Granular-ball Optimization Algorithm

The existing intelligent optimization algorithms are designed based on the finest granularity, i.e., a point. This leads to weak global search ability and inefficiency. To address this problem, we proposed a novel multi-granularity optimization algorithm, namely granular-ball optimization algorithm (GBO), by introducing granular-ball computing. GBO uses many granular-balls to cover the solution space. Quite a lot of small and fine-grained granular-balls are used to depict the important parts, and a little number of large and coarse-grained granular-balls are used to depict the inessential parts. Fine multi-granularity data description ability results in a higher global search capability and faster convergence speed. In comparison with the most popular and state-of-the-art algorithms, the experiments on twenty benchmark functions demonstrate its better performance. The faster speed, higher approximation ability of optimal solution, no hyper-parameters, and simpler design of GBO make it an all-around replacement of most of the existing popular intelligent optimization algorithms.

From W-Net to CDGAN: Bi-temporal Change Detection via Deep Learning Techniques

Traditional change detection methods usually follow the image differencing, change feature extraction and classification framework, and their performance is limited by such simple image domain differencing and also the hand-crafted features. Recently, the success of deep convolutional neural networks (CNNs) has widely spread across the whole field of computer vision for their powerful representation abilities. In this paper, we therefore address the remote sensing image change detection problem with deep learning techniques. We firstly propose an end-to-end dual-branch architecture, termed as the W-Net, with each branch taking as input one of the two bi-temporal images as in the traditional change detection models. In this way, CNN features with more powerful representative abilities can be obtained to boost the final detection performance. Also, W-Net performs differencing in the feature domain rather than in the traditional image domain, which greatly alleviates loss of useful information for determining the changes. Furthermore, by reformulating change detection as an image translation problem, we apply the recently popular Generative Adversarial Network (GAN) in which our W-Net serves as the Generator, leading to a new GAN architecture for change detection which we call CDGAN. To train our networks and also facilitate future research, we construct a large scale dataset by collecting images from Google Earth and provide carefully manually annotated ground truths. Experiments show that our proposed methods can provide fine-grained change detection results superior to the existing state-of-the-art baselines.