Semantic change detection (SCD) extends the multi-class (MCD) task to provide not only locations but also detailed land-cover/land-use (LCLU) categories before and after observation intervals. This fine-grained semantic information is very useful in many applications. Recent studies indicate that SCD can be modeled through a triple-branch Convolutional Neural Network (CNN), which contains two temporal branches branch. However, this architecture, communications between branch are...

Semantic Change Detection (SCD) refers to the task of simultaneously extracting changed areas and semantic categories (before after changes) in Remote Sensing Images (RSIs). This is more meaningful than Binary (BCD) since it enables detailed change analysis observed areas. Previous works established triple-branch Convolutional Neural Network (CNN) architectures as paradigm for SCD. However, remains challenging exploit information with a limited amount samples. In this work, we investigate...

Classifying remote sensing images is vital for interpreting image content. Presently, scene classification methods using convolutional neural networks have drawbacks, including excessive parameters and heavy calculation costs. More efficient lightweight CNNs fewer calculations, but their performance generally weaker. We propose a more network method to improve accuracy with small training dataset. Inspired by fine-grained visual recognition, this study introduces bilinear model...

Deep learning has achieved great success in remote sensing image change detection (CD). However, most methods focus only on the changed regions of images and cannot accurately identify their detailed semantic categories. In addition, CD using convolutional neural networks (CNN) have difficulty capturing sufficient global information from images. To address above issues, we propose a novel symmetric multi-task network (SMNet) that integrates local for (SCD) this paper. Specifically, employ...

Remote sensing image scene classification is an important means for the understanding of remote images. Convolutional neural networks (CNNs) have been successfully applied to and demonstrated remarkable performance. However, with improvements in resolution, categories are becoming increasingly diverse, problems such as high intraclass diversity interclass similarity arisen. The performance ordinary CNNs at distinguishing complex images still limited. Therefore, we propose a feature fusion...

Unsupervised Change Detection (UCD) in multimodal Remote Sensing (RS) images remains a difficult challenge due to the inherent spatio-temporal complexity within data, and heterogeneity arising from different imaging sensors. Inspired by recent advancements Visual Foundation Models (VFMs) Contrastive Learning (CL) methodologies, this research aims develop CL methodologies translate implicit knowledge VFM into change representations, thus eliminating need for explicit supervision. To end, we...

The task of visual geo-localization based on street-view images estimates the geographical location a query image by recognizing nearest reference in geo-tagged database. This holds considerable practical significance domains such as autonomous driving and outdoor navigation. Current approaches typically use perspective images. However, lack scene content resulting from restricted field view (FOV) is main cause inaccuracies matching localizing with same global positioning system (GPS)...

The detection and recognition of oriented objects in remote sensing images is a challenging task due to their complex backgrounds, various sizes, diverse aspect ratios, especially arbitrary orientations. Many object algorithms need obtain accurate angles or adopt anchors predict the bounding boxes. When directly predicting objects' boxes, loss angle discontinuous during training, which makes it difficult boundary objects. And also aggravate problems class imbalance computational cost. To...

Through a quasi-experimental approach, we compared Chinese college students’ learning motivation, content knowledge, English language proficiency, and instructor’s pedagogical practices between an English-medium instruction (EMI) the parallel Chinese-medium (CMI) course in non-traditional discipline. Results indicated that EMI was more effective, as to CMI, motivating of focal subject. More specifically, students held stronger external goal orientation than did their CMI peers. Further,...

Characterized by complicated backgrounds, various types, large size variations, and arbitrary orientations, the detection recognition of arbitrary-oriented objects in remote sensing images are challenging. To address aforementioned problem, an anchor-free object detector using box boundary-aware vectors is proposed. With idea CenterNet to detect as points, oriented achieved predicting center, vectors, size, type bounding box. In feature extraction stage designed architecture, Res2Net, a...

This study investigated the psychometric properties of an adapted Chinese version Motivated Strategies for Learning Questionnaire (MSLQ) using exploratory factor analysis and confirmatory analysis. Data were collected from 611 college students two universities on scales Motivation Strategies. Results suggested that cross-cultural adaptation modification necessary when addressing transferability self-regulated learning (SRL) models Western culture to Eastern culture, particularly in...

