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Mangroves, vital salt-tolerant coastal forests, confer numerous societal benefits. However, mapping and monitoring these dense, coastal ecosystems is challenging due to limited field access. Leveraging recent hyperspectral remote sensing advancements, this study addresses this challenge. Hyperspectral imaging's narrow contiguous bands are potent for characterizing vegetation structure. The research centers on evaluating hyperspectral remote sensing potential, identifying suitable vegetation indices (VI) for the study area, and employing a multistage Decision Tree (DT) by stacking Vis at each stage to extract distinctive mangrove features. Mangrove attributes like unique leaf greenness, high canopy moisture, coastal-induced temperature moderation, and distinct shortwave infrared absorption contrast them from terrestrial vegetation. Various spectral Vegetation Indices (VI) capture these features by utilizing hyperspectral image bands across wavelengths. The proposed study employs Decision Tree (DT) classifier to optimize feature extraction for mangrove mapping by employing VI-based decision functions to discriminate mangroves from non-mangrove classes. Focused on the Marine National Park (MNP) in Jamnagar, Gulf of Kutch, the study exploits AVIRIS-NG hyperspectral data. It employs the Atmospherically Resistant Vegetation Index (ARVI) for vegetation separation, the Normalized Difference Infrared Index (NDII) for elevated canopy moisture analysis, and Shortwave Infrared Absorption Depth (SIAD) to assess unique shortwave infrared absorption in mangroves. Moreover, it integrates the Mangrove Vegetation Index (MVI) and Normalized Difference Mangrove Index (NDMI) for mangrove recognition. Stacking these indices in a DT remarkably boosts classification accuracy, yielding a peak overall accuracy (OA) of 94.71% and a kappa value (k) of 0.86. Published data and reports corroborate these results. This milestone furnishes preliminary insights into mangrove presence, vital for remote sensing-driven mapping and monitoring within challenging field-access environments. Furthermore, these findings pave the way for species-level discrimination via spectral matching and Machine Learning (ML) algorithms. [ABSTRACT FROM AUTHOR]

The article examined the deep learning models and Fastai text classification technique to predict the medical speech utterances, transcriptions, and intent to extract the 25 medicals problems. The experimental work was conducted using a large amount of data which contains 6661.wav files and one.csv file, including 13 distinct categorization fields of medical speech utterances. Each illness's exploratory data analysis demonstrated the phrase length classes and disease categorization based on the recorded speech sound of patients for each disease. The preprocessing of the task included the wordcloud consisting of all the vocabulary words having different sizes based on the number of speech utterances in each category, eliminating Nan values, verifying for duplicates, and computing the corpus and their term index. Further, features are extracted to determine the number of words in each category, the length of phrases, and the number of words in each phrase, followed by lemmatization and tokenization. Deep learning models such as GRU (Gated Recurrent Unit), LSTM (Long Short Term Memory), bidirectional gated recurrent unit, bidirectional long short-term memory, and Fastai classifier have been used to exact category of disease from the medical speech utterances and their textual phrases. After the assessment, it was discovered that Fastai earned the most incredible precision, recall, accuracy, and lowest loss rate by 96.89%, 95.8%, 93.32%, and 0.169, respectively. In comparison, bidirectional LSTM had achieved the highest F1 score by 95.69% to predict the medical speech utterances for each category. [ABSTRACT FROM AUTHOR]

Mangrove forests are considered to be the most productive ecosystem yet vanishing rapidly over the world. They are mostly found in the intertidal zone and sheltered by the seacoast. Mangroves have potential socio-economic benefits such as protecting the shoreline from storm and soil erosion, flood and flow control, acting as a carbon sink, provides a fertile breeding ground for marine species and fauna. It also acts as a source of income by providing various forest products. Restoration and conservation of mangrove forests remain a big challenge due to the large and inaccessible areas covered by mangroves forests which makes field assessment difficult and time-consuming. Remote sensing along with various digital image classification approaches seem to be promising in providing better and accurate results in mapping and monitoring the mangroves ecosystem. This review paper aims to provide a comprehensive summary of the work undertaken, and addresses various remote sensing techniques applied for mapping and monitoring of the mangrove ecosystem, and summarize their potential and limitation. For that various digital image classification techniques are analyzed and compared based on the type of image used with its spectral resolution, spatial resolution, and other related image features along with the accuracy of the classification to derive specific class information related to mangroves. The digital image classification techniques used for mangrove mapping and monitoring in various studies can be classified into pixel-based, object-based, and knowledge-based classifiers. The various satellite image data analyzed are ranged from light detection and ranging (LiDAR), hyperspectral and multispectral optical imagery, synthetic aperture radar (SAR), and aerial imagery. Supervised state of the art machine learning/deep machine learning algorithms which use both pixel-based and object-based approaches and can be combined with the knowledge-based approach are widely used for classification purpose, due to the recent development and evolution in these techniques. There is a huge future scope to study the performance of these classification techniques in combination with various high spatial and spectral resolution optical imageries, SAR and LiDAR, and also with multi-sensor, multiresolution, and temporal data. [ABSTRACT FROM AUTHOR]

Cloud detection is an essential and important process in satellite remote sensing. Researchers proposed various methods for cloud detection. This paper reviews recent literature (2004–2018) on cloud detection. Literature reported various techniques to detect the cloud using remote-sensing satellite imagery. Researchers explored various forms of Cloud detection like Cloud/No cloud, Snow/Cloud, and Thin Cloud/Thick Cloud using various approaches of machine learning and classical algorithms. Machine learning methods learn from training data and classical algorithm approaches are implemented using a threshold of different image parameters. Threshold-based methods have poor universality as the values change as per the location. Validation on ground-based estimates is not included in many models. The hybrid approach using machine learning, physical parameter retrieval, and ground-based validation is recommended for model improvement. [ABSTRACT FROM AUTHOR]