Meijuan Yang's research while affiliated with Northwestern Polytechnical University and other places
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Publications (9)
Introduction
Current detection methods for apple leaf diseases still suffer some challenges, such as the high number of parameters, low detection speed and poor detection performance for small dense spots, which limit the practical applications in agriculture. Therefore, an efficient and accurate model for apple leaf disease detection based on YOLO...
Convolutional neural networks (CNNs) have superior feature learning capabilities with large numbers of labeled samples. The reality is that labeling these samples is costly in terms of human labor. Existing data augmentation methods alleviate the scarcity of labeled samples. However, these methods are not suitable for synthetic aperture radar (SAR)...
Contrastive self-supervised learning (CSSL) is a promising method in extracting effective features from unlabeled data. It performs well in image-level tasks, such as image classification and retrieval. However, the existing CSSL methods are not suitable for pixel-level tasks, e.g., change detection (CD), since they ignore the correlation between l...
Contrastive self-supervised learning (CSSL) has achieved promising results in extracting visual features from unlabeled data. Most of the current CSSL methods are used to learn global image features with low-resolution that are not suitable or efficient for pixel-level tasks. In this paper, we propose a coarse-to-fine CSSL framework based on a nove...
Touzi decomposition provides more scattering information corresponding to parameters describing different targets. It is possible to explore the selection method of parameters, which plays an important role in polarimetric synthetic aperture radar (PolSAR) image classification. Therefore, this paper presents an innovative parameter ordering scheme...
Due to the complementary properties of different types of sensors, change detection between heterogeneous images receives increasing attention from researchers. However, change detection cannot be handled by directly comparing two heterogeneous images since they demonstrate different image appearances and statistics. In this article, we propose a d...
Supervised change detection methods always face a big challenge that the current scene (target domain) is fully unlabeled. In remote sensing, it is common that we have sufficient labels in another scene (source domain) with a different but related data distribution. In this article, we try to detect changes in the target domain with the help of the...
Synthetic aperture radar (SAR) image classification is an important part in the understanding and interpretation of SAR images. Each patch in SAR images has a scene category, but usually contains multiple land-cover classes or latent properties, which can be represented by topics in the probabilistic topic model (PTM). The representation and select...
Supervised deep neural networks (DNNs) have been extensively used in diverse tasks. Generally, training such DNNs with superior performance requires a large amount of labeled data. However, it is time-consuming and expensive to manually label the data, especially for tasks in remote sensing, e.g., change detection. The situation motivates us to res...
Citations
... In recent years, with the continuous improvement in the volume and spatial resolution of remote sensing images covering the world [15], compared with supervised learning models that use high-cost labeled data as supervised signals, self-supervised contrastive learning models driven by a large number of unlabeled data are expected to become an effective solution for large-scale land cover classification with limited labeled data [16][17][18][19][20]. ...
... Some of these methods do feature extraction and classification in two individual steps. For example, various target decomposition methods are used for scattering and polarimetric feature extraction [35,45], and some special transforms are used for contextual feature extraction [9,40]. For example, multi-scale decompositions such as different versions of wavelets, curvelets, contourlet and Gabor filters are used for shape and texture feature extraction [8,21,26,36]; mathematical approaches such as morphological filters by reconstruction are utilized to provide the morphological profile (MP) of data containing shape and geometrical characteristics [17,31]; or statistical based methods such as gray-level co-occurrence matrix (GLCM) are used for texture feature extraction [3,28]. ...
... He and Yang, S.Y., Liu, F., Hou, B.A. and Li, L.L. are highly connected to each other. Their research areas include computer science [81][82][83], automation and control systems [84], engineering [85], science and technology, operations research management science, neurosciences and neurology, and telecommunications. ...
... Being a form of radar, synthetic aperture radar (SAR) is capable of high-resolution remote sensing. Moreover, due to its all-time, all-weather, and large-scale observation capabilities, the SAR system can provide detailed information about the monitored region and, thus, plays an important role in many applications of both military and civilian fields [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. For example, the SAR can gather systematically high-quality data of a city and help build a smart city [18]. ...
... In this subsection, we focus on remote sensing change and anomaly detection, object detection and tracking. They play critical roles in detecting and preventing non-agricultural events, air defense and surveillance etc. 1) Transformer for Change & Anomaly Detection: Compared with hand-crafted methods, CNN-based remote sensing change detection methods can robustly model some complex change types [203], [205], [207], [453]- [460]. Transformer shows excellent potential for change detection tasks with some challenges [3], [16], [21], [111], [208]. ...
... These blurry phenomena severely diminish the perceptual capabilities of both humans and devices in terms of image quality and information content. Consequently, the accuracy and precision of advanced visual tasks like image segmentation [3], autonomous driving [4], satellite monitoring [5], among others, are negatively impacted. ...
