Monitoring the wildfire progression is essential to quantify the fire-disturbance areas for emergency responses. To combine the advantages of pixelwise machine learning (ML) method and region-based deep learning (DL) segmentation model, this study proposes a two-phase hybrid framework for near real-time burned area progression mapping: the first one intends to depict burned area delimitation using a contextual algorithm HRNet to exclude the unburned areas outside the perimeter and minimize omission errors, which partially remain unburned patches within the delimitation as commission errors. The second phase refines the burned area spatially using ensemble fusion based on an updating support vector machine (SVM) model under the voting scheme as new imagery arrives to reduce the commission errors consecutively. The validation results showed that the accuracy of perimeter prediction using the HRNet can reach 96.77% in Kappa. The iterative optimization can improve the average Kappa value from 62.55% to 70.75% for burned area pixel classification using pixelwise SVM alone. The proposed ensemble learning framework can further refine the burned area progression results, reaching an average Kappa up to 85.19%, at four acquisition dates with Sentinel-2 and Landsat-8 available during the Sand fire event that occurred in California.

Satellite remote sensing provides a cost-effective way for monitoring wildfires on a large scale, and the continuous observations and measurements have made remote sensing a primary source of unlabelled big data. Supervised deep learning has shown great success in various remote sensing applications, but it heavily relies on high-quality labels. However, burned area labels are only available for a small part of the world, supervised deep learning from limited labelled data has poor generalization performance across geographical regions and climate zones. Different satellite sensors represent the same physical objects in various ways, while multi-source satellite data often exhibits a combination of common and complementary information, such as optical and radar data. The common information makes it possible to exploit huge amounts of unlabelled multi-source data in model training through consistency regularization between multi-source predictions. In this work, we adopted an unsupervised geospatial domain adaptation (GDA) framework based Dual Stream U-Net model, which combines the supervised loss and unsupervised multi-modal consistency regularization to exploit both labelled and unlabelled multi-model data for model training in a semi-supervised learning manner. The experimental results demonstrate that unsupervised GDA has better generalization performance across geographical regions than fully supervised learning.

Cross-view geo-localization is a critical task in various applications, such as smart city management and disaster monitoring. Current methods typically divide a satellite image into patches and use these patches to identify the geographic location of a query image. However, these methods can only provide the location of an image rather than the location of a specific object of interest. This makes it difficult to link these methods to GeoDatabases to obtain detailed information about a target object, such as its name and construction time. To overcome this limitation, we propose a novel problem of cross-view object geo-localization in a local region with high-resolution satellite images. This problem includes two main challenges: accurately identifying the location of an object and distinguishing the target object from others in satellite images. To address these challenges, we present a new Detection-based Geo-localization method called DetGeo, which consists of an object detection-based framework with a two-branch encoder and a query-aware cross-view fusion module. DetGeo uses cross-view images as input to the detector to provide object-level geo-localization. The fusion module employs cross-view spatial attention to focus on relevant areas of target objects during cross-view feature fusion. To evaluate our method, we constructed a new Cross-View Object Geo-Localization dataset called CVOGL, which comprises ground-view or drone-view images as query images and satellite-view images as geo-tagged reference images. Comprehensive experiments are conducted to demonstrate the effectiveness of our method on CVOGL. https://github.com/sunyuxi/DetGeo.

Climate change by anthropogenic warming leads to increases in dry fuels and promotes forest fires. Multispectral images' quality is easily affected by poor atmospheric conditions. SAR satellite sensors can penetrate through clouds and image day and night. However, the burned area mapping methods widely used for optical data are not feasible to be applied for SAR data owing to the differences in imaging mechanisms. Recent advances in deep image translation can fill this gap by using Generative Adversarial Networks (GAN). In this research, we apply a GAN-based model for SAR to optical image translation over fire-disturbed regions. Specifically, Sentinel-1 SAR images are translated into Sentinel-2 images using the ResNet-based Pix2Pix model, which is trained on 281 large fire events and tested on the other 23 events in Canada. The generated images preserve the spectral characteristics well and show high similarity to the real images with Structure Similarity Index Measure (SSIM) over 0.59.

