In the past era of explosive data growth, how to deal with large-scale, unlabeled remote sensing images (RSIs) has become a concern. Due to the lack of data labels, unsupervised methods are usually used to deal with them. As one of the best unsupervised algorithms, spectral clustering (SC) is a very effective data processing and analysis technology. However, the scalability of SC affects its development in the era of big data. Deep network technology has developed rapidly in the past, and it is often used to solve the problem of out-of-sample expansion (OOSE) that traditional machine learning cannot solve. However, because RSI is usually high-dimensional data, it is easy to cause dimension explosion in deep networks. The main motivation of this work is to solve the above problems. We have designed a new algorithm called deep SC with projected adaptive feature selection (DSCFS), which benefits from deep learning theory and feature projection technology. We use a neural network to map RSI data and then further process the data through regularization embedding. At the same time, adaptive feature projection is applied to extract the main features of RSI, and the loss feedback network is calculated through these two steps. A large number of experiments show that the performance of our proposed method is better than other mainstream methods.

Unsupervised feature selection (UFS)is an important technology for dimensionality reduction and has gained great interest in a wide range of fields. Recently, most popular methods are spectral-based which frequently use adaptive graph constraints to promote performance. However, no literature has considered the grouping characteristic of the data features, which is the most basic and important characteristic for arbitrary data. In this paper, based on the spectral analysis method, we first simulate the data feature grouping characteristic. Then, the similarity between data is adaptively reconstructed through the similarity between groups, which can explore the more fine-grained relationship between data than the previous adaptive graph methods. In order to achieve the aforementioned goal, the local similarity matrix and the global similarity matrix are defined, and the weighted KL entropy is used to constrain the relationship between the global similarity matrix and the local similarity matrices. Furthermore, the symmetrical self-representation structure is used to improve the performance of the reconstruction error term in the conventional spectral-based methods. After the model is constructed, a simple but efficient algorithm is proposed to solve the full model. Extensive experiments on 8 benchmark dataset with different types to show the effectiveness of the proposed method.

Apple leaf diseases seriously affect the quality of apples and may lead to yield losses, detecting apple leaf diseases accurately can prevent diseases from spreading and promote the healthy growth of the industry. However, recent studies cannot achieve accurate detection of leaf diseases with high accuracy because the lesions are of different sizes. So, this paper proposed a novel apple leaf disease detection method called VMF-SSD (V-space-based Multi-scale Feature-fusion SSD), which is designed to extract more reliable multi-scale feature representations for varied sizes of diseased spots and improve the final detection performance. The multi-scale feature extraction is established with multi-scale feature representation to further improve the disease detection performance, especially for small spots. After that, a V-space-based location branch is presented to enhance the texture feature information and help further identify disease spot location. Finally, attention mechanisms are utilized to automatically learn the importance of feature channels at different scales for distinguishing diseased spots of different sizes. Experimental results showed that the VMF-SSD method achieves 83.19% mAP and obtains the detection speed of 27.53 FPS on the test set, which indicates that the proposed VMF-SSD method can achieve competitive performance on apple leaf diseases detection task and satisfy the requirements of agricultural production applications.

Aphids, brown spots, mosaics, rusts, powdery mildew and Alternaria blotches are common types of early apple leaf pests and diseases that severely affect the yield and quality of apples. Recently, deep learning has been regarded as the best classification model for apple leaf pests and diseases. However, these models with large parameters have difficulty providing an accurate and fast diagnosis of apple leaf pests and diseases on mobile terminals. This paper proposes a novel and real-time early apple leaf disease recognition model. AD Convolution is firstly utilized to replace standard convolution to make smaller number of parameters and calculations. Meanwhile, a LAD-Inception is built to enhance the ability of extracting multiscale features of different sizes of disease spots. Finally, the LAD-Net model is built by the LR-CBAM and the LAD-Inception modules, replacing a full connection with global average pooling to further reduce parameters. The results show that the LAD-Net, with a size of only 1.25MB, can achieve a recognition performance of 98.58%. Additionally, it is only delayed by 15.2ms on HUAWEI P40 and by 100.1ms on Jetson Nano, illustrating that the LAD-Net can accurately recognize early apple leaf pests and diseases on mobile devices in real-time, providing portable technical support.

Unsupervised feature selection can play an important role in addressing the issue of processing massive unlabelled high-dimensional data in the domain of machine learning and data mining. This paper presents a novel unsupervised feature selection method, referred to as Multi-Group Adaptive Graph Representation (MGAGR). Different from existing methods, the relationship between features is explored via the global similarity matrix, which is reconstructed by local similarities of multiple groups. Specifically, the similarity of a feature compared to other features can be represented by the linear combination of all the local similarities. The local similarity of a representative group is given a large weight to reconstruct the global similarity. Besides, an iterative algorithm is given to solve the optimization problem, in which the global similarity matrix, its corresponding reconstruction weights and the self-representation matrix are iteratively improved. Experimental results on 8 benchmark datasets demonstrates that the proposed method outperforms the state-of-the-art unsupervised feature selection methods in terms of clustering performance. The source code is available at:https://github.com/misteru/MGAGR.

