remotesensing technology has become increasingly important in recent years due to its ability to collect high-resolution images of agricultural fields. One of the most popular methods for crop classification in agric...
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ISBN:
(纸本)9798350303513
remotesensing technology has become increasingly important in recent years due to its ability to collect high-resolution images of agricultural fields. One of the most popular methods for crop classification in agricultural fields is object-based image analysis (OBIA). At the same time, the convolutional long short-term memory (ConvLSTM) network has shown great potential in processing spatiotemporal *** this study, we proposed a new model called OB-ConvLSTM (Object-based ConvLSTM) that combines OBIA and ConvLSTM for spatiotemporal crop classification tasks. This model extracts crop spectral information from the spatial dimension of remotesensingimages, extracts crop growth information from multiple remotesensingimages in the temporal dimension, and synthesizes the spatial and temporal dimension information to improve crop classification accuracy. Compared with traditional crop classification models based on single temporal remotesensing, the model proposed in this study is superior to existing models in classification accuracy and model robustness. The proposed OB-ConvLSTM model has been applied to crop classification tasks in major crop-producing regions, achieving over 93% of the crop species recognition accuracy, with mIoU reaching 83%.The main contribution of this study is to design a temporal remotesensingimage semantic segmentation model structure suitable for field crop classification, combining the OBIA method with ConvLSTM and improving the model's performance by optimizing model components such as activation functions and optimizers. Specifically, there are several innovations in the following aspects: First, to facilitate model input, this study uses the SLIC algorithm to segment remotesensingimages into uniformly sized superpixel objects and aligns the superpixel objects in the temporal dimension;Subsequently, we used the ConvLSTM model totrain and classify superpixel objects with temporal information, and adopted the Mish activation functi
LiDAR technology is widely used for point cloud data acquisition in geographic mapping, ecological surveying, etc., which facilitates the research. The PointNet model is a pioneering representative of deep learning te...
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Ultrasonic nondestructive testing has been widely used in industry due to its various advantages. However. with the increasing demand for non-destructive testing accuracy, low-resolution ultrasonic images can easily l...
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Agriculture plays a major role in today's world. 70% of rice production is cultivated in south India. Thailand, the US, India, Vietnam, etc., are exporting rice all over the world. But they are facing one of the m...
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remotesensing technology holds significant advantages in the analysis of aquatic ecological environments, including rapid processing speed, abundant information, extensive spatial coverage, and high reliability. Tota...
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Recently, convolution neural network based methods have dominated the remotesensingimage super-resolution (RSISR). However, most of them own complex network structures and a large number of network parameters, which...
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ISBN:
(数字)9783031189166
ISBN:
(纸本)9783031189159;9783031189166
Recently, convolution neural network based methods have dominated the remotesensingimage super-resolution (RSISR). However, most of them own complex network structures and a large number of network parameters, which is not friendly to computational resources limited scenarios. Besides, scale variations of objects in the remotesensingimage are still challenging for most methods to generate high-quality super-resolution results. To this end, we propose a scale-aware group convolution (SGC) for RSISR. Specifically, each SGC firstly uses group convolutions with different dilation rates for extracting multi-scale features. Then, a scale-aware feature guidance approach and enhancement approach are leveraged to enhance the representation ability of different scale features. Based on SGC, a cascaded scale-aware distillation network (CSDN) is designed, which is composed of multiple SGC based cascade scale-aware distillation blocks (CSDBs). The output of each CSDB will be fused via the backward feature fusion module for final image super-resolution reconstruction. Extensive experiments are performed on the commonly-used UC Merced dataset. Quantitative and qualitative experiment results demonstrate the effectiveness of our method.
images are now employed as data in a variety of applications, including medical imaging, remotesensing, patternrecognition, and video processing. image compression is the process of minimizing the size of images by ...
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The fusion of multi-source data with different spatial and spectral resolutions is a crucial task in many remotesensing and computer vision applications. Model-based fusion methods are more interpretable and. flexibl...
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In recent years, the rapid development of drones has brought tremendous changes to many fields. From refined management in agriculture and forestry to aerial surveying in urban planning, the popularity of UAVs provide...
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image registration is a basic problem in image analysis and imageprocessing. image registration has important applications in aerial image fusion, patternrecognition, three-dimensional reconstruction and other field...
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