Object-based image analysis (OBIA) is regarded as an effective technology for high-spatial resolution (HSR) image classification due to its clear and intuitive technical process. However, OBIA depends on the manual tuning of image classification features, which is a tricky job. Deep learning (DL) technology auto-learns image features from massive images and obtains higher image classification accuracy than traditional techniques. In this study, a novel method called object-scale adaptive convolutional neural network (OSA-CNN), which combines OBIA with CNN, is proposed for HSR image classification. First, OSA-CNN collects the image patches along the main axes of the object primitives obtained through image segmentation; the size of the former is automatically determined through the axis widths of the latter. This step generates the input units required for CNN classification. Second, the Squeeze-and-Excitation block is extracted from the SE network into the network structure of GoogleNet, which realizes the weighted fusion of the multiscale convolutional features, enhances useful features, and suppresses useless ones. In the classification stage, multiscale image segmentation and CNN classification are fused using an object-scale adaptive mechanism. Finally, object primitives are classified through majority voting on the image patches. The network structure modifications, multiscale classification fusion, and other improvements are verified by gradually incorporating these steps into the original GoogleNet. The experiments show that these improvements effectively enhance the image classification accuracy. This study presents an effective way of combining OBIA and DL techniques to utilize the advantages of both approaches and facilitate HSR image classification.

Floods can cause huge damage to society, the economy, and the environment. As a result, it is vital to determine the extent and type of land cover in flooded areas quickly and accurately in order to facilitate disaster relief and mitigation efforts. Synthetic aperture radar (SAR) is an all-weather, 24 h data source used to extract information about flood inundations, and its primary aim is to extract water body information for flood monitoring. In this study, we have studied the backscattering characteristics of water and non-water, combined the threshold segmentation method with Markov random fields (MRF), and embedded simulated annealing (SA) in the process of image noise reduction, resulting in the development of a water extraction method KI-MRF-SA with high accuracy in classification and high automation. Furthermore, object-scale adaptive convolutional neural networks (OSA-CNN) are introduced for the classification of optical images before the flood in order to provide reference data for flood inundation analysis. The method proposed in this study consists of the following three steps: (1) The Kittler and Illingworth (KI) thresholding algorithm is used for the segmentation of SAR images in order to determine the initial flood inundation extent; (2) MRF and SA algorithms are employed as a means to optimize the initial flood inundation extent, and the results are combined across multiple polarizations by using an intersection operation to determine the final flood inundation extent; and (3) As part of the flood mapping process, land cover types before the flood are classified using OSA-CNN and combined with flood inundation extents. According to the experimental results, it is evident that the proposed KI-MRF-SA method is capable of distinguishing water from non-water with significantly higher accuracy (3–5% improvement in the overall accuracy) than conventional thresholding methods. Combined with the classification method of OSA-CNN proposed in our earlier research, the overall classification accuracy of flood-affected areas could reach 92.7%.