Remote sensing for image object detection has numerous important applications. However, complex backgrounds and large object-scale differences pose considerable challenges in the task. To overcome these issues, we proposed a one-stage remote model: multi-feature information complementary detector (MFICDet). This contains positive negative feature guidance module (PNFG) global (GFIC). Specifically, PNFG is used to refine features that are beneficial explore noisy background of abstract...

Point cloud classification of airborne light detection and ranging (LiDAR) data is essential to extract geoinformation. Although deep learning provides a new approach for classification, the time-consuming training process dependence prevent its widespread application point clouds. To solve these problems leverage potential high-performing neural networks, we propose an LiDAR method based on transfer learning. A strategy generate feature images considering spatial distribution first...

Scene classification is an important and challenging task employed toward understanding remote sensing images. Convolutional neural networks have been widely applied in scene recent years, boosting accuracy. However, with improvements resolution, the categories of images become ever more fine-grained. The high intraclass diversity interclass similarity are main characteristics that differentiate image from natural classification. To extract discriminative representation images, we propose...

Object detection is used widely in remote sensing image interpretation. Although most models for object have achieved high accuracy, computational complexity and low speeds limit their application real-time tasks. This study developed an adaptive feature-aware method of images based on the single-shot detector architecture called (AFADet). Self-attention to extract high-level semantic information derived from deep feature maps spatial localization objects model improved localizing objects....

Coastline extraction from remote sensing images has become an indispensable means in island coastal survey, which plays a very important role automatic navigation, targeting and mapping work. Image segmentation boundary are ways to obtain the position of coastline. And active contour model tool for image extraction. So this paper we try use extract However excessive timeconsumption low degree automation two aspects need resolve. Aiming this, quadtreemethod initial contour, is close...

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This study examined psychometric properties and measurement invariance of the Motivated Strategies for Learning Questionnaire Chinese adult learners, learning strategy scale (MSLQ-CAL-LS). Data were collected from 2499 college students 15 universities. Results factor analysis suggested satisfactory MSLQ-CAL-LS. We further identified strong evidence to support configural, metric, scalar strict across gender groups, confirming appropriate use MSLQ-CAL-LS that can accurately capture construct...

In this paper, a new algorithm based on the wavelet transform (WT) is proposed for voice activity detection (VAD). It utilizes difference of spectral distribution between and noise. First, performs to signal decomposes it into subbands using bandpass filtering feature WT; then detects in by comparing subband energy detail components Computer simulation results are given illustrate effectiveness VAD algorithm.

In order to make full use of local neighborhood information for high-resolution remote sensing images, this study combined iterative slow feature analysis (ISFA) and stacked denoising autoencoder (SDAE) improve the change detection precision. First, approach introduced ISFA initial in an unsupervised way, which enlarged separability changed unchanged areas. Then, by setting different membership degrees, samples were obtained through fuzzy-means clustering. Finally, model was built SDAE...

In recent years, registration between the remote sensing image and vector data has seen its increasing usage in photogrammetry-related applications. Many researchers select higher-level primitives instead of traditional control points since disadvantages point features. This paper proposed an automatic approach based on line features, which is insensitive to rotational scale transformation. this approach, similarity measure established according distance conjugate entities same reference...

The goal of cross-view image based geo-localization is to determine the location a given street-view by matching it with collection geo-tagged aerial images, which has important applications in fields remote sensing information utilization and augmented reality. Most current methods focus on content ignore relations between feature nodes, resulting insufficient mining effective information. To address this problem, study proposes relation guided geo-localization. This method first processes...

Navigation landmark features such as docks are often used in ships for localization and searching shore targets during the voyage, which of great economic military significance. Continuous complete dock data could hardly be extracted by existing coast extraction method, because spatial relationships other characteristics ignored only considers grayscale remote sensing image .A method based on waterline perceptual organization is proposed this paper. To make full use relationships, introduced...