High-Resolution Remote-Sensing Image-Change Detection Based on Morphological Attribute Profiles and Decision Fusion

Wang, Chao; Liu, Hui; Shen, Yi; Zhao, Kunmin; Xing, Hongyan; Wu, Hongyou

doi:10.1155/2020/8360361

Cited by 7 publications

(5 citation statements)

References 32 publications

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“…Other CNN variants architectures are proposed in [1], [27], [50], [59], [60] which use CWNN, deep CVA, and self-paced learning, respectively. In addition, complex architectures that use Markov chains, spectral unmixing, regression-based learning, and decision fusion are mentioned in [61]. These secondary methods can be used to assemble CNNs for multi-algorithmic applications.…”

Section: Discussion and Summarymentioning

confidence: 99%

Change detection using multispectral satellite images: a systematic review of literature

Vasantrao,

Gupta,

Mishra

et al. 2024

Bulletin EEI

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Change detection (CD) provides information about the changes on earth’s surface over a period of time. Many algorithms have been proposed over the years for effective CD of satellite imagery. This paper presents the steps to preprocess the captured satellite images, which can be used to perform predictive analysis of earth’s surface by CD techniques. To design a highly effective system for CD, it is recommended that algorithm designers select efficient algorithms from any given application. Thus, this paper presents and investigates the review of most appropriate literature on CD, where CD techniques have been presented into three groups; i) thresholding, ii) clustering, and iii) deep learning. The first two categories mainly rely on the traditional machine learning, whereas the last one exploits the state-of-the-art deep learning models. At the end, the standard methods are summarized based on advantages, limitation, and research gap.

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Section: Discussion and Summarymentioning

confidence: 99%

Change detection using multispectral satellite images: a systematic review of literature

Vasantrao,

Gupta,

Mishra

et al. 2024

Bulletin EEI

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“…On the other hand, the traditional decision-making method of taking the union directly for the DAPs corresponding to different attributes ignores the evidential conflict and redundant information. Therefore, based on Dempster-Shafer (D-S) evidence theory, this paper proposed an unsupervised decision fusion framework combining ACGA-DAPs and image segmentation [5].…”

Section: Construct An Unsupervised Decision Fusion Frameworkmentioning

confidence: 99%

“…In recent years, morphological attribute profiles (MAPs) have been proven to have a strong ability to detect buildings in complex urban backgrounds, which has been one of the most effective spatial structure modelling methods for HRRS images. The morphological feature set of local area constructed by MAPs can be used to realize the multi-attribute and multi-scale expression of different ground objects, thus significantly improving the separability of buildings and other ground objects [5][6][7]. However, the following limitations must be overcome to realize high-precision, unsupervised building detection based on MAPs: (1) The potential building pixels are directly determined by the differential attribute profiles (DAPs) extracted from the differential of neighboring attribute profiles (APs), and morphological attribute profile (MAP) theory does not give a scale parameter setting using clear rules, so the requirement according to the scale of the original image is used to construct (on an adaptive basis) a reasonable parameter set.…”

Section: Introductionmentioning

confidence: 99%

Automatic Building Detection from High-Resolution Remote Sensing Images Based on Joint Optimization and Decision Fusion of Morphological Attribute Profiles

et al. 2021

Self Cite

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High-resolution remote sensing (HRRS) images, when used for building detection, play a key role in urban planning and other fields. Compared with the deep learning methods, the method based on morphological attribute profiles (MAPs) exhibits good performance in the absence of massive annotated samples. MAPs have been proven to have a strong ability for extracting detailed characterizations of buildings with multiple attributes and scales. So far, a great deal of attention has been paid to this application. Nevertheless, the constraints of rational selection of attribute scales and evidence conflicts between attributes should be overcome, so as to establish reliable unsupervised detection models. To this end, this research proposes a joint optimization and fusion building detection method for MAPs. In the pre-processing step, the set of candidate building objects are extracted by image segmentation and a set of discriminant rules. Second, the differential profiles of MAPs are screened by using a genetic algorithm and a cross-probability adaptive selection strategy is proposed; on this basis, an unsupervised decision fusion framework is established by constructing a novel statistics-space building index (SSBI). Finally, the automated detection of buildings is realized. We show that the proposed method is significantly better than the state-of-the-art methods on HRRS images with different groups of different regions and different sensors, and overall accuracy (OA) of our proposed method is more than 91.9%.

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“…In real recognition, the target is generally stored in the environment will not be so ideal. There will be a series of problems, such as occlusion interference, which makes the target detection face new challenges [4][5].…”

Section: Introductionmentioning

confidence: 99%

Improved YOLOv5s Algorithm for Target Detection in Highlighted Field Camera Surveillance Images

Sun,

Liao,

Zhou

et al. 2024

Applied Mathematics and Nonlinear Sciences

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The complex and changing environment in the field brings a more significant detection challenge to camera surveillance. This paper proposes target detection in camera surveillance images using the improved YOLOv5s algorithm for target tracking in surveillance images. Firstly, to address the weak feature extraction ability of YOLOv5s for small-scale and overlapping targets, the feature extraction performance of YOLOv5s is improved by combining the attention mechanism and substitution loss function in the deep learning network to improve the feature extraction performance of YOLOv5s for target detection in field camera surveillance images. Then, to test the improved algorithm’s performance, the improved algorithm in this paper is compared with SSD, Faster R-CNN, and YOLOv5s detection methods, and performance comparison experiments are done on the basis of the dataset. The results show that the average detection accuracy (MAP) of the algorithm in this paper is improved by 19%, 14.5% and 6.3% than SSD, Faster R-CNN, and YOLOv5s, respectively, and the average speed of detection is 324 FPS, the accuracy is improved, the detection is faster, and for the YOLOv5 m with DA and PT has a higher AP than the other models in this paper. This study enhances the scalability of YOLOv5s algorithm in complex environments, which is crucial for advancing image target detection.

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High-Resolution Remote-Sensing Image-Change Detection Based on Morphological Attribute Profiles and Decision Fusion

Cited by 7 publications

References 32 publications

Change detection using multispectral satellite images: a systematic review of literature

Change detection using multispectral satellite images: a systematic review of literature

Automatic Building Detection from High-Resolution Remote Sensing Images Based on Joint Optimization and Decision Fusion of Morphological Attribute Profiles

Improved YOLOv5s Algorithm for Target Detection in Highlighted Field Camera Surveillance Images

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