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Remote sensing images change detection is the key technology for monitoring forest windfall damages. Genetic algorithm (GA) is a branch of intelligent optimization techniques available to contribute to the surveys of windstorm and wildfire detection in forest areas. However, traditional genetic algorithms remain challenging due to several issues, such as complex calculation, poor noise immunity, and slow convergence. Analysis at the spatial level allows classifications to utilize contextual, and hierarchical information of image objects in addition to solely using spectral information. Additionally, ensemble learning presents a possibility for improving classification accuracy. Ensemble classifiers combined with spatial-based GA offers a promising method (E-nGA) for automating the process of monitoring forest loss. The research in this paper is presented in four parts. First, block-matching and 3D filtering is performed to suppress noises while enhancing valuable information. The difference image is then generated using the image difference method. Afterward, context-based saliency detection and Fuzzy c-means algorithm are conducted on the difference image to reduce the search space. Finally, the proposed E-nGA is executed to further classify the pixels and produce the final change map. Our first proposition is to design improved genetic operators in the GA, relying not only on pixel values but also on spatial information. Our second proposition is to consider an ensemble classification model based on multiple vegetation features for decision integration. Six frequently-used classification methods, as well as the simple genetic algorithm, are executed to demonstrate the effectiveness of the proposed framework in improving the robustness and detection accuracy.

Nowadays, a large amount of information is stored as text, and numerous text mining techniques have been developed for various applications, such as event detection, news topic classification, public opinion detection, and sentiment analysis. Although significant progress has been achieved for short text classification, document-level text classification requires further exploration. Long documents always contain irrelevant noisy information that shelters the prominence of indicative features, limiting the interpretability of classification results. To alleviate this problem, a model called MIPELD (mining the frequent pattern of a named entity for long document classification) for long document classification is demonstrated, which mines the frequent patterns of named entities as features. Discovered patterns allow semantic generalization among documents and provide clues for verifying the results. Experiments on several datasets resulted in good accuracy and marco-F1 values, meeting the requirements for practical application. Further analysis validated the effectiveness of MIPELD in mining interpretable information in text classification.