Kolmogorov-Arnold Network for Satellite Image Classification in Remote Sensing

• URL: http://arxiv.org/abs/2406.00600v1
• Date: Sun, 2 Jun 2024 03:11:37 GMT
• Title: Kolmogorov-Arnold Network for Satellite Image Classification in Remote Sensing
• Authors: Minjong Cheon,
• Abstract summary: We propose the first approach for integrating the Kolmogorov-Arnold Network (KAN) with pre-trained Convolutional Neural Network (CNN) models for remote sensing scene classification tasks. Our novel methodology, named KCN, aims to replace traditional Multi-Layer Perceptrons (MLPs) with KAN to enhance classification performance. We employed multiple CNN-based models, including VGG16, MobileNetV2, EfficientNet, ConvNeXt, ResNet101, and Vision Transformer (ViT), and evaluated their performance when paired with KAN.
• Score: 4.8951183832371
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this research, we propose the first approach for integrating the Kolmogorov-Arnold Network (KAN) with various pre-trained Convolutional Neural Network (CNN) models for remote sensing (RS) scene classification tasks using the EuroSAT dataset. Our novel methodology, named KCN, aims to replace traditional Multi-Layer Perceptrons (MLPs) with KAN to enhance classification performance. We employed multiple CNN-based models, including VGG16, MobileNetV2, EfficientNet, ConvNeXt, ResNet101, and Vision Transformer (ViT), and evaluated their performance when paired with KAN. Our experiments demonstrated that KAN achieved high accuracy with fewer training epochs and parameters. Specifically, ConvNeXt paired with KAN showed the best performance, achieving 94% accuracy in the first epoch, which increased to 96% and remained consistent across subsequent epochs. The results indicated that KAN and MLP both achieved similar accuracy, with KAN performing slightly better in later epochs. By utilizing the EuroSAT dataset, we provided a robust testbed to investigate whether KAN is suitable for remote sensing classification tasks. Given that KAN is a novel algorithm, there is substantial capacity for further development and optimization, suggesting that KCN offers a promising alternative for efficient image analysis in the RS field.

Related papers

• KANICE: Kolmogorov-Arnold Networks with Interactive Convolutional Elements [1.663204995903499]
  We introduce KANICE, a novel neural architecture that combines Convolutional Neural Networks (CNNs) with Kolmogorov-Arnold Network (KAN) principles. KANICE integrates Interactive Convolutional Blocks (ICBs) and KAN linear layers into a CNN framework. We evaluated KANICE on four datasets: MNIST, Fashion-MNIST, EMNIST, and SVHN.
  arXiv  Detail & Related papers  (2024-10-22T16:50:34Z)
• Kolmogorov-Arnold Network Autoencoders [0.0]
  Kolmogorov-Arnold Networks (KANs) are promising alternatives to Multi-Layer Perceptrons (MLPs) KANs align closely with the Kolmogorov-Arnold representation theorem, potentially enhancing both model accuracy and interpretability. Our results demonstrate that KAN-based autoencoders achieve competitive performance in terms of reconstruction accuracy.
  arXiv  Detail & Related papers  (2024-10-02T22:56:00Z)
• How to Learn More? Exploring Kolmogorov-Arnold Networks for Hyperspectral Image Classification [26.37105279142761]
  Kolmogorov-Arnold Networks (KANs) were proposed as viable alternatives for vision transformers (ViTs) In this study, we assess the effectiveness of KANs for complex hyperspectral image (HSI) data classification. To enhance the HSI classification accuracy obtained by the KANs, we develop and propose a Hybrid architecture utilizing 1D, 2D, and 3D KANs.
  arXiv  Detail & Related papers  (2024-06-22T03:31:02Z)
• DCNN: Dual Cross-current Neural Networks Realized Using An Interactive Deep Learning Discriminator for Fine-grained Objects [48.65846477275723]
  This study proposes novel dual-current neural networks (DCNN) to improve the accuracy of fine-grained image classification. The main novel design features for constructing a weakly supervised learning backbone model DCNN include (a) extracting heterogeneous data, (b) keeping the feature map resolution unchanged, (c) expanding the receptive field, and (d) fusing global representations and local features.
  arXiv  Detail & Related papers  (2024-05-07T07:51:28Z)
• Supervised Training of Siamese Spiking Neural Networks with Earth's Mover Distance [4.047840018793636]
  This study adapts the highly-versatile siamese neural network model to the event data domain. We introduce a supervised training framework for optimizing Earth's Mover Distance between spike trains with spiking neural networks (SNN)
  arXiv  Detail & Related papers  (2022-02-20T00:27:57Z)
• Hybrid SNN-ANN: Energy-Efficient Classification and Object Detection for Event-Based Vision [64.71260357476602]
  Event-based vision sensors encode local pixel-wise brightness changes in streams of events rather than image frames. Recent progress in object recognition from event-based sensors has come from conversions of deep neural networks. We propose a hybrid architecture for end-to-end training of deep neural networks for event-based pattern recognition and object detection.
  arXiv  Detail & Related papers  (2021-12-06T23:45:58Z)
• Implementing a foveal-pit inspired filter in a Spiking Convolutional Neural Network: a preliminary study [0.0]
  We have presented a Spiking Convolutional Neural Network (SCNN) that incorporates retinal foveal-pit inspired Difference of Gaussian filters and rank-order encoding. The model is trained using a variant of the backpropagation algorithm adapted to work with spiking neurons, as implemented in the Nengo library. The network has achieved up to 90% accuracy, where loss is calculated using the cross-entropy function.
  arXiv  Detail & Related papers  (2021-05-29T15:28:30Z)
• Adaptive Nearest Neighbor Machine Translation [60.97183408140499]
  kNN-MT combines pre-trained neural machine translation with token-level k-nearest-neighbor retrieval. Traditional kNN algorithm simply retrieves a same number of nearest neighbors for each target token. We propose Adaptive kNN-MT to dynamically determine the number of k for each target token.
  arXiv  Detail & Related papers  (2021-05-27T09:27:42Z)
• ANNETTE: Accurate Neural Network Execution Time Estimation with Stacked Models [56.21470608621633]
  We propose a time estimation framework to decouple the architectural search from the target hardware. The proposed methodology extracts a set of models from micro- kernel and multi-layer benchmarks and generates a stacked model for mapping and network execution time estimation. We compare estimation accuracy and fidelity of the generated mixed models, statistical models with the roofline model, and a refined roofline model for evaluation.
  arXiv  Detail & Related papers  (2021-05-07T11:39:05Z)
• Cyclic Differentiable Architecture Search [99.12381460261841]
  Differentiable ARchiTecture Search, i.e., DARTS, has drawn great attention in neural architecture search. We propose new joint objectives and a novel Cyclic Differentiable ARchiTecture Search framework, dubbed CDARTS. In the DARTS search space, we achieve 97.52% top-1 accuracy on CIFAR10 and 76.3% top-1 accuracy on ImageNet.
  arXiv  Detail & Related papers  (2020-06-18T17:55:19Z)
• FBNetV3: Joint Architecture-Recipe Search using Predictor Pretraining [65.39532971991778]
  We present an accuracy predictor that scores architecture and training recipes jointly, guiding both sample selection and ranking. We run fast evolutionary searches in just CPU minutes to generate architecture-recipe pairs for a variety of resource constraints. FBNetV3 makes up a family of state-of-the-art compact neural networks that outperform both automatically and manually-designed competitors.
  arXiv  Detail & Related papers  (2020-06-03T05:20:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.