In recent days, the major threat in the world is forest fire that affects the biodiversity, climate change, and so forth. So detection process is more essential to monitor the forest region. To detect the forest fire, the paper proposes a novel detection technique of support vector machine (SVM)-Krill herd that can effectively detect the fire region using different kinds of features. Land surface temperature, fire intensity, water vapor, and top of atmosphere temperature are being extracted as some of the features that can be exposed. These features are preferred to classify the LANDSAT image into two classes using SVM optimized by Krill herd. The Euclidean distance is chosen to find the similarity between the test and trained image and then predict the giving query image containing a fire or not based on its training samples. With the help of the feature extractor parameters, the performances have to be analyzed. When compared with existing fire detection algorithms like active fire detection, SFIDE, convolutional neural network (CNN), hybrid intelligent, and PSO-SVM algorithm, the proposed SVM-Krill herd-based detection method increases the accuracy by 1.562%, 0.675%, 1.290%, 0.876%, and 1.038%. The proposed detection method of SVM-Krill herd achieves 99.21% accuracy and high precision as 98.41%.

中文翻译：

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使用支持向量机对 LANDSAT 图像进行森林火灾检测

最近几天，世界面临的主要威胁是影响生物多样性、气候变化等的森林火灾。因此检测过程对于监测林区更为重要。为了检测森林火灾，本文提出了一种新的支持向量机（SVM）检测技术——磷虾群，可以利用不同种类的特征有效地检测火灾区域。地表温度、火灾强度、水汽和大气顶部温度被提取为一些可以暴露的特征。这些特征更适合使用 Krill herd 优化的 SVM 将 LANDSAT 图像分为两类。选择欧几里德距离来找出测试图像和训练图像之间的相似性，然后根据训练样本预测给定的查询图像是否包含火灾。在特征提取器参数的帮助下，必须分析性能。与现有的火灾检测算法如主动火灾检测、SFIDE、卷积神经网络（CNN）、混合智能和PSO-SVM算法相比，所提出的SVM-Krill herd-based检测方法的准确率分别提高了1.562%、0.675%、 1.290%、0.876% 和 1.038%。所提出的 SVM-Krill 群检测方法达到了 99.21% 的准确率和 98.41% 的高精度。