Conventional geographic object-based image analysis (GEOBIA) land cover classification methods by using very high resolution images are hardly applicable due to their complex ground truth and manually selected features, while convolutional neural networks (CNNs) with many hidden layers provide the possibility of extracting deep features from very high resolution images. Compared with pixel-based CNNs, superpixel-based CNN classification, carrying on the idea of GEOBIA, is more efficient. However, superpixel-based CNNs are still problematic in terms of their processing units and accuracies. Firstly, the limitations of salt and pepper errors and low boundary adherence caused by superpixel segmentation still exist; secondly, this method uses the central point of the superpixel as the classification benchmark in identifying the category of the superpixel, which does not allow classification accuracy to be ensured. To solve such problems, this paper proposes a region-based majority voting CNN which combines the idea of GEOBIA and the deep learning technique. Firstly, training data was manually labeled and trained; secondly, images were segmented under multiresolution and the segmented regions were taken as basic processing units; then, point voters were generated within each segmented region and the perceptive fields of points voters were put into the multi-scale CNN to determine their categories. Eventually, the final category of each region was determined in the region majority voting system. The experiments and analyses indicate the following: 1. region-based majority voting CNNs can fully utilize their exclusive nature to extract abstract deep features from images; 2. compared with the pixel-based CNN and superpixel-based CNN, the region-based majority voting CNN is not only efficient but also capable of keeping better segmentation accuracy and boundary fit; 3. to a certain extent, region-based majority voting CNNs reduce the impact of the scale effect upon large objects; and 4. multi-scales containing small scales are more applicable for very high resolution image classification than the single scale.

Change detection (CD) remains an important issue in remote sensing applications, especially for high-resolution images, but it has yet to be fully resolved. In this study, we propose a novel Siamese network model, i.e., the multiple attention Siamese network (MASNet), for high-resolution image change detection (HRCD). The selective kernel convolution (SKConv) is first embedded into the encoders of the Siamese network to improve its feature extraction ability. The attention feature fusion module (AFFM) is then proposed and utilized to reform the network’s decoder, which realizes the fusion and selection of image features extracted from different scales and times. Experiments on three benchmark CD datasets, namely, the season-varying change detection dataset (SCDDS), Wuhan University (WHU) building change detection dataset (WBDS), and Google dataset (GDS), are conducted to validate the proposed model. The results show that the proposed attention module (the AFFM) is superior to multiple classic attention modules, including the squeeze-and-excitation block (SE), spatial SE block (sSE), spatial and channel SE block (scSE), convolutional block attention module (CBAM), and efficient channel attention module (ECA), in terms of the fusing of CD features. Furthermore, MASNet achieves a higher overall accuracy (OA), mean intersection over union (mIoU), F1-score (F1), and kappa coefficient (kappa) than ten state-of-the-art HRCD methods, such as the deeply supervised image fusion network (DSIFN), spatial–temporal attention neural network (STANet), and dual attentive fully convolutional Siamese network (DASNet) while maintaining low missed alarms and false alarms and having a fair method efficiency.

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.

This article presents a remote-sensing image retrieval scheme using image visual, object, and spatial relationship semantic features. It includes two main stages, namely offline multi-feature extraction and online query. In the offline stage, remote-sensing images are decomposed into several blocks using the Quin-tree structure. Image visual features, including textures and colours, are extracted and stored. Further, object-oriented support vector machine (SVM) classification is carried out to obtain the image object semantic. A spatial relationship semantic is then obtained by a new spatial orientation description method. The online query stage, meanwhile, is a coarse-to-fine process that includes two sub-steps, which are a rough image retrieval based on the object semantic and a template-based fine image retrieval involving both visual and semantic features. This method is different from many other semantic-based remote-sensing image retrieval methods and is suitable for ‘scene matching’. Moreover, the scheme is distinctive in system design, spatial relationship semantic description, and method of combining and utilizing visual and semantic features.

The traditional remote sensing image segmentation method uses the same set of parameters for the entire image. However, due to objects’ scale-dependent nature, the optimal segmentation parameters for an overall image may not be suitable for all objects. According to the idea of spatial dependence, the same kind of objects, which have the similar spatial scale, often gather in the same scene and form a scene. Based on this scenario, this paper proposes a stratified object-oriented image analysis method based on remote sensing image scene division. This method firstly uses middle semantic which can reflect an image’s visual complexity to classify the remote sensing image into different scenes, and then within each scene, an improved grid search algorithm is employed to optimize the segmentation result of each scene, so that the optimal scale can be utmostly adopted for each scene. Because the complexity of data is effectively reduced by stratified processing, local scale optimization ensures the overall classification accuracy of the whole image, which is practically meaningful for remote sensing geo-application.