As a variant of Convolutional Neural Networks (CNNs) in Deep Learning, the Fully Convolutional Network (FCN) model achieved state-of-the-art performance for natural image semantic segmentation. In this paper, an accurate classification approach for high resolution remote sensing imagery based on the improved FCN model is proposed. Firstly, we improve the density of output class maps by introducing Atrous convolution, and secondly, we design a multi-scale network architecture by adding a skip-layer structure to make it capable for multi-resolution image classification. Finally, we further refine the output class map using Conditional Random Fields (CRFs) post-processing. Our classification model is trained on 70 GF-2 true color images, and tested on the other 4 GF-2 images and 3 IKONOS true color images. We also employ object-oriented classification, patch-based CNN classification, and the FCN-8s approach on the same images for comparison. The experiments show that compared with the existing approaches, our approach has an obvious improvement in accuracy. The average precision, recall, and Kappa coefficient of our approach are 0.81, 0.78, and 0.83, respectively. The experiments also prove that our approach has strong applicability for multi-resolution image classification.

An approach based on the improved quadtree structure and region adjacency graph for the segmentation of a high-resolution remote sensing image is proposed in this paper. In order to obtain the initial segmentation results of the image, the image is first iteratively split into quarter sections and the quadtree structure is constructed. In this process, an improved fast calculation method for standard deviation of image is proposed, which significantly increases the speed of quadtree segmentation with standard deviation criterion. A spatial indexing structure was built using improved Morton encoding based on this structure, which provides the merging process with data structure for neighborhood queries. Then, in order to obtain the final segmentation result, we constructed a feature vector using both spectral and texture factors, and proposed an algorithm for region merging based on the region adjacency graph technique. Finally, to validate the method, experiments were performed on GeoEye-1 and IKONOS color images, and the segmentation results were compared with two typical algorithms: multi-resolution segmentation and Mean-Shift segmentation. The experimental results showed that: (1) Compared with multi-resolution and Mean-Shift segmentation, our method increased efficiency by 3–5 times and 10 times, respectively; (2) Compared with the typical algorithms, the new method significantly improved the accuracy of segmentation.

With the acceleration of China's modernization, soil erosion caused by illegal activities in production and construction projects is becoming serious. Timely and effective monitoring and supervision is necessary. Traditional monitoring methods have many shortcomings, such as long period, limited precision, limited terrain. So it can't demonstrate the real situation of the project construction area timely and effectively. This paper attempts to apply remote sensing technique, combining the Convolution Neural Network(CNN) method, to achieve the purpose of intelligent monitoring of disturbed lands. By investigating the effect of the application of the convolutional neural network model with different training volumes, results showing that the accuracy and efficiency of the model are more and more high with the increase of training volumes. It is proved that the application of CNN in monitoring of land disturbance is effective.

The extraction of roads from high spatial resolution remote sensing images remains a problem though lots of efforts have been made in this area. High spatial resolution remote sensing images represent the surface of the earth in detail. As spatial resolution increases, spectral variability within the road cover units becomes complex and traditional remote sensing image processing methods on pixel basis are no longer suitable. This paper studies automatic road extraction from remote sensing images based on methods of Pulse-Coupled Neural Network and mathematical morphology. PCNN is a useful biologically inspired algorithm, and has the properties of linking field and dynamic threshold which make similar neurons generate pulses simultaneously. PCNN has the ability of a neuron to capture neighboring neurons which are in similar states and the independency of the pulses within unattached neuron regions. The method of mathematical morphology has the prime principle which is using a certain structure element to measure and extract the corresponding form in an image. In this paper, the simplified PCNN is applied as the image segmentation algorithm, and morphological transformation is used to purify the roads' information and to extract the road centerlines. Experimental results show that this method is efficient in road extraction from remote sensing images.