Department of CSE, Anand Institute of Higher Technology, Kalasalingam Nagar, Kazhipattur, OMR, Chennai 603103, India.
President Vasundhara Blessing Foundation, Academician GLA University, Chaumuhan, India.
Comput Intell Neurosci. 2022 Nov 22;2022:2668567. doi: 10.1155/2022/2668567. eCollection 2022.
Deep learning is widely used for the classification of images that have various attributes. Image data are used to extract colour, texture, form, and local features. These features are combined in feature-level image fusion to create a merged remote sensing image. A trained depth belief network (DBN) processes and divides fusion images, while a Softmax classifier determines the land type. As tested, the proposed approach can categorise all types of land. Traditional methods of detecting distant sensing photographs have limitations that can be overcome by using convolutional neural networks (CNN). Traditional techniques are incapable of combining deep learning elements while doing badly in classification. After PCA decreases data dimensionality, deep learning is applied to generate effective features that employ deep learning after PCA has reduced the dimensionality of the data. Principal component analysis is commonly used because of its effectiveness in attaining linear dimension reduction. It may be used on its own or as a starting point for further study into various different dimensionality reduction approaches. Data can be altered by remapping onto a new set of orthogonal axes using a process known as projection-based principal component analysis. Following remote sensing of land resources, the pictures were classified using a support vector machine. Euroset satellite images are used to assess the suggested approach. Accuracy and kappa have both increased. It was accurate and within 95.83 % of the planned figures. The classification findings' kappa value and reasoning time were 95.87 % and 128 milliseconds, respectively. Both the model's performance and the classification effect are excellent.
深度学习广泛应用于具有各种属性的图像分类。图像数据用于提取颜色、纹理、形状和局部特征。这些特征在特征级图像融合中组合，创建合并的遥感图像。经过训练的深度置信网络 (DBN) 处理和分割融合图像，而 Softmax 分类器确定土地类型。经过测试，所提出的方法可以对所有类型的土地进行分类。使用卷积神经网络 (CNN) 可以克服传统遥感照片检测方法的局限性。传统技术无法结合深度学习元素，在分类方面表现不佳。在 PCA 降低数据维度后，应用深度学习生成有效特征，在 PCA 降低数据维度后应用深度学习。由于 PCA 在实现线性降维方面的有效性，因此它被广泛使用。它可以单独使用，也可以作为进一步研究各种不同降维方法的起点。通过使用称为基于投影的主成分分析的过程，数据可以通过重新映射到新的一组正交轴上来改变。在对土地资源进行遥感后，使用支持向量机对图像进行分类。使用 Euroset 卫星图像评估所提出的方法。准确性和kappa 都有所提高。它准确且在计划数字的 95.83% 以内。分类结果的 kappa 值和推理时间分别为 95.87%和 128 毫秒。模型的性能和分类效果都非常出色。
