Remote Sensing Images Background Noise Processing Method for Ship Objects in Instance Segmentation

Chai, Bosong; Nie, Xuan; Gao, Heyu; Jia, Jianchao; Qiao, Qiao

doi:10.1007/s12524-022-01631-7

Cited by 6 publications

(3 citation statements)

References 23 publications

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“…The combination of attention mechanism and convolutional neural network is the focus of research in the field of computer vision, and the addition of the attention mechanism enables the model to focus its attention on the object region of the image ( Kim and Verghese, 2012 ), differentiating from processing the whole image, focusing on extracting the object region features, and effectively improving the model performance. In terms of the object detection task in the field of computer vision, the introduction of the attention mechanism can make the object feature extraction more adequate, reduce the interference of the background image and negative samples ( Chai et al., 2023 ), and realize the effective improvement of the model detection performance.…”

Section: Methodsmentioning

confidence: 99%

Automatic detection of standing dead trees based on improved YOLOv7 from airborne remote sensing imagery

Zhou,

Wu,

et al. 2024

Front. Plant Sci.

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Detecting and localizing standing dead trees (SDTs) is crucial for effective forest management and conservation. Due to challenges posed by mountainous terrain and road conditions, conducting a swift and comprehensive survey of SDTs through traditional manual inventory methods is considerably difficult. In recent years, advancements in deep learning and remote sensing technology have facilitated real-time and efficient detection of dead trees. Nevertheless, challenges persist in identifying individual dead trees in airborne remote sensing images, attributed to factors such as small target size, mutual occlusion and complex backgrounds. These aspects collectively contribute to the increased difficulty of detecting dead trees at a single-tree scale. To address this issue, the paper introduces an improved You Only Look Once version 7 (YOLOv7) model that incorporates the Simple Parameter-Free Attention Module (SimAM), an unparameterized attention mechanism. This improvement aims to enhance the network’s feature extraction capabilities and increase the model’s sensitivity to small target dead trees. To validate the superiority of SimAM_YOLOv7, we compared it with four widely adopted attention mechanisms. Additionally, a method to enhance model robustness is presented, involving the replacement of the Complete Intersection over Union (CIoU) loss in the original YOLOv7 model with the Wise-IoU (WIoU) loss function. Following these, we evaluated detection accuracy using a self-developed dataset of SDTs in forests. The results indicate that the improved YOLOv7 model can effectively identify dead trees in airborne remote sensing images, achieving precision, recall and mAP@0.5 values of 94.31%, 93.13% and 98.03%, respectively. These values are 3.67%, 2.28% and 1.56% higher than those of the original YOLOv7 model. This improvement model provides a convenient solution for forest management.

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Section: Methodsmentioning

confidence: 99%

Automatic detection of standing dead trees based on improved YOLOv7 from airborne remote sensing imagery

Zhou,

Wu,

et al. 2024

Front. Plant Sci.

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“…It can extract ship features adequately while reducing the introduction of background information. Su et al [99] and Chai et al [109] utilized DCN instead of standard convolution to extract features, enhancing the ability to capture irregular ship features. Guo et al [94] and Cui et al [110] integrated DCN into FPN to better adapt to the geometric features of ships.…”

Section: Dcn-based Methodsmentioning

confidence: 99%

Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances

Zhao,

Wang,

et al. 2024

Remote Sensing

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Ship detection aims to automatically identify whether there are ships in the images, precisely classifies and localizes them. Regardless of whether utilizing early manually designed methods or deep learning technology, ship detection is dedicated to exploring the inherent characteristics of ships to enhance recall. Nowadays, high-precision ship detection plays a crucial role in civilian and military applications. In order to provide a comprehensive review of ship detection in optical remote-sensing images (SDORSIs), this paper summarizes the challenges as a guide. These challenges include complex marine environments, insufficient discriminative features, large scale variations, dense and rotated distributions, large aspect ratios, and imbalances between positive and negative samples. We meticulously review the improvement methods and conduct a detailed analysis of the strengths and weaknesses of these methods. We compile ship information from common optical remote sensing image datasets and compare algorithm performance. Simultaneously, we compare and analyze the feature extraction capabilities of backbones based on CNNs and Transformer, seeking new directions for the development in SDORSIs. Promising prospects are provided to facilitate further research in the future.

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“…Compared to natural images, there are some considerable difficulties in RSIs as illustrated in Figure 1. Firstly, RSIs often possess obvious background noises, which are represented by disturbing elements (e.g., shadows, blurred edges, and ambiguous backgrounds) [WZC*20] [CNG*23] and unrelated remote ground objects [DTB21]; beside, RSIs tend to exhibit a multi‐scale phenomenon that both inter‐class and intra‐class ground objects have significant scale variations; furthermore, ground objects in RSIs frequently exist within specific spatial distribution pattern (e.g., buildings are individually placed, whereas cars, roads, and houses jointly represent a neighborhood); even within the same pattern, ground objects may exhibit complex compositional relationships and geometric variations (e.g., buildings are likely to have different orientations and shapes).…”

Section: Introductionmentioning

confidence: 99%

MAPMaN: Multi‐Stage U‐Shaped Adaptive Pattern Matching Network for Semantic Segmentation of Remote Sensing Images

Hong,

Ma,

Wang

et al. 2023

Computer Graphics Forum

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Remote sensing images (RSIs) often possess obvious background noises, exhibit a multi‐scale phenomenon, and are characterized by complex scenes with ground objects in diversely spatial distribution pattern, bringing challenges to the corresponding semantic segmentation. CNN‐based methods can hardly address the diverse spatial distributions of ground objects, especially their compositional relationships, while Vision Transformers (ViTs) introduce background noises and have a quadratic time complexity due to dense global matrix multiplications. In this paper, we introduce Adaptive Pattern Matching (APM), a lightweight method for long‐range adaptive weight aggregation. Our APM obtains a set of pixels belonging to the same spatial distribution pattern of each pixel, and calculates the adaptive weights according to their compositional relationships. In addition, we design a tiny U‐shaped network using the APM as a module to address the large variance of scales of ground objects in RSIs. This network is embedded after each stage in a backbone network to establish a Multi‐stage U‐shaped Adaptive Pattern Matching Network (MAPMaN), for nested multi‐scale modeling of ground objects towards semantic segmentation of RSIs. Experiments on three datasets demonstrate that our MAPMaN can outperform the state‐of‐the‐art methods in common metrics. The code can be available at https://github.com/INiid/MAPMaN.

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Remote Sensing Images Background Noise Processing Method for Ship Objects in Instance Segmentation

Cited by 6 publications

References 23 publications

Automatic detection of standing dead trees based on improved YOLOv7 from airborne remote sensing imagery

Automatic detection of standing dead trees based on improved YOLOv7 from airborne remote sensing imagery

Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances

MAPMaN: Multi‐Stage U‐Shaped Adaptive Pattern Matching Network for Semantic Segmentation of Remote Sensing Images

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