The electroencephalogram (EEG) has great attraction in emotion recognition studies due to its resistance to deceptive actions of humans. This is one of the most significant advantages of brain signals in comparison to visual or speech signals in the emotion recognition context. A major challenge in EEG-based emotion recognition is that EEG recordings exhibit varying distributions for different people as well as for the same person at different time instances. This nonstationary nature of EEG limits the accuracy of it when subject independency is the priority. The aim of this study is to increase the subject-independent recognition accuracy by exploiting pretrained state-of-the-art Convolutional Neural Network (CNN) architectures. Unlike similar studies that extract spectral band power features from the EEG readings, raw EEG data is used in our study after applying windowing, pre-adjustments and normalization. Removing manual feature extraction from the training system overcomes the risk of eliminating hidden features in the raw data and helps leverage the deep neural network’s power in uncovering unknown features. To improve the classification accuracy further, a median filter is used to eliminate the false detections along a prediction interval of emotions. This method yields a mean cross-subject accuracy of 86.56% and 78.34% on the Shanghai Jiao Tong University Emotion EEG Dataset (SEED) for two and three emotion classes, respectively. It also yields a mean cross-subject accuracy of 72.81% on the Database for Emotion Analysis using Physiological Signals (DEAP) and 81.8% on the Loughborough University Multimodal Emotion Dataset (LUMED) for two emotion classes. Furthermore, the recognition model that has been trained using the SEED dataset was tested with the DEAP dataset, which yields a mean prediction accuracy of 58.1% across all subjects and emotion classes. Results show that in terms of classification accuracy, the proposed approach is superior to, or on par with, the reference subject-independent EEG emotion recognition studies identified in literature and has limited complexity due to the elimination of the need for feature extraction.

Vegetation index algorithms based on radiance and/or reflectance data in the Visible Near Infrared (VNIR) band are created for multispectral and hyperspectral images to detect vegetation. By this way, vegetation and soil regions can be separated from each other. Furthermore, vegetation maturity and health can be observed and tracked. Besides VNIR band cameras, there are many cameras working in the Short Wavelength Infrared (SWIR) band. Therefore, the plant areas should also be detected in SWIR band images. For the purpose of the vegetation detection, a new vegetation index algorithm is created for SWIR hyperspectral images in this research. The proposed method’s feasibility and precision is evaluated using SWIR band hyperspectral data collected at various locations and times during the year. Test results show that besides vegetation regions, soil and water regions in the SWIR band hyperspectral images can be detected successfully. On Hyperion hyperspectral data, 98.9% and 98.5% mean scores are achieved for recall and precision, respectively.

Nitrogen is crucial for plant physiology due to the fact that plants consume a significant amount of nitrogen during the development period. Nitrogen supports the root, leaf, stem, branch, shoot and fruit development of plants. At the same time, it also increases flowering. To monitor the vegetation nitrogen concentration, one of the best indicators developed in the literature is the Normalized Difference Nitrogen Index (NDNI), which is based on the usage of the spectral bands of 1510 and 1680 nm from the Short-Wave Infrared (SWIR) region of the electromagnetic spectrum. However, the majority of remote sensing sensors, like cameras and/or satellites, do not have an SWIR sensor due to high costs. Many vegetation indexes, like NDVI, EVI and MNLI, have also been developed in the VNIR region to monitor the greenness and health of the crops. However, these indexes are not very well correlated to the nitrogen content. Therefore, in this study, a novel method is developed which transforms the estimated VNIR band indexes to NDNI by using a regression method between a group of VNIR indexes and NDNI. Training is employed by using VNIR band indexes as the input and NDNI as the output, both of which are calculated from the same location. After training, an overall correlation of 0.93 was achieved. Therefore, by using only VNIR band sensors, it is possible to estimate the nitrogen content of the plant with high accuracy.

Many dehazing methods have been presented by many researchers either on single hazy images or multiple images like videos. Single image dehazing is a very challenging problem, since there is no ground truth image and/or depth map. Beyond many traditional dehazing methods, with the increasing volume of hazy image datasets, recent literature about dehazing mostly focuses on deep learning methods. Although, deep learning models perform better than traditional methods on the validation data of the specific dataset which they are trained with, the generalization performance of deep learning models is generally poor. Another important bottleneck for deep models is the training cost in terms of time and hardware requirements. In this study, Atmospheric Light Scattering model is improved by taking into account the amount of haze and refining the transmission and air light used in atmospheric light scattering model. According to VIS quality metric results, proposed method is the second best method on Dense-Haze and CHIC-Color datasets by achieving 0.87 and 0.86 scores, respectively, and the best method on our own IMFD dataset by handling 0.91. Results of this study shows that proposed method is superior and/or competitive to the state of the art deep learning models in terms of the visual quality of the dehazed image.