Robust unsupervised small area change detection from SAR imagery using deep learning

Small area change detection using synthetic aperture radar (SAR) imagery is a highly challenging task, due to speckle noise and imbalance between classes (changed and unchanged). In this paper, a robust unsupervised approach is proposed for small area change detection using deep learning techniques. First, a multi-scale superpixel reconstruction method is developed to generate a difference image (DI), which can suppress the speckle noise effectively and enhance edges by exploiting local, spatially homogeneous information. Second, a two-stage centre-constrained fuzzy c-means clustering algorithm is proposed to divide the pixels of the DI into changed, unchanged and intermediate classes with a parallel clustering strategy. Image patches belonging to the first two classes are then constructed as pseudo-label training samples, and image patches of the intermediate class are treated as testing samples. Finally, a convolutional wavelet neural network (CWNN) is designed and trained to classify testing samples into changed or unchanged classes, coupled with a deep convolutional generative adversarial network (DCGAN) to increase the number of changed class within the pseudo-label training samples. Numerical experiments on four real SAR datasets demonstrate the validity and robustness of the proposed approach, achieving up to 99.61% accuracy for small area change detection

Change Detection in Multi-temporal Images Using Multistage Clustering for Disaster Recovery Planning

Change detection analysis on multi-temporal images using various methods have been developed by many researchers in the field of spatial data analysis and image processing. Change detection analysis has many benefit for real world applications such as medical image analysis, valuable material detector, satellite image analysis, disaster recovery planning, and many others. Indonesia is one of the most country that encounter natural disaster. The most memorable disaster was happened in December 26, 2004. Change detection is one of the important part management planning for natural disaster recovery. This article present the fast and accurate result of change detection on multi-temporal images using multistage clustering. There are three main step for change detection in this article, the first step is to find the image difference of two multi-temporal images between the time before disaster and after disaster using operation log ratio between those images. The second step is clustering the difference image using Fuzzy C means divided into three classes. Change, unchanged, and intermediate change region. Afterword the last step is cluster the change map from fuzzy C means clustering using k means clustering, divided into two classes. Change and unchanged region. Both clustering\u27s based on Euclidian distance

CHANGE DETECTION IN MULTI-TEMPORAL IMAGES USING MULTISTAGE CLUSTERING FOR DISASTER RECOVERY PLANNING

Change detection analysis on multi-temporal images using various methods have been developed by many researchers in the field of spatial data analysis and image processing. Change detection analysis has many benefit for real world applications such as medical image analysis, valuable material detector, satellite image analysis, disaster recovery planning, and many others. Indonesia is one of the most country that encounter natural disaster. The most memorable disaster was happened in December 26, 2004. Change detection is one of the important part management planning for natural disaster recovery. This article present the fast and accurate result of change detection on multi-temporal images using multistage clustering. There are three main step for change detection in this article, the first step is to find the image difference of two multi-temporal images between the time before disaster and after disaster using operation log ratio between those images. The second step is clustering the difference image using Fuzzy C means divided into three classes. Change, unchanged, and intermediate change region. Afterword the last step is cluster the change map from fuzzy C means clustering using k means clustering, divided into two classes. Change and unchanged region. Both clustering’s based on Euclidian distance

Classification of Synthetic Aperture Radar Images using Particle Swarm Optimization Technique

In this thesis, SAR image classification problem is considered as an optimization problem various clustering techniques are addressed in literature for SAR image classification. This thesis focuses on an evolutionary based stochastic optimization technique that is Particle Swarm Optimization (PSO) technique for classification of SAR images. This technique composes of three main processes: firstly, selecting training samples for every region in the SAR image. Secondly, training these samples using PSO, and obtain cluster center of every region. Finally, the classification of SAR image with respect to cluster center is obtained. To show the effectiveness of this approach, classified SAR images are obtained and compared with other clustering techniques such as K-means algorithm and Fuzzy C-means algorithm (FCM). The performance of PSO is found to be superior than other techniques in terms of classification accuracy and computational complexity. The result is validated with various SAR images

Decomposition and unsupervised segmentation of dual-polarized polarimetric SAR data using fuzzy entropy and coherency clustering method