Wildfires vary across space and time, precisely and timely mapping on the wildfire affected areas is critical for wildfire management, population and property protection, and environmental impact assessment. In this study, we established a large-scale annotated wildfire burned area dataset based on freely available Sentinel-1 SAR and Sentinel-2 multispectral instrument (MSI) data and Canada Wildfire Burned Area Database. This dataset includes bi-temporal Sentinel-1 and Sentinel-2 images, which allows users to exploit remotely sensed data acquired in both optical and microwave domains. On the proposed dataset, we achieved the highest IoU score of 0.86 on the Sentinel-2 data with Siamese U-Net, and the highest IoU score of 0.80 on the Sentinel-1 data using U-Net with early fusion. The combined use of Sentinel-1 and Sentinel-2 failed to bring significant improvement compared to Sentinel-2 based results, but this dataset may have the potential to boost Sentinel-1 based results with Sentinel-2 data for near real-time wildfire progression mapping.

With the rapid increase of remote sensing images in temporal, spectral, and spatial resolutions, it is urgent to develop effective techniques for joint interpretation of spatial-temporal images. Multitype change detection (CD) is a significant research topic in multitemporal remote sensing image analysis, and its core is to effectively measure the difference degree and represent the difference among the multitemporal images. In this paper, we propose a novel difference representation learning (DRL) network and present an unsupervised learning framework for multitype CD task. Deep neural networks work well in representation learning but rely too much on labeled data, while clustering is a widely used classification technique free from supervision. However, the distribution of real remote sensing data is often not very friendly for clustering. To better highlight the changes and distinguish different types of changes, we combine difference measurement, DRL, and unsupervised clustering into a unified model, which can be driven to learn Gaussian-distributed and discriminative difference representations for nonchange and different types of changes. Furthermore, the proposed model is extended into an iterative framework to imitate the bottom-up aggregative clustering procedure, in which similar change types are gradually merged into the same classes. At the same time, the training samples are updated and reused to ensure that it converges to a stable solution. The experimental studies on four pairs of multispectral data sets demonstrate the effectiveness and superiority of the proposed model on multitype CD.

We propose an unsupervised deep convolutional coupling network for change detection based on two heterogeneous images acquired by optical sensors and radars on different dates. Most existing change detection methods are based on homogeneous images. Due to the complementary properties of optical and radar sensors, there is an increasing interest in change detection based on heterogeneous images. The proposed network is symmetric with each side consisting of one convolutional layer and several coupling layers. The two input images connected with the two sides of the network, respectively, are transformed into a feature space where their feature representations become more consistent. In this feature space, the different map is calculated, which then leads to the ultimate detection map by applying a thresholding algorithm. The network parameters are learned by optimizing a coupling function. The learning process is unsupervised, which is different from most existing change detection methods based on heterogeneous images. Experimental results on both homogenous and heterogeneous images demonstrate the promising performance of the proposed network compared with several existing approaches.

Change detection and analysis (CDA) is an important research topic in the joint interpretation of spatial-temporal remote sensing images. The core of CDA is to effectively represent the difference and measure the difference degree between bi-temporal images. In this paper, we propose a novel difference representation learning network (DRLnet) and an effective optimization framework without any supervision. Difference measurement, difference representation learning and unsupervised clustering are combined as a single model, i.e., DRLnet, which is driven to learn clustering-friendly and discriminative difference representations (DRs) for different types of changes. Further, DRLnet is extended into a recurrent learning framework to update and reuse limited training samples and prevent the semantic gaps caused by the saltation in the number of change types from over-clustering stage to the desired one. Experimental results identify the effectiveness of the proposed framework.

Change detection can be treated as a generative learning procedure, in which the connection between bitemporal images and the desired change map can be modeled as a generative one. In this letter, we propose an unsupervised change detection method based on generative adversarial networks (GANs), which has the ability of recovering the training data distribution from noise input. Here, the joint distribution of the two images to be detected is taken as input and an initial difference image (DI), generated by traditional change detection method such as change vector analysis, is used to provide prior knowledge for sampling the training data based on Bayesian theorem and GAN's min-max game theory. Through the continuous adversarial learning, the shared mapping function between the training data and their corresponding image patches can be built in GAN's generator, from which a better DI can be generated. Finally, an unsupervised clustering algorithm is used to analyze the better DI to obtain the desired binary change map. Theoretical analysis and experimental results demonstrate the effectiveness and robustness of the proposed method.

