Hyperspectral remote sensing images have high spectral resolution and provide rich information on the types of features, but their high data dimensions and large data volume pose challenges in data processing. In addition, it is difficult to obtain ground truths of hyperspectral images (HSIs). Owing to the small number of training samples, the super-normative classification of HSIs is particularly challenging and time-consuming. As deep learning techniques continue to evolve, an increasing number of models have emerged for HSI classification. In this paper, we propose a classification algorithm for HSIs called the residual generative adversarial network (ResGAN), which automatically extracts spectral and spatial features for HSI classification. When unlabeled HSI data are used to train ResGAN, the generator generates fake HSI samples with a similar distribution to real data, and the discriminator contains high values suitable for training a small number of samples with real labels. The main innovations of this method are twofold. First, the generative adversarial network (GAN) is based on a dense residual network, which fully learns the higher-level features of HSIs. Second, the loss function is modified using the Wasserstein distance with a gradient penalty, and the discriminant model of the network is changed to enhance the training stability. Using image data obtained from airborne visibleinfrared sensors of an imaging spectrometer, the performance of ResGAN was compared with that of two HSI classification methods. The proposed network obtains excellent classification results after only marking a small number of samples. From both subjective and objective viewpoints, ResGAN is an excellent alternative to the standard GAN for HSI classification.
The research focuses on various factors influencing intrinsic and extrinsic motivational levels in international tenuredacademics and contractual teaching staff in Management and Business departments towards the adoption of Management of Technology (MOT) related methodologies. A set of hypotheses were defined to deduce the relationship between teaching and adoption of MOT as a framework. This research implies that job performance of international academics strongly depends on various motivational levels. The study was conducted using the interaction survey method with in-depth personal interviews consisting of open ended questions with 250 international academics (respondents consisting of Japanese and Foreign teaching staff) chosen for the study based in Japan. Hence, policy recommendationsand decision making should be dealt with prudence and pragmatism.
The primary problem that affects the use of efficient grid or cluster computing systems are the discovery and usage of parallel and high performance grid computing applications and related services. The primary objective of this paper is to build on a protocol that minimizes the number of messages exchanged between the various peers by applying new grid based pre-coalition concepts to the peer to peer grid services model A 3p viGrid. Lists of agents are maintained in the form of categorized listings that help in the formation of potential coalitions and negotiations among the agents. By developing a new pre-coalition protocol we are able to simulate the number of messages passed inside the A 3p viGrid system that uses predetermined coalition formations to utilize coalition leaders/agents for communication on behalf of a clan or society of agents or peers.
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