Weakly supervised semantic segmentation methods can effectively alleviate the problem of high cost and difficult access to annotation in traditional methods. Among these approaches, point annotated semantic label not only offers a more affordable option but also provides accurate location and category information, playing an indispensable role in current research. However, point annotation labeling encounters challenges such as missing global and texture information, and limiting segmentation accuracy and efficiency while being susceptible to noise interference. For the above problems, a weakly supervised remote sensing image classification framework based on point annotated semantic label is proposed, which consists of three components: data augmentation, Pixel-Net, and iterative superpixel-based sample expansion (ISSE). First, the data augmentation method is used to generate a sufficient number of training samples. Subsequently, the weakly supervised network Pixel-Net is trained using point annotated semantic labels. Pixel-Net incorporates traditional image processing techniques such as edge detection and blurring into deep learning, enabling effective learning of edge and spectral semantic details while reducing the impact of noise on classification results. Finally, ISSE leverages contextual information from superpixels and pseudo-labels to enrich the valuable information in weakly supervised labels, thereby improving the model’s classification performance. In the experiments, existing semantic segmentation methods and Pixel-Net are evaluated on the Vaihingen and Zurich Summer datasets, and the effectiveness of ISSE is verified. The results show that Pixel-Net achieves the best segmentation accuracy on both datasets, while ISSE can effectively utilize the existing point annotation labels to mitigate the effect of noise and thus improve the accuracy of weakly supervised semantic segmentation.

Extracting buildings from remote sensing images plays an important role in urban development planning, disaster assessment and mapping. Convolutional neural network (CNN) has been widely applied to building extraction because of its powerful deep semantic feature extraction ability. However, existing CNN-based building extraction methods are difficult to accurately extract multiscale buildings with accurate edges because of the limitation of feature receptive fields and the loss of spatial detail information. For the above problems, this paper proposes a multiscale receptive field network (MSRF-Net) to accurately extract multiscale buildings from remote sensing images. MSRF-Net includes multiscale receptive field feature encoder (MRFF-Encoder) and multipath decoder. In the MRFF-Encoder, a multiscale attentional down (MSAD) module and asymmetric residual inception (ARI) module are proposed to capture multiscale receptive field features. In the multipath decoder, convolutions with different kernel size and dilation are used in three parallel paths to learn localization-preserved multiscale features with multiscale receptive field. What’s more, the features of different branches and MRFF-Encoder are fused by the proposed feature combination module, which contribute to capture context information of multiscale receptive field while recovering the resolution of feature space. The experimental results show that compared with the latest MAP-Net, MSRF-Net has achieved F1 score growth of 1.14%, 0.42%, 1.11% and IoU score growth of 1.68%, 0.76% and 1.64% respectively on Massachusetts data set, WHU data set and the Typical Cities Building data set.

This paper proposes an effective pore representation descriptor based on Convolutional Neural Networks (CNNs). We make full use of the diversity and large quantities of sweat pores in fingerprints to learn a deep feature, denoted as DeepPoreID. The DeepPoreID is then used to describe the local feature for each pore and finally integrated into the classical direct pore matching method. Experiments carried on the challenge public high-resolution fingerprint database with small image size of 320 × 240 shows the effectiveness of the proposed DeepPoreID. The results also have shown that the proposed method outperforms other existing state-of-the-art methods in the aspect of recognition accuracy. About ~35% rise in accuracy can be obtained when compared with the best result achieved by existing methods.