Nowadays, 3-D convolutional neural networks (3-D CNN) have attracted lots of attention in the spectral-spatial classification of hyperspectral imageries (HSI). In this model, the feed-forward processing structure reduces the computational burden of 3-D structural processing. However, this model as a vector-based methodology cannot analyze the full content of the HSI information, and as a result, its features are not quite discriminative. On the other hand, convolutional long short-term memory (CLSTM) can recurrently analyze the 3-D structural data to extract more discriminative and abstract features. However, the computational burden of this model as a sequence-based methodology is extremely high. In the meanwhile, the robust spectral-spatial feature extraction with a reasonable computational burden is of great interest in HSI classification. For this purpose, a two-stage method based on the integration of CNN and CLSTM is proposed. In the first stage, 3-D CNN is applied to extract low-dimensional shallow spectral-spatial features from HSI, where information on the spatial features are less than that of the spectral information; consequently, in the second stage, the CLSTM, for the first time, is applied to recurrently analyze the spatial information while considering the spectral one. The experimental results obtained from three widely used HSI datasets indicate that the application of the recurrent analysis for spatial feature extractions makes the proposed model robust against different spatial sizes of the extracted patches. Moreover, applying the 3-D CNN prior to the CLSTM efficiently reduces the model’s computational burden. The experimental results also indicated that the proposed model led to a 1% to 2% improvement compared to its counterpart models.

In this paper, a multi‐resolution hybrid approach is proposed for the reconstruction of building models from point clouds of lidar data. The detection of the main roof planes is obtained through a polyhedral approach, whereas the models of appended parts, in this case the dormers, are reconstructed by adopting a model‐driven approach. Clustering of the roof points in a multi‐resolution space is based on the fuzzy c‐mean in the polyhedral section of this hybrid approach. A weighted plane algorithm is developed in order to determine the planes of each cluster. The verification of planes between multi‐resolution spaces adopts a method based on a least squares support vector machine that, in the model‐driven section, is applied for detecting types of projecting structures. A method is then developed to determine the dormer models’ parameters. Finally, the detection of boundary roof lines is obtained through a customised fuzzy Hough transform. The paper outlines the concept of the algorithms and the processing chain, and illustrates the results obtained by applying the technique to buildings of different complexities.

ABSTRACT:This paper presents an automatic method to extract road centerline networks from high and very high resolution satellite images. The present paper addresses the automated extraction roads covered with multiple natural and artificial objects such as trees, vehicles and either shadows of buildings or trees. In order to have a precise road extraction, this method implements three stages including: classification of images based on maximum likelihood algorithm to categorize images into interested classes, modification process on classified images by connected component and morphological operators to extract pixels of desired objects by removing undesirable pixels of each class, and finally line extraction based on RANSAC algorithm. In order to evaluate performance of the proposed method, the generated results are compared with ground truth road map as a reference. The evaluation performance of the proposed method using representative test images show completeness values ranging between 77% and 93%. IntroductionAutomatic road and road centerline extraction from highresolution satellite imagery has been an interesting research topic in the field of remote sensing (RS) and geographic information systems (GIS). Extracting precise and up-to-date road network information is a matter of issue when updating spatial databases.The history of researches on road extraction from aerial and satellite images can be addressed on seventies. Road extraction methods based on image processing techniques have a widespread variety from road tracking methods that are based on state of semi-automatic and automatic definition of seed points (Bonnefon et al. 2002) Automatic road extraction methods from aerial and satellite images are clearly influenced by occlusion of road encircling objects such as buildings and trees. Moreover, occlusion caused by shadow of these objects is another factor affecting efficiency of the methods that use radiometric changes in their feature extraction step. In oppose, surrounding objects can sometimes provide supporting contextual information for road extraction (Miao et al. 2014b;Jin and Davis 2005).Miao et al. (2014b) presented a semi-automatic method to extract road centerlines from VHR images. They argued that the surrounding objects, for instance occlusion of trees and shadows are problematic factors to optimize the extraction in high-resolution images. In order to overcome this issue, they used a geodesic method with the help of road seed points which is semi-automatic and manual method. Jin and Davis (2005) presented an integrated system for automatic road mapping from high-resolution multi-spectral satellite imagery using multi-detector information fusion. The integrated system is implemented automatically and the results show a completeness range between 70 % and 86 % with correctness range between 70 % and 92 %. They believe that in highresolution images, although the details such as street marking and cars provide additional context information, they also disrupt extracting the overall road ...

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