Robust Shadow Detection by Exploring Effective Shadow Contexts

Fang, Xianyong; He, Xiaohao; Wang, Linbo; Shen, Jianbing

doi:10.1145/3474085.3475199

Cited by 11 publications

(2 citation statements)

References 37 publications

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“…RMU-Net outperforms the other four networks in prediction accuracy, characterized by minimal false positives and negatives, smooth textures, distinct boundaries, and detailed precision. In contrast, Res-Net, U 2 -Net, and ECANet [55] results are marked by multiple holes, mottled textures, increased false positives, fragmented patterns, and general disarray. MSASDNet's [56] outcomes feature fragmented patterns and less nuanced detail.…”

Section: B Analysis Of Shadow/side Extraction Results 1) Qualitative ...mentioning

confidence: 82%

Building Height Extraction From High-Resolution Single-View Remote Sensing Images Using Shadow and Side Information

Xu,

Feng,

Wan

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Extracting building heights from single-view remote sensing images greatly enhances the application of remote sensing data. While methods for extracting building height from single-view shadow images have been widely studied, it remains a challenging task. The main reasons are as follows: (1) The traditional method for extracting shadow information exhibits low accuracy. (2) The use of only shadow information to extract building height results in limited application scenarios. To solve the above problems, this paper introduces building side and shadow information to complement each other, and proposes a building height extraction method from high-resolution single-view remote sensing images using shadow and side information. Firstly, we propose the RMU-Net method, which utilizes multi-scale features for the extraction of shadow and side information. This method aims to address issues related to pixel detail loss and imprecise edge segmentation, which result from significant scale differences within segmentation targets. Additionally, we employ the area threshold method to optimize the segmentation results, specifically to tackle small stray patches and holes, enhancing the overall integrity and accuracy of shadow and side information extraction. Secondly, we propose a method for building height extraction that integrates shadow and side information based on an enhanced proportional coefficient model. The accuracy of measuring building side and shadow lengths is improved by incorporating the fishing net method, informed by our analysis of the geometric relationships among buildings. Finally, we establish a dataset containing building shadow and side information from remote sensing images, and select multiple areas for experimental analysis. The results demonstrate a shadow extraction accuracy of 91.03% and a side extraction accuracy of 90.29%. Additionally, the average absolute error (MAE) for building height extraction is 1.22, while the average root mean square error (RMSE) is 1.21. Furthermore, the proposed method's validity and scalability are affirmed through experimental analyses of applicability and anti-interference performance in extensive areas.

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Section: B Analysis Of Shadow/side Extraction Results 1) Qualitative ...mentioning

confidence: 82%

Building Height Extraction From High-Resolution Single-View Remote Sensing Images Using Shadow and Side Information

Xu,

Feng,

Wan

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Interestingly, Transformer [5] can capture the long-range relations. Besides, the parallel structures in the convolutional neural networks (CNN)-based studies, Inception [10] and its variants [11][12][13][14] have been demonstrated to be very effective with rich scales.…”

Section: Introductionmentioning

confidence: 99%

Parallel matters: Efficient polyp segmentation with parallel structured feature augmentation modules

Guo

Fang

Wang

et al. 2023

IET Image Processing

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The large variations of polyp sizes and shapes and the close resemblances of polyps to their surroundings call for features with long-range information in rich scales and strong discrimination. This article proposes two parallel structured modules for building those features. One is the Transformer Inception module (TI) which applies Transformers with different reception fields in parallel to input features and thus enriches them with more long-range information in more scales. The other is the Local-Detail Augmentation module (LDA) which applies the spatial and channel attentions in parallel to each block and thus locally augments the features from two complementary dimensions for more object details. Integrating TI and LDA, a new Transformer encoder based framework, Parallel-Enhanced Network (PENet), is proposed, where LDA is specifically adopted twice in a coarse-tofine way for accurate prediction. PENet is efficient in segmenting polyps with different sizes and shapes without the interference from the background tissues. Experimental comparisons with state-of-the-arts methods show its merits.

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