In the fields of machine learning and data mining, unsupervised feature selection plays an important role in processing large amounts of high-dimensional unlabeled data. This paper proposes an original and novel unsupervised feature selection based on feature grouping and orthogonal constraints. We consider the domain relationship in the original data and reconstruct the similarity matrix based on the correlation between the features. We use a generalized incoherent regression model based on orthogonal constraints. Furthermore, a graph regularization term with local structure preservation constraints is added to ensure that the feature subset does not lose local structural features in the original data space. Besides, an iterative algorithm is proposed to solve the optimization problem by iteratively updating the global similarity matrix, and constructing weight matrix, pseudo-label matrix and transformation matrix. Through experiments on 6 benchmark datasets, the clustering performance of the proposed method outperforms state-of-the-art unsupervised feature selection methods. The source code is available at: https://github.com/misteru/FGOC.

Hyperspectral image denoising can play an important role in addressing the issue of preprocessing massive high-dimensional hyperspectral data for subsequent object detection or classification. This paper presents a novel low-rank and semi-nonnegative tensor factorization method for HSI denoising. The framelet regularization is introduced to constrain the reduced-dimensionality factor, rather than directly regularizing HSI itself in the low-rank and non-negative tensor factorization model. Thus, our method preserves the details and geometric features of restored HSI in the spatial domain and demand much less calculation and computer memory. Extensive experimental results show that our method is superior to other existing methods for HSI denoising in simulated benchmark datasets. Our source code is available at: https://github.com/misteru/LRSNTF.

For hyperspectral image, it is a challenging task to select informative and distinctive bands due to the lack of labeled samples and massive redundancy. To address this issue, we propose a new unsupervised band selection method via Sparse Principal Component Analysis and Adaptive Multiple Graph Learning (SPCA-AMGL). Based on PCA, it proposes a Sparse PCA with L 2,1 norm sparse constraint, which can effectively select the bands with high information and low correlation. In addition, an adaptive multiple graph learning is used for manifold-preserving, which ensures that the bands containing abundant spatial structure information are preserved. Specifically, it constructs multiple initial similarity graphs with different distance metrics, and then learns an adaptive graph from them. In this way, it overcomes the shortcoming of insufficient intrinsic structure of data learned from a single graph. Experimental result on Indian Pines data set proves the effectiveness and advancement of SPCA-AMGL. The source code is available at: https://github.com/ZWX0823/SPCA-AMGL.

Unsupervised feature selection (UFS) aims to remove the redundant information and select the most representative feature subset from the original data, so it occupies a core position for high-dimensional data preprocessing. Many proposed approaches use self-expression to explore the correlation between the data samples or use pseudolabel matrix learning to learn the mapping between the data and labels. Furthermore, the existing methods have tried to add constraints to either of these two modules to reduce the redundancy, but no prior literature embeds them into a joint model to select the most representative features by the computed top ranking scores. To address the aforementioned issue, this article presents a novel UFS method via a convex non-negative matrix factorization with an adaptive graph constraint (CNAFS). Through convex matrix factorization with adaptive graph constraint, it can dig up the correlation between the data and keep the local manifold structure of the data. To our knowledge, it is the first work that integrates pseudo label matrix learning into the self-expression module and optimizes them simultaneously for the UFS solution. Besides, two different manifold regularizations are constructed for the pseudolabel matrix and the encoding matrix to keep the local geometrical structure. Eventually, extensive experiments on the benchmark datasets are conducted to prove the effectiveness of our method. The source code is available at: https://github.com/misteru/CNAFS.

Vision-to-language tasks aim to integrate computer vision and natural language processing together, which has attracted the attention of many researchers. For typical approaches, they encode image into feature representations and decode it into natural language sentences. While they neglect high-level semantic concepts and subtle relationships between image regions and natural language elements. To make full use of these information, this paper attempt to exploit the text-guided attention and semantic-guided attention (SA) to find the more correlated spatial information and reduce the semantic gap between vision and language. Our method includes two-level attention networks. One is the text-guided attention network which is used to select the text-related regions. The other is SA network which is used to highlight the concept-related regions and the region-related concepts. At last, all these information are incorporated to generate captions or answers. Practically, image captioning and visual question answering experiments have been carried out, and the experimental results have shown the excellent performance of the proposed approach.