With the rapid technological development of various satellite sensors, high-resolution remotely sensed imagery has been an important source of data for change detection in land cover transition. However, it is still a challenging problem to effectively exploit the available spectral information to highlight changes. In this paper, we present a novel change detection framework for high-resolution remote sensing images, which incorporates superpixel-based change feature extraction and hierarchical difference representation learning by neural networks. First, highly homogenous and compact image superpixels are generated using superpixel segmentation, which makes these image blocks adhere well to image boundaries. Second, the change features are extracted to represent the difference information using spectrum, texture, and spatial features between the corresponding superpixels. Third, motivated by the fact that deep neural network has the ability to learn from data sets that have few labeled data, we use it to learn the semantic difference between the changed and unchanged pixels. The labeled data can be selected from the bitemporal multispectral images via a preclassification map generated in advance. And then, a neural network is built to learn the difference and classify the uncertain samples into changed or unchanged ones. Finally, a robust and high-contrast change detection result can be obtained from the network. The experimental results on the real data sets demonstrate its effectiveness, feasibility, and superiority of the proposed technique.

In this paper, we propose a novel approach for detecting multiple changes from two multi-temporal images. Despite the development of the change vector analysis (CVA) framework and its improved version the compressed CVA (C2VA) framework, it is found that they are limited when tackling the multi-change detection task for the images with one channel. Also, the intensity itself is fragile due to the existing noise, which especially influences the detection of subtle changes. Therefore, the stacked denosing autoencoder (SDAE) which serves as a fine tool for feature extraction is employed to generate a multi-dimensional feature representations. In this way, the C2VA framework can be applied to the inner robust features so that a satisfactory performance can be guaranteed. Experimental results from two datasets show its high accuracy and moderate time complexity, which demonstrates the effectiveness of the proposed SDAE-C2VA approach.

Due to the noise interference and redundancy in multispectral images, it is promising to transform the available spectral channels into a suitable feature space for relieving noise and reducing the redundancy. The booming of deep learning provides a flexible tool to learn abstract and invariant features directly from the data in their raw forms. In this letter, we propose an unsupervised change detection technique for multispectral images, in which we combine deep belief networks (DBNs) and feature change analysis to highlight changes. First, a DBN is established to capture the key information for discrimination and suppress the irrelevant variations. Second, we map bitemporal change feature into a 2-D polar domain to characterize the change information. Finally, an unsupervised clustering algorithm is adopted to distinguish the changed and unchanged pixels, and then, the changed types can be identified by classifying the changed pixels into several classes according to the directions of feature changes. The experimental results demonstrate the effectiveness and robustness of the proposed method.

With the increase of multisource data available from remote sensing platforms, it is demanding to develop unsupervised techniques for change detection from multisource data. The difference in imaging mechanism makes it difficult to carry out a direct comparison between multisource data in original observation spaces. Different sensors provide different descriptions on the same truth in low-dimension observation spaces, but the same truth indicates the comparability of multisource data in some high-dimensional feature spaces. Inspired by this, we try to solve this problem by transforming multisource data into a common high-dimension feature space. In this paper, an iterative coupled dictionary learning (CDL) model is proposed for multisource image change detection. This model aims to establish a pair of coupled dictionaries, one of which is responsible for the data from one sensor, whereas the other is responsible for the data from another sensor. The atoms from these two coupled dictionaries have a one-to-one correspondence at the same location. Such a property guarantees the transferability of the reconstruction coefficients between bitemporal patch pairs and provides us a desired mechanism to bridge multisource data and highlight changes. The contributions can be summarized as follows: CDL is designed to explore the intrinsic difference of multisource data for change detection in a high-dimension feature space, and an iterative scheme for unsupervised sample selection is proposed to keep the purity of training samples and gradually optimize the current coupled dictionaries. The experimental results have demonstrated the feasibility, effectiveness, and robustness of the proposed framework.