Multi-view clustering (MVC) aims to integrate complementary information from multiple views to enhance clustering performance. Late Fusion Multi-View Clustering (LFMVC) has shown promise by synthesizing diverse clustering results into a unified consensus. However, current LFMVC methods struggle with noisy and redundant partitions and often fail to capture high-order correlations across views. To address these limitations, we present a novel theoretical framework for analyzing the generalization error bounds of multiple kernel k-means, leveraging local Rademacher complexity and principal eigenvalue proportions. Our analysis establishes a convergence rate of O(1/n), significantly improving upon the existing rate in the order of O(sqrt(k/n)). Building on this insight, we propose a low-pass graph filtering strategy within a multiple linear K-means framework to mitigate noise and redundancy, further refining the principal eigenvalue proportion and enhancing clustering accuracy. Experimental results on benchmark datasets confirm that our approach outperforms state-of-the-art methods in clustering performance and robustness.

As data sources and modalities become more diverse, existing multi-view graph clustering methods face high computational complexity, hindering scalability. Bipartite graph clustering overcomes this by using anchors to build association graphs, reducing complexity to linear time. However, the quality of bipartite graphs and the neglect of higher-order correlations remain significant limitations. To tackle these challenges, we propose MCBGF, a scalable bipartite graph-based multi-view clustering method enhanced by consensus graph filtering. By integrating the original bipartite structure to counteract degradation from over-smoothing in high-order filters, MCBGF achieves robust and consistent performance. With linear time complexity, MCBGF efficiently processes large-scale datasets and consistently outperforms state-of-the-art methods in experimental evaluations. The code has been released at https://github.com/sxuHui/MCBGF.

Earth observation (EO) data have shown promise in predicting species richness of vascular plants ($\alpha $-diversity), but extending this approach to large spatial scales is challenging because geographically distant regions may exhibit different compositions of plant species ($\beta $-diversity), resulting in a location-dependent relationship between richness and spectral measurements. In order to handle such geolocation dependence, we propose Spatioformer, where a novel geolocation encoder is coupled with the transformer model to encode geolocation context into remote sensing imagery. The Spatioformer model compares favorably to state-of-the-art models in richness predictions on a large-scale ground-truth richness dataset harmonized Australian vegetation plot (HAVPlot) that consists of 68 170 in situ richness samples covering diverse landscapes across Australia. The results demonstrate that geolocational information is advantageous in predicting species richness from satellite observations over large spatial scales. With Spatioformer, plant species richness maps over Australia are compiled from the Landsat archive for the years from 2015 to 2023. The richness maps produced in this study reveal the spatiotemporal dynamics of plant species richness in Australia, providing supporting evidence to inform effective planning and policy development for plant diversity conservation. Regions of high richness prediction uncertainties are identified, highlighting the need for future in situ surveys to be conducted in these areas to enhance the prediction accuracy.

Target speaker information can be utilized in speech enhancement (SE) models to more effectively extract the desired speech. Previous works introduce the speaker embedding into speech enhancement models by means of concatenation or affine transformation. In this paper, we propose a speaker attentive module to calculate the attention scores between the speaker embedding and the intermediate features, which are used to rescale the features. By merging this module in the state-of-the-art SE model, we construct the personalized SE model for ICASSP Signal Processing Grand Challenge: DNS Challenge 5 (2023). Our system achieves a final score of 0.529 on the blind test set of track1 and 0.549 on track2.

Diversity of terrestrial plants plays a key role in maintaining a stable, healthy, and productive ecosystem. Though remote sensing has been seen as a promising and cost-effective proxy for estimating plant diversity, there is a lack of quantitative studies on how confidently plant diversity can be inferred from spaceborne hyperspectral data. In this study, we assessed the ability of hyperspectral data captured by the DLR Earth Sensing Imaging Spectrometer (DESIS) for estimating plant species richness in the Southern Tablelands and Snowy Mountains regions in southeast Australia. Spectral features were firstly extracted from DESIS spectra with principal component analysis, canonical correlation analysis, and partial least squares analysis. Then regression was conducted between the extracted features and plant species richness with ordinary least squares regression, kernel ridge regression, and Gaussian process regression. Results were assessed with the coefficient of correlation$(r)$and Root-Mean-Square Error (RMSE), based on a two-fold cross validation scheme. With the best performing model,$r$is 0.71 and RMSE is 5.99 for the Southern Tablelands region, while$r$is 0.62 and RMSE is 6.20 for the Snowy Mountains region. The assessment results reported in this study provide supports for future studies on understanding the relationship between spaceborne hyperspectral measurements and terrestrial plant biodiversity.

