Transesterification of cottonseed oil with methanol to biodiesel was investigated in various Brønsted acidic ionic liquids an alkane sulfonic acid group. The properties, structures, and acidities the were experimentally characterized theoretically analyzed. acidity−activity correlation for studied. Among all these liquids, 1-(4-sulfonic acid) butylpyridinium hydrogen sulfate exhibited best catalytic performance, which is ascribed its strong acidity. activity near that concentrated sulfuric...

Ship detection is a challenging problem in complex optical remote-sensing images. In this letter, an effective ship framework images based on the convolutional neural network proposed. The designed to predict bounding box of with orientation angle information. Note that information which added regression makes accurately fit into region. order make model adaptable multiscale targets, especially small-sized ships, we design feature maps from layers different depths. whole pipeline single and...

Object detection is an essential task in computer vision. Recently, several convolution neural network (CNN)-based detectors have achieved a great success natural scenes. However, for optical remote sensing images with large scale of view, lower proportion foreground target pixels and drastic differences object present considerable challenges. To address these problems, we propose novel one-stage detector called the full-scale (FSoD-Net) which consists proposed multiscale enhancement...

Remote sensing image classification (RSIC) is a classical and fundamental task in the intelligent interpretation of remote imagery, which can provide unique labeling information for each acquired image. Thanks to potent global context extraction ability multi-head self-attention (MSA) mechanism, visual transformer (ViT)-based architectures have shown excellent capability natural scene classification. However, order achieve powerful RSIC performance, it insufficient capture spatial alone....

In recent years, convolutional neural network (CNN)-based methods have been widely used for optical remote sensing object detection and shown excellent performance. Some aerospace systems, such as satellites or aircrafts, need to adopt these observe objects on the ground. Due limited budget of logical resources power consumption in an embedded device is a good choice implement CNN-based methods. However, it still challenge strike balance between performance consumption. this paper, we...

Optical remote sensing object detection is a challenging task, because of the complex background interference, ambiguous appearances tiny objects, densely arranged circumstances, and multiclass with vaster scale variances irregular aspect ratios. The performance seriously restricted. Thus, in this article, inspired by anchor-free framework, aiming to solve these difficulties improve optical performance, powerful one-stage detector multiscale semantic fusion-guided fractal convolution network...

Scene classification has become an active research area in remote sensing (RS) image interpretation. Recently, Transformer-based methods have shown great potential modeling global semantic information and been exploited RS scene classification. In this letter, we propose a multi-level fusion Swin Transformer (MFST), which integrates feature merging (MFM) module adaptive compression (AFC) to further boost the performance for The MFM narrows gaps features via patch lower-level maps lateral...

Introduction Maize kernel variety identification is crucial for reducing storage losses and ensuring food security. Traditional single models show limitations in processing large-scale multimodal data. Methods This study constructed an interpretable ensemble learning model maize seed through improved differential evolutionary algorithm data fusion. Morphological hyperspectral of samples were extracted preprocessed, three methods used to screen features, respectively. The base learner the...

Object detection in complex optical remote sensing images is a challenging problem due to the wide variety of scales, densities, and shapes object instances on earth surface. In this letter, we focus wide-scale variation multiclass propose an effective framework based YOLOv2. To make model adaptable multiscale detection, design network that concatenates feature maps from layers different depths adopt introducing strategy oriented response dilated convolution. Through strategy, performance...

In recent times, many efforts have been made to improve remote sensing image scene classification, especially using popular deep convolutional neural networks. However, most of these methods do not consider the specific orientation images. this letter, we propose improved oriented response network (IORN), which is based on ORN, handle problem in classification. We average active rotating filters (A-ARFs) IORN. While IORNs are being trained, A-ARFs updated by a method that different from ARFs...

Currently, under supervised learning, a model pre-trained by large-scale nature scene dataset and then fine-tuned on few specific task labeling data is the paradigm that has dominated knowledge transfer learning. Unfortunately, due to different categories of imaging stiff challenges annotation, there not large enough uniform remote sensing support pre-training in domain (RSD). Moreover, models datasets learning directly fine-tuning diverse downstream tasks seems be crude method, which easily...

Arbitrary-oriented object detection (AOOD) from optical remote sensing imagery has to correctly generate delicate oriented boundary boxes (OBBs) and meanwhile identify their specific categories. However, how make detectors learn parameters of OBBs, especially for the crucial orientation information, category complex background becomes a challenge task. Therefore, in this article, exploring better way guide detector parametric information novel one-stage anchor-free called Posterior Instance...

Ensuring the security of germplasm resources is great significance for sustainable development agriculture and ecological balance. By combining morphological characteristics maize seeds with hyperspectral data, variety classification has been achieved using machine learning algorithms. Initially, data are obtained from images, followed by selection feature subsets Recursive Feature Elimination (RFE) Select From Model (SFM) methods, indicating that features selected RFE exhibit better...

Objective Since the Guangxi government implemented public county hospital reform in 2009, there have been no studies of hospitals this underdeveloped area China. This study aimed to establish an evaluation indicator system for and generate recommendations development policymaking. Methods A performance was developed based on balanced scorecard theory. Opinions were elicited from 25 experts administrative units, universities Delphi method used modify indicators. The Topsis evaluate five...

Traditional region-of-interest (ROI) detection methods for remote sensing images are generally formulated at pixel level and less efficient when applied on large high-resolution images. This letter presents an accurate approach via superpixel-to-pixel saliency analysis ROI detection. At first, the image is downsampled segmented into superpixels by simple linear iterative clustering. Next, structure tensor background contrast used to yield superpixel feature maps texture color. After fusing...

This paper presents a method for detecting the small infrared target under complex background. An algorithm, named local mutation weighted information entropy (LMWIE), is proposed to suppress Then, grey value of targets enhanced by calculating energy. Image segmentation based on adaptive threshold used solve problems that noise with improvement targets. Experimental results show compared Butterworth high-pass filter method, algorithm more effective and faster detection.

With the development of remote-sensing technology, optical imagery processing has played an important role in many application fields, such as geological exploration and natural disaster prevention. However, relative radiation correction geometric are key steps preprocessing because raw image data without will cause poor performance during application. Traditionally, downlinked to ground station, preprocessed, distributed users. This process generates long delays, which is a major bottleneck...

Land cover classification is a popular research field in remote sensing applications, which have to both consider the pixel-level and boundary mapping comprehensively. Although multi-scale features deep learning (DL) network powerful ability, how use feature description produce an accurate land from very high resolution (VHR) optical image still challenging task because of large intraclass or small interclass difference covers. Therefore, aiming at achieving more classification, we proposed...

With the development of satellite load technology and very large scale integrated (VLSI) circuit technology, onboard real-time synthetic aperture radar (SAR) imaging systems have become a solution for allowing rapid response to disasters. A key goal SAR system design is achieve high processing performance with severe size, weight, power consumption constraints. In this paper, we analyse computational burden commonly used chirp scaling (CS) algorithm. To reduce hardware cost, propose partial...