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Crop classification is a significant requirement to estimate crop area, structure, and spatial distribution, as well as provide important input parameters for crop yield models. Different techniques were considered in this system and providing betterment in automation. But none of them gave promising results. So here, a Convolutional Deep Neural Network (CDNN) is proposed to identify the crop areas with the help of Synthetic-Aperture Radar (SAR) satellite images as well as the cultivation status of the crop. First, in training phase, the segmented image of the crop is preprocessed using HLS, then feature is extracted using BRIEF, then, they are classified using CDNN. Then after in testing phase, the input SAR image from the database is further processed using MRG algorithm and classified centered on the training results. After classification, the cultivation status of each classified crop can be identified by taking the Euclidean distance (ED) betwixt the standard parameters and resultant parameters of a specific crop. After computing ED, the ED is contrasted with the threshold value and the cultivation status of a particular crop can be identified. The results are analyzed to ascertain the performance shown by the proposed technique with other existent techniques.

This paper proposes a multi-oriented text localization method in natural images suitable for real-time processing of high-definition video on portable and mobile devices. Our method is based on the connected components (CC) approach: first, CC are isolated by convolving a multi-scale pyramid with a specifically designed linear spatial filter followed by hysteresis thresholding. Next, non-textual CC are pruned employing a local classifier consisting of a cascade of multilayer perceptron (MLP) fed with increasingly extended feature vectors. The stroke width feature is estimated in linear time complexity by computing the maximal inscribed squares in the CC. Candidate CC and their neighbors are then checked using a more context aware neural network classifier that takes into account the target CC and their vicinity. Finally, text sequences are extracted in all pyramid levels and fused using dynamic programming. The main contribution of the work presented here is execution speed: the CPU-only parallel implementation of the proposed method is capable of processing 1080p HD video at nearly 30 frames per second on a standard laptop. Furthermore, when benchmarked on the ICDAR 2013 Robust Reading and on the ICDAR 2015 Incidental Scene Text data sets, our system performs more than twice faster than the state-of-the-art, while still delivering competitive results in terms of precision and recall.

Recently, analytical approaches based on the eigenfunctions of spatial configuration matrices have been proposed in order to consider explicitly spatial predictors. The present study demonstrates the usefulness of eigenfunctions in spatial modeling applied to ecological problems and shows equivalencies of and differences between the two current implementations of this methodology. The two approaches in this category are the distance-based (DB) eigenvector maps proposed by P. Legendre and his colleagues, and spatial filtering based upon geographic connectivity matrices (i.e., topology-based; CB) developed by D. A. Griffith and his colleagues. In both cases, the goal is to create spatial predictors that can be easily incorporated into conventional regression models. One important advantage of these two approaches over any other spatial approach is that they provide a flexible tool that allows the full range of general and generalized linear modeling theory to be applied to ecological and geographical problems in the presence of nonzero spatial autocorrelation.

High spatial resolution multispectral (HMS) images can provide sufficient information for researchers to analyze the potential disasters in the living environment. However, an original multispectral (MS) image is with low-space-resolution and high-spectrum-resolution, while an original panchromatic (PAN) image has the opposite property. Pansharpening aims at obtaining HMS image by retaining spectrum of the MS image and injecting details of the PAN image simultaneously. In this paper, we present a new pansharpening method. First, we use a bilateral filter (BF) to obtain the low-frequency-component (LFC) of PAN and MS images, respectively. Then the high-frequency-component (HFC) of PAN and MS images are readily obtained. Second, an adaptive intensity-hue-saturation (AIHS) based method is applied to generate the HFC of intensity. Finally, a dual-scale guided image filter (GIF) is utilized to calculate the difference between HFC of intensity and PAN to get the detail images. And then, these detail images are injected into the original MS image to achieve the HMS image. The proposed method is applied into testing various satellite data sets, and performs better effect on both visual quality and objective indictors than the existing methods.

The low-resolution aware linear minimum mean squared error (LRA-LMMSE) channel estimator, designed for low-resolution MIMO receivers, achieves a notable reduction in mean squared error by incorporating a comparator network. This network comprises multiple simple comparators that generate binary outputs. In this study, we propose the Kalman filter-based channel estimator with comparator networks (KFB-CN) for temporally and spatially correlated channels in MIMO systems utilizing 1-bit analog-to-digital converters and comparator networks. Following a comprehensive mathematical derivation of the real-valued Kalman filter system and observation models, we demonstrate, via numerical simulations, that the KFB-CN surpasses the performance of the Kalman filter-based estimator without comparator networks. Furthermore, we present a dynamic comparator network selection algorithm that adjusts the utilized comparators in real time to account for variations in channel correlation coefficients. Lastly, we propose a robust detector, i.e., a channel state information mismatch-aware detector, for comparator network-aided systems by integrating the mean squared error estimated from the Kalman filter channel estimator. Numerical simulations highlight a tenfold improvement in performance with respect to symbol error rate of a multi-user uplink MIMO system.

