Due to the unique feature of the three -dimensional convolution neural network, it is used in image classification. For There are some problems such as noise, lack of labeled samples, the tendency to overfitting, a lack of extraction of spectral and spatial features, which has challenged the classification. Among the mentioned problems, the lack of experimental samples is the main problem that has been used to solve the methods in recent years. Among them, convolutional neural network-based algorithms have been proposed as a popular option for hyperspectral image analysis due to their ability to extract useful features and high performance. The traditional CNN-based methods mainly use the 2D-CNN for feature extraction, which makes the interband correlations of HS Is underutilized. The 3D-CNN extracts the joint spectral-spatial information representation, but it depends on a more complex model. To address these issues, the report uses a 3D fast learning block (depthwise separable convolution block and a fast convolution block) followed by a 2D convolutional neural network was introduced to extract spectral-spatial features. Using a hybrid CNN reduces the complexity of the model compared to using 3D-CNN alone and can also perform well against noise and a limited number of training samples. In addition, a series of optimization methods including batch normalization, dropout, exponential decay learning rate, and L2 regularization are adopted to alleviate the problem of overfitting and improve the classification results. To test the performance of this hybrid method, it is performed on the S alinas, University Pavia and Indian Pines datasets, and the results are compared with 2D-CNN and 3D-CNN deep learning models with the same number of layers.

Land use and land cover change (LULCC) is a main driver of global environmental change and has destructive effects on the structure and function of the ecosystem. This study attempts to detect temporal and spatial changes in LULC patterns of the Chalus watershed during the last two decades using multitemporal Landsat images and predict the future LULC changes and patterns of the Chalus watershed for the year 2040. A hybrid method between segment-based and pixel-based classification was applied for each Landsat image 2001, 2014 and 2021 to produce LULC maps of the Chalus watershed. In this study, the transition potential maps and the transition probability matrices between LULC types were provided by the Support Vector Machine (SVM) algorithm and the Markov Chain model, respectively, to project the 2021 and 2040 LULC maps. The achieved K-index values that compared the simulated LULC map with the actual LULC map of the year 2021 resulted in a Kstandard = 0.9160, Kno = 0.9379, Klocation = 0.9318 and KlocationStrata = 0.9320, showing a good agreement between the actual and simulated LULC map. Analysis of the historical LULC changes depicted that during 2001-2021, the significant increase of Agricultural land (14317 ha) and Barren area (9063 ha), and the sharp decline of Grassland (26215 ha) and Forest cover (5989 ha) were the major LULC changes in the Chalus watershed. The model predicted that Forest cover will continue to decrease from 29.46% (50720.2667 ha) in 2021 to 25.67% of area (44207.78694 ha) in 2040, as well as, unceasing expansion of Barren area, Agricultural land and Built-up area will be expected by 2040. Therefore, understanding the spatiotemporal dynamics of LULC change is extremely important to implement essential measures and minimize the destructive consequences of these changes.

The development of remote sensing images in recent years has made it possible to identify materials in inaccessible environments and study natural materials on a large scale. But hyperspectral images (HSIs) are a rich source of information with their unique features in various applications. However, several problems reduce the accuracy of HSI classification; for example, the extracted features are not effective, noise, the correlation of bands, and most importantly, the limited labeled samples. To improve accuracy in the case of limited training samples, we propose a multiscale dual-branch residual spectral-spatial network with attention to the HSI classification model named MDBRSSN in this article. First, due to the correlation and redundancy between HSI bands, a principal component analysis operation is applied to preprocess the raw HSI data. Then, in MDBRSSN, a dual-branch structure is designed to extract the useful spectral-spatial features of HSI. The advanced feature, multiscale abstract information extracted by the convolution neural network, is applied to image processing, which can improve complex hyperspectral data classification accuracy. In addition, the attention mechanisms applied separately to each branch enable MDBRSSN to optimize and refine the extracted feature maps. Such an MDBRSSN framework can learn and fuse deeper hierarchical spectral-spatial features with fewer training samples. The purpose of designing the MDBRSSN model is to have high classification accuracy compared to state-of-the-art methods when the training samples are limited, which is proved by the results of the experiments in this article on four datasets. In Salinas, Pavia University, Indian Pines, and Houston 2013, the proposed model obtained 99.64%, 98.93%, 98.17%, and 96.57% overall accuracy using only 1%, 1%, 5%, and 5% of labeled data for training, respectively, which are much better compared to the state-of-the-art methods.

An ultra wideband (UWB) communications system that applies time reversal to transmit the desired signal is investigated. Exact expressions for the first-and second-order moments, cross-correlation, intersymbol interference metric, and correlation coefficient of time reversal (TR) UWB equivalent channel are derived in terms of the physical channel parameters such as delay spread and mean excess delay. These expressions are verified by simulated and experimental results. It is shown that TR-UWB excess delay is very smaller than UWB and its delay spread decreases as signaling bandwidth increases. Semi-analytical results show that the time reversal UWB delay spread is approximately the same as UWB. Furthermore, an ISI metric is derived for TR-UWB channel based on transmitted signal and UWB channel parameters. Moreover, correlation coefficient of two TR-UWB received signals with different power delay profile is computed analytically. Simulation and analytical results show that for t >0.3T w correlation coefficient is below 0.25 and for t >T w correlation coefficient is zero, where T w is the transmitted pulse width. Finally, theoretical performance of a receiver with one tap matched filter is computed and compared with measured and simulated result.

With the one-bit time reversal ultra wideband (OTR-UWB) transceiver, the data symbols are encoded using the reversed order of the channel phase. The major factor limiting SISO OTR capacity and performance is intersymbol interference (ISI). As the data rate goes up, the ISI becomes more severe and it degrades system performance and capacity. In this article, a new single input multiple output (SIMO) system is proposed for an OTR-UWB system. The proposed transceiver structure is based on spatial focusing property of OTR. The performance of the proposed SIMO OTR-UWB system is analyzed in terms of the signal-to-interference-plus-noise-ratio (SINR). It is shown that using a SIMO OTR transceiver, ISI is reduced and the system capacity is increased almost linearly with the number of received antenna. Transmitted signal power at SIMO OTR decreases therefore in low data rates, SISO performance is better than SIMO, but in high rate scenario, SIMO OTR suppresses ISI better than SISO OTR and its performance is better. It is possible to compensate the reduced power using a receiver with more sensitivity, but to compensate the ISI effects, an MMSE receiver or equalizer techniques should be used, since the computational complexity of MMSE receiver grows exponentially with channel length and equalizers reduce the efficient bit rate. It is shown that the proposed SIMO-OTR capacity linearly increases with the number of antennas and decreases logarithmic (almost linearly).