Spatial features with spectral properties enhance the quality of satellite image while mapping complex land cover. These features are integrated with the proposed classification approach for improving classification results. The ultimate objective of this investigation is to provide high-level features to the convolutional neural network (CNN) for mapping flooded regions (before and after) using remote sensing data. Here, boundary-based segmentation is done to recognize the dimensions and scales of objects. Modeling a fully trained Convolutional network is feasible for training a huge amount of data in remote sensing studies. Fine-tuned CNN is utilized with slight modification for attaining classified Landsat images. Classification outcomes and confusion matrix are manipulated using B-CNN are compared with classifiers like SVM, random forest (RF) to compute B-CNN efficiency.

具有光谱特性的空间特征可在绘制复杂的土地覆盖图时提高卫星图像的质量。这些功能与建议的分类方法集成在一起，以改善分类结果。这项研究的最终目的是为卷积神经网络（CNN）提供高级功能，以便使用遥感数据来映射淹没区域（之前和之后）。在这里，进行基于边界的分割以识别对象的尺寸和比例。对训练有素的卷积网络进行建模对于在遥感研究中训练大量数据是可行的。经过微调的CNN稍作修改即可获得分类的Landsat图像。使用B-CNN处理分类结果和混淆矩阵，并与SVM等分类器进行比较，