Change detection by comparing two bitemporal images is one of the most challenging tasks in remote sensing. At present, most related studies focus on change area detection while neglecting multiple change type identification. In this letter, an attention gates generative adversarial adaptation network (AG-GAAN) is proposed on multiple change detection. The AG-GAAN has the following contributions: 1) this method can automatically detect multiple changes; 2) it includes attention gates mechanism for spatial constraint and accelerates change area identification with finer contours; and 3) the domain similarity loss is introduced to improve the discriminability of the model so that the model can map out real changes more accurately. To demonstrate the robustness of this approach, we used the Google Earth data sets that include seasonal variations for change detection and understanding. The experimental results demonstrated that the proposed method can accurately detect the multiple change types from bitemporal imagery.

Hyperspectral image analysis plays an important role in agriculture, mineral industry, and for military purposes. However, it is quite challenging when classifying high-dimensional hyperspectral data with few labeled samples. Currently, generative adversarial networks (GANs) have been widely used for sample generation, but it is difficult to acquire high-quality samples with unwanted noises and uncontrolled divergences. To generate high-quality hyperspectral samples, a self-attention generative adversarial adaptation network (SaGAAN) is proposed in this work. It aims to increase the number and quality of training samples to avoid the impact of over-fitting. Compared to the traditional GANs, the proposed method has two contributions: (1) it includes a domain adaptation term to constrain generated samples to be more realistic to the original ones; and (2) it uses the self-attention mechanism to capture the long-range dependencies across the spectral bands and further improve the quality of generated samples. To demonstrate the effectiveness of the proposed SaGAAN, we tested it on two well-known hyperspectral datasets: Pavia University and Indian Pines. The experiment results illustrate that the proposed method can greatly improve the classification accuracy, even with a small number of initial labeled samples.

Forests play a vital role in combating gradual developmental deficiencies and balancing regional ecosystems, yet they are constantly disturbed by man-made or natural events. Therefore, developing a timely and accurate forest disturbance detection strategy is urgently needed. The accuracy of traditional detection algorithms depends on the selection of thresholds or the formulation of complete rules, which inevitably reduces the accuracy and automation level of detection. In this paper, we propose a new multitemporal convolutional network framework (MT-CNN). It is an integrated method that can realize long-term, large-scale forest interference detection and distinguish the types (forest fire and harvest/deforestation) of disturbances without human intervention. Firstly, it uses the sliding window technique to calculate an adaptive threshold to identify potential interference points, and then a multitemporal CNN network is designed to render the disturbance types with various disturbance duration periods. To illustrate the detection accuracy of MT-CNN, we conducted experiments in a large-scale forest area (about 990 ${\mathrm{km}}^2$) on the west coast of the United States (including northwest California and west Oregon) with long time-series Landsat data from 1986 to 2020. Based on the manually annotated labels, the evaluation results show that the overall accuracies of disturbance point detection and disturbance type recognition reach $90\%$. Also, this method is able to detect multiple disturbances that continuously occurred in the same pixel. Moreover, we found that forest disturbances that caused forest fire repeatedly appear without a significant coupling effect with annual temporal and precipitation variations. Potentially, our method is able to provide large-scale forest disturbance mapping with detailed disturbance information to support forest inventory management and sustainable development.

Time-series image change detection is one of the most challenging tasks to remote sensing society. Due to complex phenological patterns of cropland, it is difficult to design an efficient strategy for cropland change detection. In this work, an integrated framework is proposed to perform change detection with a limited number of training samples. There are two improvements in this proposed cropland change detection method: 1) the harmonic function is utilized to fill the missing data within a time-series image stack by considering phenological patterns of cropland and 2) the CropGAN was developed to generate realistic samples for training data set enrichment. Compared to the traditional change detection methods, the proposed strategy able to detect different kinds of cropland changes even with few number of samples. Experiments on a Landsat time-series image stack demonstrated that the proposed CropGAN can significantly improve change detection accuracies, given a limited number of labeled samples.