal remote sensing data has achieved great success. However, due to the limitations imposed by imaging conditions, the case of missing modalities often occurs in practice. Most previous researchers regard the classification in case of different missing modalities as independent tasks. They train a specific classification model for each fixed missing modality by extracting multimodal joint representation, which cannot handle the classification of arbitrary (including multiple and random) missing modalities. In this work, we propose a local diffusion shared-specific autoencoder (LDS2AE), which solves the classification of arbitrary missing modalities with a single model. The LDS2AE captures the data distribution of different modalities to learn multimodal shared feature for classification by designing a novel local diffusion autoencoder which consists of a modality-shared encoder and several modality-specific decoders. The modality-shared encoder is designed to extract multimodal shared feature by employing the same parameters to map multimodal data into a shared subspace. The modality-specific decoders put the multimodal shared feature to reconstruct the image of each modality, which facilitates the shared feature to learn unique information of different modalities. In addition, we incorporate masked training to the diffusion autoencoder to achieve local diffusion, which significantly reduces the training cost of model. The approach is tested on widely-used multimodal remote sensing datasets, demonstrating the effectiveness of the proposed LDS2AE in addressing the classification of arbitrary missing modalities. The code is available at https://github.com/Jiahuiqu/LDS2AE.

Multiscale attention for few‐shot image classification

AbstractIn recent years, the application of traditional deep learning methods in the agricultural field using remote sensing techniques, such as crop area and growth monitoring, crop classification, and agricultural disaster monitoring, has been greatly facilitated by advancements in deep learning. The accuracy of image classification plays a crucial role in these applications. Although traditional deep learning methods have achieved significant success in remote sensing image classification, they often involve convolutional neural networks with a large number of parameters that require extensive optimization using numerous remote sensing images for training purposes. To address these challenges, we propose a novel approach called multiscale attention network (MAN) for sample‐based remote sensing image classification. This method consists primarily of feature extractors and attention modules to effectively utilize different scale features through multiscale feature training during the training phase. We evaluate our proposed method on three datasets comprising agricultural remote sensing images and observe superior performance compared to existing approaches. Furthermore, we validate its generalizability by testing it on an oil well indicator diagram specifically designed for classification tasks.

An improved interval type-2 possibilistic C-means clustering algorithm for interclass maximization

Cluster analysis is an important method for data analysis, which is also widely used in remote sensing image classification. An effective clustering algorithm is proposed for the uncertainty caused by the overlap between categories in pattern data and remote sensing images. First, an improved PCM (Possibilistic C-means) algorithm based on distance of cluster center (dc-PCM) is proposed to solve the clustering consistency problem of PCM algorithm. Then, based on dc-PCM algorithm, an improved interval type-2 probabilistic c-means clustering algorithm (IT2-dcPCM) is proposed for interclass maximization. The computational complexity of the proposed algorithm is analyzed, and its performance is evaluated by three data sets, two groups of images segmentation and two groups of remote sensing image classification experiments. The results show that IT2-dcPCM algorithm can assign proper membership degrees to clustering samples, and it has good performance in image segmentation and remote sensing image classification with serious spectral aliasing.

Joint superpixel and Transformer for high resolution remote sensing image classification

Deep neural networks combined with superpixel segmentation have proven to be superior to high-resolution remote sensing image (HRI) classification. Currently, most HRI classification methods that combine deep learning and superpixel segmentation use stacking on multiple scales to extract contextual information from segmented objects. However, this approach does not take into account the contextual dependencies between each segmented object. To solve this problem, a joint superpixel and Transformer (JST) framework is proposed for HRI classification. In JST, HRI is first segmented into superpixel objects as input, and Transformer is used to model the long-range dependencies. The contextual relationship between each input superpixel object is obtained and the class of analyzed objects is output by designing an encoding and decoding Transformer. Additionally, we explore the effect of semantic range on classification accuracy. JST is also tested by using two HRI datasets with overall classification accuracy, average accuracy and Kappa coefficients of 0.79, 0.70, 0.78 and 0.91, 0.85, 0.89, respectively. The effectiveness of the proposed method is compared qualitatively and quantitatively, and the results achieve competitive and consistently better than the benchmark comparison method.

Duplex-Hierarchy Representation Learning for Remote Sensing Image Classification.