En aquesta tesi, el teorema de descomposició H-α i l'algorisme de segmentació fuzzy c-means s'apliquen a un conjunt de dades polarimètriques amb polarització dual d'un sistema SAR (Synthetic Aperture Radar) usant MATLAB i s'avalua la precisió de la segmentació. La segmentació es realitza amb l'objectiu de separar els diferents elements del paisatge emprant les característiques pròpies de cada mecanisme de dispersió. Com es veurà, els paràmetres d'entropia i d'alpha resulten molt valuosos per a diferenciar els diversos tipus d'objectius i l'algorisme fuzzy c-means proposat aplicat a l'entropia i a la matriu de coherència obté resultats robustos en el procés de segmentació. Ambdós algorismes s'apliquen sobre un conjunt de dades de Pangkalan Bun, Indonèsia, proporcionat pel satèl·lit radar TerraSAR-X.En esta tesis, el teorema de descomposición H-α y el algoritmo de segmentación fuzzy c-means se aplican a un conjunto de datos polarimétricos con polarización dual de un sistema SAR (Synthetic Aperture Radar) usando MATLAB y se evalúa la precisión de la segmentación. La segmentación separa los diferentes elementos del paisaje usando las características propias de cada mecanismo de dispersión. Como se verá, los parámetros de entropía y de alpha resultan muy valiosos para diferenciar los tipos de objetivos y el algoritmo fuzzy c-means propuesto aplicado a la entropía y a la matriz de coherencia proporciona resultados robustos en la segmentación. Ambos algoritmos se aplican sobre un conjunto de datos de Pangkalan Bun, Indonesia, proporcionado por el satélite TerraSAR-X.In this thesis, H-α decomposition theorem and fuzzy c-means segmentation algorithm are applied to dual-polarized polarimetric SAR (Synthetic Aperture Radar) data using MATLAB and the accuracy of segmentation is evaluated. The segmentation is done with the purpose of separating the different elements of the landscape using the characteristics of the scattering mechanisms. As it will be shown, entropy and alpha decomposition parameters are a valuable key to differentiate between diverse types of targets and the proposed fuzzy c-means algorithm applied to the entropy and coherency matrix provides robust results in the segmentation process. Both algorithms are applied on a dual-polarized SAR dataset of Pangkalan Bun, Indonesia, provided by TerraSAR-X radar Earth observation satellite

Evaluation of the change in synthetic aperture radar imaging using transfer learning and residual network

Change detection from synthetic aperture radar images becomes a key technique to detect change area related to some phenomenon as flood and deformation of the earth surface. This paper proposes a transfer learning and Residual Network with 18 layers (ResNet-18) architecture-based method for change detection from two synthetic aperture radar images. Before the application of the proposed technique, batch denoising using convolutional neural network is applied to the two input synthetic aperture radar image for speckle noise reduction. To validate the performance of the proposed method, three known synthetic aperture radar datasets (Ottawa; Mexican and for Taiwan Shimen datasets) are exploited in this paper. The use of these datasets is important because the ground truth is known, and this can be considered as the use of numerical simulation. The detected change image obtained by the proposed method is compared using two image metrics. The first metric is image quality index that measures the similarity ratio between the obtained image and the image of the ground truth, the second metrics is edge preservation index, it measures the performance of the method to preserve edges. Finally, the method is applied to determine the changed area using two Sentinel 1 B synthetic aperture radar images of Eddahbi dam situated in Morocco

Change Detection Techniques with Synthetic Aperture Radar Images: Experiments with Random Forests and Sentinel-1 Observations

This work aims to clarify the potential of incoherent and coherent change detection (CD) approaches for detecting and monitoring ground surface changes using sequences of synthetic aperture radar (SAR) images. Nowadays, the growing availability of remotely sensed data collected by the twin Sentinel-1A/B sensors of the European (EU) Copernicus constellation allows fast mapping of damage after a disastrous event using radar data. In this research, we address the role of SAR (amplitude) backscattered signal variations for CD analyses when a natural (e.g., a fire, a flash flood, etc.) or a human-induced (disastrous) event occurs. Then, we consider the additional pieces of information that can be recovered by comparing interferometric coherence maps related to couples of SAR images collected between a principal disastrous event date. This work is mainly concerned with investigating the capability of different coherent/incoherent change detection indices (CDIs) and their mutual interactions for the rapid mapping of "changed" areas. In this context, artificial intelligence (AI) algorithms have been demonstrated to be beneficial for handling the different information coming from coherent/incoherent CDIs in a unique corpus. Specifically, we used CDIs that synthetically describe ground surface changes associated with a disaster event (i.e., the pre-, cross-, and post-disaster phases), based on the generation of sigma nought and InSAR coherence maps. Then, we trained a random forest (RF) to produce CD maps and study the impact on the final binary decision (changed/unchanged) of the different layers representing the available synthetic CDIs. The proposed strategy was effective for quickly assessing damage using SAR data and can be applied in several contexts. Experiments were conducted to monitor wildfire's effects in the 2021 summer season in Italy, considering two case studies in Sardinia and Sicily. Another experiment was also carried out on the coastal city of Houston, Texas, the US, which was affected by a large flood in 2017; thus, demonstrating the validity of the proposed integrated method for fast mapping of flooded zones using SAR data