Remote sensing scene classification with multi-spatial scale frequency covariance pooling

Chen, Wenjie; Gao, Yuan; Chen, Aibin; Zhou, Guoxiong; Wang, Jianwu; Yang, Xiaobo; Jiang, Rundong

doi:10.1007/s11042-022-12603-x

Cited by 4 publications

(2 citation statements)

References 43 publications

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“…Each of these ship categories presents its own set of features and structural complexities, rendering the classification task more challenging than initially perceived. The distinctiveness of these ships in terms of size, structural design, and functionalities inevitably introduces a high degree of intra-class variability [29,30].…”

Section: Proposed Heterogeneous Ship Datamentioning

confidence: 99%

Heterogeneous Ship Data Classification with Spatial–Channel Attention with Bilinear Pooling Network

Tienin,

Cui,

Mba Esidang

et al. 2023

Remote Sensing

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The classification of ship images has become a significant area of research within the remote sensing community due to its potential applications in maritime security, traffic monitoring, and environmental protection. Traditional monitoring methods like the Automated Identification System (AIS) and the Constant False Alarm Rate (CFAR) have their limitations, such as challenges with sea clutter and the problem of ships turning off their transponders. Additionally, classifying ship images in remote sensing is a complex task due to the spatial arrangement of geospatial objects, complex backgrounds, and the resolution limitations of sensor platforms. To address these challenges, this paper introduces a novel approach that leverages a unique dataset termed Heterogeneous Ship data and a new technique called the Spatial–Channel Attention with Bilinear Pooling Network (SCABPNet). First, we introduce the Heterogeneous Ship data, which combines Synthetic Aperture Radar (SAR) and optical satellite imagery, to leverage the complementary features of the SAR and optical modalities, thereby providing a richer and more-diverse set of features for ship classification. Second, we designed a custom layer, called the Spatial–Channel Attention with Bilinear Pooling (SCABP) layer. This layer sequentially applies the spatial attention, channel attention, and bilinear pooling techniques to enhance the feature representation by focusing on extracting informative and discriminative features from input feature maps, then classify them. Finally, we integrated the SCABP layer into a deep neural network to create a novel model named the SCABPNet model, which is used to classify images in the proposed Heterogeneous Ship data. Our experiments showed that the SCABPNet model demonstrated superior performance, surpassing the results of several state-of-the-art deep learning models. SCABPNet achieved an accuracy of 97.67% on the proposed Heterogeneous Ship dataset during testing. This performance underscores SCABPNet’s capability to focus on ship-specific features while suppressing background noise and feature redundancy. We invite researchers to explore and build upon our work.

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Section: Proposed Heterogeneous Ship Datamentioning

confidence: 99%

Heterogeneous Ship Data Classification with Spatial–Channel Attention with Bilinear Pooling Network

Tienin,

Cui,

Mba Esidang

et al. 2023

Remote Sensing

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“…Recent years have seen the emergence of high-performance deep-learning-based categorization architectures in remote-sensing 6 – 10 Applications for emergency response and catastrophe management can benefit from deep-learning approaches to quickly retrieve vital information, improve response times in time-sensitive circumstances, and help in-the-loop decision-making processes.…”

Section: Introductionmentioning

confidence: 99%

Deformable patch-based-multi-layer perceptron Mixer model for forest fire aerial image classification

Mittal

Sharma

Singh

2022

J. Appl. Rem. Sens.

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Unmanned Aerial Vehicles (UAVs), equipped with mounting camera sensors, facilitate a wide domain of applications deployed in the real-time world. The situational awareness for applications such as search and rescue in case of wildfires, estimation of endangered flora and fauna and emergency responses have seen paradigm shift due to UAVs capability of accessing in remote and challenging areas such as forests. The last decade has seen tremendous growth in CNN based methods for object classification, detection and segmentation tasks. Recently emerged Attention-based Transformer models have been trying to achieve state-of-the art results in predicting images. This paper proposed a novel MLP-Mixer architecture for classification of burned piles in dense forests. MLP mixer architecture tries to eliminate the shortcomings of convolutions and attention by merging them to obtain good performance. The shallow learning of CNN layers and fixed-size patch embedding in transformers have been eliminated by introducing a new module of DePatch in proposed MLP mixer model which divides the input images in a deformable pattern to detect forest fires at an early stage. On the pile images dataset obtained by drones during a burning pile of debris in an Arizona pine forest, our suggested classification algorithm has been tested. The performance of the proposed model has been compared with transformer models.

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EGANet: Elevation-guided attention network for scene classification in panchromatic remote sensing images

Datla,

Swetha,

Gayathri

2024

Neural Comput & Applic

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Remote sensing scene classification with multi-spatial scale frequency covariance pooling

Cited by 4 publications

References 43 publications

Heterogeneous Ship Data Classification with Spatial–Channel Attention with Bilinear Pooling Network

Heterogeneous Ship Data Classification with Spatial–Channel Attention with Bilinear Pooling Network

Deformable patch-based-multi-layer perceptron Mixer model for forest fire aerial image classification

EGANet: Elevation-guided attention network for scene classification in panchromatic remote sensing images

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