Remote sensing image classification (RSIC) is designed to assign specific semantic labels to aerial images, which is significant and fundamental in many applications. In recent years, substantial work has been conducted on RSIC with the help of deep learning models. Even though these models have greatly enhanced the performance of RSIC, the issues of diversity in the same class and similarity between different classes in remote sensing images remain huge challenges for RSIC. To solve these problems, a duplex-hierarchy representation learning (DHRL) method is proposed. The proposed DHRL method aims to explore duplex-hierarchy spaces, including a common space and a label space, to learn discriminative representations for RSIC. The proposed DHRL method consists of three main steps: First, paired images are fed to a pretrained ResNet network for extracting the corresponding features. Second, the extracted features are further explored and mapped into a common space for reducing the intra-class scatter and enlarging the inter-class separation. Third, the obtained representations are used to predict the categories of the input images, and the discrimination loss in the label space is minimized to further promote the learning of discriminative representations. Meanwhile, a confusion score is computed and added to the classification loss for guiding the discriminative representation learning via backpropagation. The comprehensive experimental results show that the proposed method is superior to the existing state-of-the-art methods on two challenging remote sensing image scene datasets, demonstrating that the proposed method is significantly effective.

Simplified Multi-head Mechanism for Few-Shot Remote Sensing Image Classification

The study of few-shot remote sensing image classification has received significant attention. Although meta-learning-based algorithms have been the primary focus of recent examination, feature fusion methods stress feature extraction and representation. Nonetheless, current feature fusion methods, like the multi-head mechanism, are restricted by their complicated network structure and challenging training process. This manuscript presents a simplified multi-head mechanism for obtaining multiple feature representations from a single sample. Furthermore, we perform specific fundamental transformations on remote-sensing images to obtain more suitable features for information representation. Specifically, we reduce multiple feature extractors of the multi-head mechanism to a single one and add an image transformation module before the feature extractor. After transforming the image, the features are extracted resulting in multiple features for each sample. The feature fusion stage is integrated with the classification prediction stage, and multiple linear classifiers are combined for multi-decision fusion to complete feature fusion and classification. By combining image transformation with feature decision fusion, we compare our results with other methods through validation tests and demonstrate that our algorithm simplifies the multi-head mechanism while maintaining or improving classification performance.

A Comprehensive Framework for Improving Remote Sensing Image Classification: Combining Augmentation and Missing Pixel Imputation

A Comprehensive Framework for Improving Remote Sensing Image Classification: Combining Augmentation and Missing Pixel Imputation

Unsupervised Joint Contrastive Learning for Aerial Person Re-Identification and Remote Sensing Image Classification

Unsupervised person re-identification (Re-ID) aims to match the query image of a person with images in the gallery without the use of supervision labels. Most existing methods usually generate pseudo-labels through clustering algorithms for contrastive learning, which inevitably results in noisy labels assigned to samples. In addition, methods that only apply contrastive learning at the clustering level fail to fully consider instance-level relationships between instances. Motivated by this, we propose a joint contrastive learning (JCL) framework for unsupervised person Re-ID. Our proposed method involves creating two memory banks to store features of cluster centroids and instances and applies cluster and instance-level contrastive learning, respectively, to jointly optimize the neural networks. The cluster-level contrastive loss is used to promote feature compactness within the same cluster and reinforce identity similarity. The instance-level contrastive loss is used to distinguish easily confused samples. In addition, we use a WaveBlock attention module (WAM), which can continuously wave feature map blocks and introduce attention mechanisms to produce more robust feature representations of a person without considerable information loss. Furthermore, we enhance the quality of our clustering by leveraging camera label information to eliminate clusters containing single camera captures. Extensive experimental results on two widely used person Re-ID datasets verify the effectiveness of our JCL method. Meanwhile, we also used two remote sensing datasets to demonstrate the generalizability of our method.

Semantic interleaving global channel attention for multilabel remote sensing image classification

ABSTRACT Multilabel remote sensing image classification (MLRSIC) has received increasing research interest. Taking the co-occurrence relationship of multiple labels as additional information helps to improve the overall performance. However, current methods only focus on using it to constrain the final feature which is output from a convolutional neural network (CNN). On the one hand, these methods need to exploit the potential of label correlation in feature representation fully. On the other hand, they increase the label noise sensitivity of the system, resulting in poor robustness. In this paper, a novel method called ‘Semantic Interleaving Global chaNnel Attention’ (SIGNA) is proposed for MLRSIC. First, the label co-occurrence graph is obtained according to the statistical information of the training set and fed into a graph neural network (GNN) to generate optimal semantic feature representations of each label. Next, the semantic features are interleaved with visual features which are extracted by CNNs to guide the overall features of the input image transform from the original feature space to the semantic feature space with embedded label relations. Then, global attention triggered by semantic interleaving is used to emphasize visual features in important channels. Finally, to make SIGNA easier to use and more optimized, multihead SIGNA-based feature adaptive weighting networks are proposed as plug-in blocks to plug into any layers of a CNN. For remote sensing images, better classification performance can be achieved by inserting the plug-in blocks into the shallow layers of CNNs. We conducted extensive experimental comparisons on three data sets: UCM, AID and DFC15. Experimental results demonstrate that the proposed SIGNA achieves superior classification performance compared to state-of-the-art (SOTA) methods. Notes that the codes of this paper will be open to the community for reproducibility research.