Remote Sensing Image Scene Classification: Advances and Open Challenges

Tombe, Ronald; Viriri, Serestina

doi:10.3390/geomatics3010007

Cited by 4 publications

(6 citation statements)

References 62 publications

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“…The accuracy of a model is measured by the fraction of all true predictions by all predictions of the model. The formula to compute the accuracy is represented below in Equation (1). Accuracy = (TP + TN)/(TP + FP + TN + FN)…”

Section: Accuracymentioning

confidence: 99%

“…Because of the rising demand for accurate image classification in many areas, the natural scene classification of remotely sensed images has become a prominent study area. In this domain, remote-sensing image classification [1] is an important problem in detecting and mapping various forms of land cover on the Earth's surface and its manual labeling is a tedious task. This is the reason why the automated identification of natural landscapes in remote-sensing images has gained substantial attention from the research community [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

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Cross-Domain Transfer Learning for Natural Scene Classification of Remote-Sensing Imagery

et al. 2023

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Natural scene classification, which has potential applications in precision agriculture, environmental monitoring, and disaster management, poses significant challenges due to variations in the spatial resolution, spectral resolution, texture, and size of remotely sensed images of natural scenes on Earth. For such challenging problems, deep-learning-based algorithms have demonstrated amazing performances in recent years. Among these methodologies, transfer learning is a useful technique which employs the learned features already extracted from the pre-trained models from large-scale datasets for the problem at hand, resulting in quicker and more accurate models. In this study, we deployed cross-domain transfer learning for the land-cover classification of remotely sensed images of natural scenes. We conducted extensive experiments to measure the performance of the proposed method and explored the factors that affect the performance of the models. Our findings suggest that fine-tuning the ResNet-50 model outperforms various other models in terms of the classification accuracy. The experimental results showed that the deployed cross-domain transfer-learning system achieved outstanding (99.5% and 99.1%) accurate performances compared to previous benchmarks on the NaSC-TG2 dataset with the final tuning of the whole structure and only the last three layers, respectively.

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Section: Accuracymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cross-Domain Transfer Learning for Natural Scene Classification of Remote-Sensing Imagery

et al. 2023

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“…With the ongoing development of remote sensing observation technology, there has been a substantial increase in the number of high-quality remote sensing images produced, including those with high-definition and high-resolution [1]. Interpreting these images efficiently and accurately becomes a challenge for researchers [2]. One way to interpret is through scene image classification, which entails extracting and mapping deep semantic features in images to the relevant categories.…”

Section: Introductionmentioning

confidence: 99%

LPNet: A remote sensing scene classification method based on large kernel convolution and parameter fusion

Wang,

Shi,

Wang

et al. 2024

IET Image Processing

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Remote sensing scene images contain numerous feature targets with unrelated semantic information, so how to extract to the local key information and semantic features of the image becomes the key to achieving accurate classification. Existing Convolutional Neural Networks (CNNs) mostly concentrate on the global representation of an image and lose the shallow features. To overcome these issues, this paper proposes LPNet for remote sensing scene image classification. First, LPNet employs LKConv to extract the semantic features in the image, while using standard convolution to extract local key information in the image. Additionally, the LPNet applies a shortcut residual concatenation branch to reuse features. Then, parameter fusion combines parameters from previous branches, improving the capacity of the model to obtain a more comprehensive and rich feature representation of the image. Finally, considering the relationship between the classification ability of the model and the depth of feature extraction, the Feature Mixture (FM) Block is used to deepen the model for feature extraction. Comparative experiments on four publicly available datasets show that LPNet provides comparable results to other state‐of‐the‐art methods. The effectiveness of LPNet is further demonstrated by visualizing the effective receptive fields (ERFs).

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“…Since objects in remote sensing images are generally small and may be densely distributed, using the human eye to obtain effective information is inefficient and error-prone, and manually extracting this information is an unpractical task. Target detection technology has been widely used in remote sensing [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

RSI-YOLO: Object Detection Method for Remote Sensing Images Based on Improved YOLO

Yuan

et al. 2023

Sensors

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With the continuous development of deep learning technology, object detection has received extensive attention across various computer fields as a fundamental task of computational vision. Effective detection of objects in remote sensing images is a key challenge, owing to their small size and low resolution. In this study, a remote sensing image detection (RSI-YOLO) approach based on the YOLOv5 target detection algorithm is proposed, which has been proven to be one of the most representative and effective algorithms for this task. The channel attention and spatial attention mechanisms are used to strengthen the features fused by the neural network. The multi-scale feature fusion structure of the original network based on a PANet structure is improved to a weighted bidirectional feature pyramid structure to achieve more efficient and richer feature fusion. In addition, a small object detection layer is added, and the loss function is modified to optimise the network model. The experimental results from four remote sensing image datasets, such as DOTA and NWPU-VHR 10, indicate that RSI-YOLO outperforms the original YOLO in terms of detection performance. The proposed RSI-YOLO algorithm demonstrated superior detection performance compared to other classical object detection algorithms, thus validating the effectiveness of the improvements introduced into the YOLOv5 algorithm.

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Remote Sensing Image Scene Classification: Advances and Open Challenges

Cited by 4 publications

References 62 publications

Cross-Domain Transfer Learning for Natural Scene Classification of Remote-Sensing Imagery

Cross-Domain Transfer Learning for Natural Scene Classification of Remote-Sensing Imagery

LPNet: A remote sensing scene classification method based on large kernel convolution and parameter fusion

RSI-YOLO: Object Detection Method for Remote Sensing Images Based on Improved YOLO

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