Precise skin lesion classification is still challenging due to two problems, i.e., (1) inter-class similarity and intra-class variation of skin lesion images, and (2) the weak generalization ability of single Deep Convolutional Neural Network trained with limited data. Therefore, we propose a Global-Part Convolutional Neural Network (GP-CNN) model, which treats the fine-grained local information and global context information with equal importance. The Global-Part model consists of a Global Convolutional Neural Network (G-CNN) and a Part Convolutional Neural Network (P-CNN). Specifically, the G-CNN is trained with downscaled dermoscopy images, and is used to extract the global-scale information of dermoscopy images and produce the Classification Activation Map (CAM). While the P-CNN is trained with the CAM guided cropped image patches and is used to capture local-scale information of skin lesion regions. Additionally, we present a data-transformed ensemble learning strategy, which can further boost the classification performance by integrating the different discriminant information from GP-CNNs that are trained with original images, color constancy transformed images, and feature saliency transformed images, respectively. The proposed method is evaluated on the ISIC 2016 and ISIC 2017 Skin Lesion Challenge (SLC) classification datasets. Experimental results indicate that the proposed method can achieve the state-of-the-art skin lesion classification performance (i.e., an AP value of 0.718 on the ISIC 2016 SLC dataset and an Average Auc value of 0.926 on the ISIC 2017 SLC dataset) without any external data, compared with other current methods which need to use external data.

Change detection is an important method of analyzing information about changes in geographical features. However, existing deep learning feature difference methods often lead to the loss of detailed information. Differences in features can arise from factors like illumination or geometric variations rather than actual change regions, resulting in inaccurate change detection. This leads to poor detection of fine-grained boundaries and internal hole problems. To alleviate this, we propose a novel change detection network guided by change boundary awareness and incorporating the concept of boundary-to-center. Our network introduces a change boundary-aware module (CBM) to capture boundary information of change regions. This module enhances boundaries, reducing the influence of noise in feature differences and providing rich contextual information to improve the accuracy of change boundaries. Additionally, we propose a bi-temporal feature aggregation module (BFAM) based on spatial-temporal features. The BFAM aggregates multiple receptive fields features and complements texture information. Both modules utilize the SimAM attention mechanism to enhance the fine-grained nature of the features. In addition, we introduce a deep feature extraction module (DFEM) to extract deep features and minimize information loss during the decoupling process. The proposed change detection network in this paper is guided by change boundary perception, progressively integrating semantic and spatial texture information to refine edges and enhance internal integrity. The performance and efficiency of B2CNet have been validated on four publicly available remote sensing image change detection datasets. Through extensive experiments, the effectiveness of the proposed method has been demonstrated. For example, in terms of IOU for LEVIR, WHU, SYSU and HRCUS datasets, the improvements compared to the baseline are 1.89%, 2.86%, 4.70% and 3.79%, respectively. The code of the proposed approach can be found at https://github.com/bao11seven/B2CNet.

With the increasing volume of high-resolution satellite images, image compression technology has become a research hotspot in the field of remote sensing image processing; however, the existing remote sensing image compression methods, such as JPEG2000, fail to ensure high-ratio and high-fidelity compression. To address this issue, we use a deep neural network to build a learned image compression model named HL-RSCompNet, which is specifically designed for remote sensing images. This model considers both high-frequency and low-frequency features in remote sensing images. We use discrete wavelet transformation (DWT) to divide the image features into two components: the high-frequency feature component and the low-frequency feature component. In addition, we introduce a frequency domain encoding-decoding module with the goal of bolstering the model’s capacity to represent both high-frequency and low-frequency features effectively. This approach allows the model to preserve more high-frequency information, thereby enhancing the overall compression performance of the learned image compression model. Extensive experimental and validation works are performed on four high-resolution remote sensing image datasets. The results indicate that our method outperforms existing traditional compression methods like JPEG2000 and even surpasses the performance of state-of-the-art learned image compression models. Our project is available at https://github.com/shao15xiang/HL-RSCompNet .

With the advent of very-high-resolution remote sensing images, semantic change detection (SCD) based on deep learning has become a research hotspot in recent years. SCD aims to observe the change in the Earth’s land surface and plays a vital role in monitoring the ecological environment, land use and land cover. Existing research mainly focus on single-task semantic change detection; the problem they face is that existing methods are incapable of identifying which change type has occurred in each multi-temporal image. In addition, few methods use the binary change region to help train a deep SCD-based network. Hence, we propose a dual-task semantic change detection network (GCF-SCD-Net) by using the generative change field (GCF) module to locate and segment the change region; what is more, the proposed network is end-to-end trainable. In the meantime, because of the influence of the imbalance label, we propose a separable loss function to alleviate the over-fitting problem. Extensive experiments are conducted in this work to validate the performance of our method. Finally, our work achieves a 69.9% mIoU and 17.9 Sek on the SECOND dataset. Compared with traditional networks, GCF-SCD-Net achieves the best results and promising performances.

Crafting an edge-based real-time object detector for unmanned aerial vehicle (UAV) aerial images is challenging because of the limited computational resources and the small size of detected objects. Existing lightweight object detectors often prioritize speed over detecting extremely small targets. To better balance this trade-off, this paper proposes an efficient and low-complexity object detector for edge computing platforms deployed on UAVs, termed EUAVDet (Edge-based UAV Object Detector). Specifically, an efficient feature downsampling module and a novel multi-kernel aggregation block are first introduced into the backbone network to retain more feature details and capture richer spatial information. Subsequently, an improved feature pyramid network with a faster ghost module is incorporated into the neck network to fuse multi-scale features with fewer parameters. Experimental evaluations on the VisDrone, SeaDronesSeeV2, and UAVDT datasets demonstrate the effectiveness and plug-and-play capability of our proposed modules. Compared with the state-of-the-art YOLOv8 detector, the proposed EUAVDet achieves better performance in nearly all the metrics, including parameters, FLOPs, mAP, and FPS. The smallest version of EUAVDet (EUAVDet-n) contains only 1.34 M parameters and achieves over 20 fps on the Jetson Nano. Our algorithm strikes a better balance between detection accuracy and inference speed, making it suitable for edge-based UAV applications.