The Earth's surface changes continuously due to several natural and humanmade factors. Efficient change detection (CD) is useful in monitoring and managing different situations. The recent rise in launched hyperspectral platforms provides a diversity of spectrum in addition to the spatial resolution required to meet recent civil applications requirements. Traditional multispectral CD algorithms hardly cope with the complex nature of hyperspectral images and their high dimensionality. To overcome these limitations, a CD deep convolutional neural network (CNN) semantic segmentation-based workflow was proposed. The proposed workflow is composed of four main stages, namely preprocessing, training, testing, and evaluation. Initially, preprocessing is performed to overcome hyperspectral image noise and the high dimensionality problem. Random oversampling (ROS), deep learning, and bagging ensemble were incorporated to handle imbalanced dataset. Also, we evaluated the generality and performance of the original UNet model and four variants of UNet, namely residual UNet, residual recurrent UNet, attention UNet, and attention residual recurrent UNet. Three hyperspectral CD datasets were employed in performance assessment for binary and multiclass change cases; all datasets suffer from class imbalance and small region of interest size. Recurrent residual UNet presented the best performance in both accuracy and inference time. Overall, the obtained results imply that deep CNN segmentation models can be utilized to implement efficient CD for hyperspectral imageries. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Recently, deep hashing dominated single label image retrieval approaches. However, the complex nature of remote sensing images, which likely contains multi-labels, hardly benefits from the above approaches. To overcome single-label image retrieval limitations in remote sensing domain, we address this problem by proposing a multi-label remote sensing image retrieval (MLRSIR-NET) framework. Specifically, the proposed MLRSIR-NET composed of two main sub-networks: multi-level feature extraction and deep hash. The multi-level feature extraction network predicts multi-level features to exploit different levels of Convolution Neural Network (CNN (characteristics. To suppress discriminative feature representation, the multi-level features are aggregated and feed to Convolutional Block Attention Module (CBAM) to amplify the representation of relevant multi-label features. CBAM is flexibly integrated into our network with end-to-end training. The hash network stacked two fully connected layers aimed to learn multiple hashing functions to encode the feature vector into a compact hash code. Finally, we conduct experiments on two benchmarks for multi-label images: Dense Labelling Remote Sensing Dataset (DLRSD) and Wuhan Dense Labeling Dataset (WHDLD) to systematically assess the performance. The results show that the proposed framework improved the accuracy in terms of Mean Average Precision (MAP) by a considerable margin of 85.4%, 87.2%, 90.8% and 92.9% for 12-bit, 24-bit, 32-bit and 48-bit code lengths respectively on DLRSD. For WHDLD, it can be noted that the proposed framework supers the DCH by 93.8%, 98.7%, 91.9%, and 94.6% on average respectively.
In modern science there is a rapid development of artificial intelligence, image processing has gradually fascinated and inspired the attention of many researchers in the field of artificial intelligence and has become an interesting and demanding task. The main idea of Image caption is to automatically generate natural language descriptions according to the information observed in an image, this is an important portion of scene understanding, which combines all the knowledge and information available of computer vision and natural language processing. The use of image caption is broad and noteworthy, for example, the understanding of human-computer collaboration. This paper reviews the related methods and focuses on the attention mechanism, which plays a vital role in computer vision and is broadly used in image caption generation tasks. Furthermore, the advantages and the shortcomings of these methods are discussed, providing the commonly used datasets and evaluation criteria in this field. Finally, this paper proposes some open challenges in the image caption task.
The agriculture sector in Egypt faces several problems, such as climate change, water storage, and yield variability. The comprehensive capabilities of Big Data (BD) can help in tackling the uncertainty of food supply occurs due to several factors such as soil erosion, water pollution, climate change, socio-cultural growth, governmental regulations, and market fluctuations. Crop identification and monitoring plays a vital role in precision agriculture. Although several machine learning models have been utilized in identifying crops, the performance of ensemble learning has not been investigated extensively. The massive volume of satellite imageries has been established as a big data problem forcing to deploy the proposed solution using big data technologies to manage, store, analyze, and visualize satellite data. In this paper, we have developed weighted voting mechanism for improving crop classification performance in a large scale, based on ensemble learning and big data schema. Based on Apache Spark, the popular DB Framework, the proposed approach was tested on El Salheya, Ismaili governate. Our findings confirm the generalization of the proposed crop identification approach at a large-scale setting.
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