The classification of very-high-resolution (VHR) remote sensing images is essential in many applications. However, high intraclass and low interclass variations in these kinds of images pose serious challenges. Fully convolutional network (FCN) models, which benefit from a powerful feature learning ability, have shown impressive performance and great potential. Nevertheless, only classification results with coarse resolution can be obtained from the original FCN method. Deep feature fusion is often employed to improve the resolution of outputs. Existing strategies for such fusion are not capable of properly utilizing the low-level features and considering the importance of features at different scales. This paper proposes a novel, end-to-end, fully convolutional network to integrate a multiconnection ResNet model and a class-specific attention model into a unified framework to overcome these problems. The former fuses multilevel deep features without introducing any redundant information from low-level features. The latter can learn the contributions from different features of each geo-object at each scale. Extensive experiments on two open datasets indicate that the proposed method can achieve class-specific scale-adaptive classification results and it outperforms other state-of-the-art methods. The results were submitted to the International Society for Photogrammetry and Remote Sensing (ISPRS) online contest for comparison with more than 50 other methods. The results indicate that the proposed method (ID: SWJ_2) ranks #1 in terms of overall accuracy, even though no additional digital surface model (DSM) data that were offered by ISPRS were used and no postprocessing was applied.

Abstract:Image segmentation is a key prerequisite for object-based classification. However, it is often difficult, or even impossible, to determine a unique optimal segmentation scale due to the fact that various geo-objects, and even an identical geo-object, present at multiple scales in very high resolution (VHR) satellite images. To address this problem, this paper presents a novel unsupervised object-based classification for VHR panchromatic satellite images using multiple segmentations via the latent Dirichlet allocation (LDA) model. Firstly, multiple segmentation maps of the original satellite image are produced by means of a common multiscale segmentation technique. Then, the LDA model is utilized to learn the grayscale histogram distribution for each geo-object and the mixture distribution of geo-objects within each segment. Thirdly, the histogram distribution of each segment is compared with that of each geo-object using the Kullback-Leibler (KL) divergence measure, which is weighted with a constraint specified by the mixture distribution of geo-objects. Each segment is allocated a geo-object category label with the minimum KL divergence. Finally, the final classification map is achieved by integrating the multiple classification results at different scales. Extensive experimental evaluations are designed to compare the performance of our method with those of some state-of-the-art methods for three different types of images. The experimental results over three different types of VHR panchromatic satellite images demonstrate the proposed method is able to achieve scale-adaptive classification results, and improve the ability to differentiate the geo-objects with spectral overlap, such as water and grass, and water and shadow, in terms of both spatial consistency and semantic consistency.

Building change detection (BCD) from remote sensing images is essential in various practical applications. Recently, inspired by the achievement of deep learning in semantic segmentation (SS), methods that treat the BCD problem as a binary SS task using deep siamese networks have attracted increasing attention. However, similar to their counterparts, these approaches still face the challenge of collecting massive pixel-level annotations. To address this issue, this article presents a novel weakly supervised method for BCD from remote sensing images using image-level labels. The proposed method elaborately designs a siamese network to integrate a multiscale joint supervision (MJS) module and an improved consistency regularization (ICR) module into a unified framework to improve the so-called class activation maps (CAMs), which is vital for producing high-quality pseudomasks using imagelevel annotations to support pixel-level BCD. To be specific, the MSJ is used for generating refined multiscale CAMs to well capture changes at different scales corresponding to various buildings of varying sizes. The ICR contributes to improving the consistency of CAMs to highlight the boundaries of changed buildings. Extensive experiments on two public BCD datasets have demonstrated that the proposed method outperforms the current state-of-the-art approaches. Furthermore, the visual detection maps also indicate that the proposed method can achieve scale-adaptive change detection results and preserve object boundaries more effectively.

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