Benchmark for Building Segmentation on Up-Scaled Sentinel-2 Imagery

Illarionova, Svetlana; Shadrin, Dmitrii; Shukhratov, Islomjon; Evteeva, K.B.; Popandopulo, Georgii; Sotiriadi, Nazar; Oseledets, Ivan; Burnaev, Evgeny

doi:10.3390/rs15092347

Cited by 10 publications

(3 citation statements)

References 63 publications

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“…Object detection annotation is more commonly available than semantic segmentation in many computer vision tasks. Therefore, the developed approach can be implemented in other specific domains of computer vision, such as remote sensing [59] or manufacturing [60], to simplify the data preparation process and improve model performance. Weak annotation improvement is another challenging task that can be addressed through the proposed approach and applied for environmental analysis [61].…”

Section: Discussionmentioning

confidence: 99%

Enabling Multi-Part Plant Segmentation with Instance-Level Augmentation Using Weak Annotations

et al. 2023

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Plant segmentation is a challenging computer vision task due to plant images complexity. For many practical problems, we have to solve even more difficult tasks. We need to distinguish plant parts rather than the whole plant. The major complication of multi-part segmentation is the absence of well-annotated datasets. It is very time-consuming and expensive to annotate datasets manually on the object parts level. In this article, we propose to use weakly supervised learning for pseudo-annotation. The goal is to train a plant part segmentation model using only bounding boxes instead of fine-grained masks. We review the existing weakly supervised learning approaches and propose an efficient pipeline for agricultural domains. It is designed to resolve tight object overlappings. Our pipeline beats the baseline solution by 23% for the plant part case and by 40% for the whole plant case. Furthermore, we apply instance-level augmentation to boost model performance. The idea of this approach is to obtain a weak segmentation mask and use it for cropping objects from original images and pasting them to new backgrounds during model training. This method provides us a 55% increase in mAP compared with the baseline on object part and a 72% increase on the whole plant segmentation tasks.

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Section: Discussionmentioning

confidence: 99%

Enabling Multi-Part Plant Segmentation with Instance-Level Augmentation Using Weak Annotations

et al. 2023

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“…To date, numerous studies have focused on improving remote sensing image resolution and enhancing the accuracy of various visual tasks by combining image reconstruction methods. In contrast, some existing studies have improved the detection accuracy by combining the SRR and target detection tasks, or the super-resolution and segmentation tasks, in order to improve the segmentation results [6,7]. However, there have been relatively few studies focused on improving the classification accuracy of remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Image Classification Based on Multi-Spectral Cross-Sensor Super-Resolution Combined With Texture Features: A Case Study in the Liaohe Planting Area

Han,

Feng,

et al. 2024

IEEE Access

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High-resolution (HR) optical remote sensing images are typically small in swath and, due to cloud cover, their revisit period, mosaic error, and other problems, it is often infeasible to obtain a large range of remote sensing images for a study area. Meanwhile, low-resolution (LR) satellite images suffer from insufficient spatial and texture information for ground objects. Therefore, classifying a study area with high spatial resolution, large area, and no cloud occlusion using optical remote sensing imagery is very difficult. In recent years, the rapid development of super-resolution reconstruction (SRR) technology has made high-quality spatial resolution reconstruction possible. The SRR of real images is usually accompanied by problems such as sensor spectral range differences, cloud occlusion in the research area, and the SRR algorithm sacrificing a lot of the original information. In this study, with an improved PGGAN, we use only a small number of samples, the wide-swath medium-resolution satellite was restored to the same resolution as the high-resolution satellite, a new method for SRR multi-spectral optical remote sensing image classification based on texture reconstruction information is proposed, and a wide range of highprecision feature classifications are achieved in the study area. In order to solve the problem of spectral distortion in the process of multi-spectral image SRR and the weak generalization of optical remote sensing image ground object classification due to the difference between temporal and spatial features, we combine the idea of ground object classification with texture features obtained after super-resolution reconstruction. We used the support vector machine (SVM) and random forest (RF) classification methods to evaluate the classification effect of each texture spectral feature combination, with the overall accuracy (OA) of the SVM and RF classifiers reaching 98.93% and 98.51%, respectively. The land-use and land-cover (LULC) classification accuracy of the SRR images combined with texture features is much higher than that when directly classifying the original GaoFen-1 and Sentinel-2 images. The obtained results imply that the method of superimposing texture features allows for better classification results in the Liaohe estuary area, providing a new technical idea for the study of LULC classification.INDEX TERMS super-resolution; deep learning; generative adversarial network; land use and land cover; multi-spectral remote sensing;

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“…Over the past few decades, remote sensing data has become increasingly accessible and has been widely used for monitoring and mapping both vegetation cover [3], infrastructure objects [4], and water bodies [5]. Remote sensing imagery can provide a synoptic view of the flooded area, and by analyzing the image data, it is possible to estimate the extent of the flooded area.…”

Section: Introductionmentioning

confidence: 99%

Flood Extent and Volume Estimation Using Remote Sensing Data

Popandopulo,

Illarionova,

Shadrin

et al. 2023

Remote Sensing

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Floods are natural events that can have a significant impacts on the economy and society of affected regions. To mitigate their effects, it is crucial to conduct a rapid and accurate assessment of the damage and take measures to restore critical infrastructure as quickly as possible. Remote sensing monitoring using artificial intelligence is a promising tool for estimating the extent of flooded areas. However, monitoring flood events still presents some challenges due to varying weather conditions and cloud cover that can limit the use of visible satellite data. Additionally, satellite observations may not always correspond to the flood peak, and it is essential to estimate both the extent and volume of the flood. To address these challenges, we propose a methodology that combines multispectral and radar data and utilizes a deep neural network pipeline to analyze the available remote sensing observations for different dates. This approach allows us to estimate the depth of the flood and calculate its volume. Our study uses Sentinel-1, Sentinel-2 data, and Digital Elevation Model (DEM) measurements to provide accurate and reliable flood monitoring results. To validate the developed approach, we consider a flood event occurred in 2021 in Ushmun. As a result, we succeeded to evaluate the volume of that flood event at 0.0087 km3. Overall, our proposed methodology offers a simple yet effective approach to monitoring flood events using satellite data and deep neural networks. It has the potential to improve the accuracy and speed of flood damage assessments, which can aid in the timely response and recovery efforts in affected regions.

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Benchmark for Building Segmentation on Up-Scaled Sentinel-2 Imagery

Cited by 10 publications

References 63 publications

Enabling Multi-Part Plant Segmentation with Instance-Level Augmentation Using Weak Annotations

Enabling Multi-Part Plant Segmentation with Instance-Level Augmentation Using Weak Annotations

Remote Sensing Image Classification Based on Multi-Spectral Cross-Sensor Super-Resolution Combined With Texture Features: A Case Study in the Liaohe Planting Area

Flood Extent and Volume Estimation Using Remote Sensing Data

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