Inspired by the problem that the current spatial registration methods are unsuitable for three-dimensional (3-D) sensor on high-dynamic platform, this paper focuses on the estimation for the registration errors of cooperative missiles and motion states of maneuvering target. There are two types of errors being discussed: sensor measurement biases and attitude biases. Firstly, an improved Kalman Filter on Earth-Centered Earth-Fixed (ECEF-KF) coordinate algorithm is proposed to estimate the deviations mentioned above, from which the outcomes are furtherly compensated to the error terms. Secondly, the Pseudo Linear Kalman Filter (PLKF) and the nonlinear scheme the Unscented Kalman Filter (UKF) with modified inputs are employed for target tracking. The convergence of filtering results are monitored by a position-judgement logic, and a low-pass first order filter is selectively introduced before compensation to inhibit the jitter of estimations. In the simulation, the ECEF-KF enhancement is proven to improve the accuracy and robustness of the space alignment, while the conditional-compensation-based PLKF method is demonstrated to be the optimal performance in target tracking.

The bilateral filter is a non-linear edge-preserving filter that can be adopted in a variety of tasks in computer photography. However, the naive bilateral filter is computationally expensive. Existing researches on the acceleration of bilateral filter mostly concentrate on range approximation. Nevertheless, the range kernel has more impact on the bilateral filter than the spatial kernel. Range approximation would have more side effects. In this paper, we propose a novel approximation of the bilateral filter with spatial subsampling, where the affinity matrix is estimated from a subset of it. We show that the main computational burden of our approximation is a large linear system, for which we propose an efficient iterative algorithm to solve. We have carried out both quantitative and qualitative experiments to evaluate our fast bilateral filter. Experimental results suggest that the proposed filter outperforms the state-of-the-art methods in approximation accuracy. The proposed filter is highly efficient; under a moderate sampling rate, i.e., ( 1 / 5 ) × ( 1 / 5 ) , it needs 0.29s to process a color image with 1 megapixel on an Intel i7-9700 CPU.

In recent years, the decoding of motor imagery (MI) from electroencephalography (EEG) signals has become a focus of research for brain-machine interfaces (BMIs) and neurorehabilitation. However, EEG signals present challenges due to their non-stationarity and the substantial presence of noise commonly found in recordings, making it difficult to design highly effective decoding algorithms. These algorithms are vital for controlling devices in neurorehabilitation tasks, as they activate the patient's motor cortex and contribute to their recovery. This study proposes a novel approach for decoding MI during pedalling tasks using EEG signals. A widespread approach is based on feature extraction using Common Spatial Patterns (CSP) followed by a linear discriminant analysis (LDA) as a classifier. The first approach covered in this work aims to investigate the efficacy of a task-discriminative feature extraction method based on CSP filter and LDA classifier. Additionally, the second alternative hypothesis explores the potential of a spectro-spatial Convolutional Neural Network (CNN) to further enhance the performance of the first approach. The proposed CNN architecture combines a preprocessing pipeline based on filter banks in the frequency domain with a convolutional neural network for spectro-temporal and spectro-spatial feature extraction. To evaluate the approaches and their advantages and disadvantages, EEG data has been recorded from several able-bodied users while pedalling in a cycle ergometer in order to train motor imagery decoding models. The results show levels of accuracy up to 80% in some cases. The CNN approach shows greater accuracy despite higher instability.

The North-Western coastal stretch of the Aruvi Aru and Kal Aru estuaries in Sri Lanka's Mannar district faces increasing vulnerability due to coastal erosion, but comprehensive research has been lacking until now. This study aims to analyze shoreline changes from 1988 to 2022 and forecast future changes until 2042. Landsat satellite images and statistical parameters such as LRR and AoR were used to calculate erosion and accretion rates. The maximum erosion rate was −4.7 to −5.3 m/year, while accretion ranged from 9.6 to 10.3 m/year. The Kalman filter model predicted further erosion in Vankalai West (45 m by 2042) and accretion in Pathaikaddumunai (78 m by 2042). Certain areas, including Vankalai, Naruvilikulam, and Kondachchikudah, were identified as high-risk zones prone to erosion. The study suggests implementing this technique for comprehensive coastal management strategies in Sri Lanka. Coastal areas are fragile and dynamic on the earth surface. North-Western coastal stretch of the Aruvi Aru and Kal Aru estuaries in Mannar district is vulnerable. Digital Shoreline Analysis System is one of the advanced techniques used in GIS to measure shoreline changes.