With supervised machine learning algorithms, meaningful information can be extracted from satellite images to support rice farm mapping. The success of these algorithms depends largely on the availability and quality of ground-truth reference data. However, collecting such data is often laborious and time-consuming. The fast development of drone technique has opened up an efficient way for ground truthing. In this study, we construct a drone-based sensing system for the purpose of efficiently collecting ground-truth data. The drone carries dual cameras that provide multispectrsal images with high spatial resolution in the same sensed site. The system is dedicated to collecting training data for rice farm mapping in Australia. To demonstrate the ability of the constructed system, real-flight experiments were conducted. Drone images of rice crops were acquired in the Riverina region of Australia, during the 2018-2019 summer season. Three-dimensional models were constructed from multiple images captured by the drone, where the structural information of rice crops was extracted. Results show that the configuration of dual cameras and the construction of three-dimensional models are particularly advantageous for ground truthing, providing valuable information for reliably identifying rice crops.

The payload Multispectral Instrument (MSI) on the satellite Sentinel- 2A provides data with strong spectral information, reasonable spatial resolution and good revisit time, which make them suitable for crop monitoring. With the availability of the image data over the complete rice growing season over two consecutive years, spectral time series analysis of rice crops is conducted in this study for the Riverina region of Coleambally, New South Wales, Australia. Vegetation and water indices are adopated to compare the growing patterns of rice over the 2015/2016 and 2016/2017 growing seasons. Rice crops of different varieties are identified and examined. Different seed sowing methods are also compared in terms of their effect on the latter season. The results show that the growth pattern of rice follows a particular trend that can be distinguished from other types of vegetation. This is facilitated by a property unique to rice where water sensitive indices produce a higher reading than vegetation indices during the initial flooding period of the season, after which, the crop growth reverses this and the biomass sensitive index becomes larger. Spectral analysis of rice crops planted via various methods reveals that the drill sowing method did not produce this unique characteristic as the late flooding time results in a very short submersion period for the rice seedlings, which is a valuable finding.

The explosive availability of remote sensing images has challenged supervised classification algorithms such as Support Vector Machines (SVM), as training samples tend to be highly limited due to the expensive and laborious task of ground truthing. The temporal correlation and spectral similarity between multitemporal images have opened up an opportunity to alleviate this problem. In this study, a SVM-based Sequential Classifier Training (SCT-SVM) approach is proposed for multitemporal remote sensing image classification. The approach leverages the classifiers of previous images to reduce the required number of training samples for the classifier training of an incoming image. For each incoming image, a rough classifier is firstly predicted based on the temporal trend of a set of previous classifiers. The predicted classifier is then fine-tuned into a more accurate position with current training samples. This approach can be applied progressively to sequential image data, with only a small number of training samples being required from each image. Experiments were conducted with Sentinel-2A multitemporal data over an agricultural area in Australia. Results showed that the proposed SCT-SVM achieved better classification accuracies compared with two state-of-the-art model transfer algorithms. When training data are insufficient, the overall classification accuracy of the incoming image was improved from 76.18% to 94.02% with the proposed SCT-SVM, compared with those obtained without the assistance from previous images. These results demonstrate that the leverage of a priori information from previous images can provide advantageous assistance for later images in multitemporal image classification.

Multi-temporal remote sensing imagery has become widely available, which opens up an opportunity to improve the efficiency of supervised classification techniques. While a classifier trained from a previous image (source domain) cannot be directly applied to the current image (target domain) because of changes in imaging conditions and dynamics of land surface spectral properties, domain transfer techniques have been introduced in recent years to remove the need for a complete retraining of the current image data. This approach is further developed in the present study, and a domain transfer algorithm named Temporal-Adaptive Support Vector Machine (TASVM) is proposed. The algorithm enables the adaptation of a classifier trained with the source-domain image to the classification of the target-domain image where class data have a different distribution. The adaptation process is allowed to be conducted at the classifier-level where the source classifiers can be transferred without re-accessing the source domain raw data. Experimental analysis showed that the proposed algorithm generated stable results, especially under the circumstances where satisfactory results were hard to achieve with traditional algorithms.

Considering that satellites rarely acquire data from the exact nadir direction, angular effect normalization needs to be conducted as an important preprocessing step to correct reflectance observations from off-nadir directions into the nadir direction. Kernel-based bidirectional reflectance distribution function models have been employed for angular effect correction. The kernels used in the model are often predetermined and fixed for an entire image. However, the fixed kernels are unable to accommodate the various reflective characteristics of different ground cover types present in the imaged area. In this paper, we propose a kernel learning procedure that enables the flexible selection of kernels for different land cover types within a scene. The kernels are selected from kernel dictionaries that contain multiple candidate kernels. The selection is conducted on the superpixel level instead of the pixel level in order to reduce within-class variation and overcome the overfitting problem. Experiments are conducted on multiangular images acquired by the Sentinel-2A satellite over a rural area in southeastern Australia. Cross-validation results show that the proposed method is able to adaptively select appropriate kernels for different land cover types, leading to an improved performance for image normalization.

The existing kernel-based methods for retrieving land surface bidirectional reflectance distribution functions (BRDFs) usually use a pre-determined combination of kernels and it is fixed for an entire image, which is unable to accommodate the different characteristics from various land cover types. In this study, a multi-kernel method based on l1-norm optimization is proposed. This method is able to automatically select appropriate kernels for each pixel from a kernel dictionary that contains several commonly used kernels. Experimental results show that BRDF retrieval accuracy is improved by adopting this new method.