A new method for remotely sensed change detection based on artificial neural networks is presented. The algorithm for an automated land-cover change-detection system was developed and implemented based on the current neural network techniques for multispectral image classification. The suitability of application of neural networks in change detection and its related network design considerations unique to change detection were first investigated. A neural-network-based change-detection system using the backpropagation training algorithm was then developed. The trained four-layered neural network was able to provide complete categorical information about the nature of changes and detect land-cover changes with an overall accuracy of 95.6 percent for a four-class (i.e., 16 change classes) classification scheme. Using the same training data, a maximum-likelihood supervised classification produced an accuracy of 86.5 percent. The experimental results using multitemporal Landsat Thematic Mapper imagery of Wilmington, North Carolina are provided. Findings of this study demonstrated the potential and advantages of using neural network in multitemporal change analysis.

Remotely Sensed Change Detection Based on Artificial Neural Networks

Effective disturbance indices for Hyrcanian forests in Kheyroud, Nowshahr, Iran were determined. The study area was divided into landscape mosaics based on ecosystem parameters including profile type, slope and elevation. Co-occurrence texture indices were derived as forest disturbance factors on the first five bands of Landsat TM, ETM+ and OLI images for the prevailing wood harvest disturbance regimes. These indices were screened using ten types of trend analyses and used for modeling disturbance of the harvesting regime through artificial neural networks. The results show that the selected indices can be useful in distinguishing areas with different disturbance intensities and as such, used in the context of health assessment through the health distance method. The accuracy of the health maps derived from the indices [increasing disturbance] led to give rise higher disturbance classification accuracy.

Image texture indices and trend analysis for forest disturbance assessment under wood harvest regimes

The use of a neural network for determining the change of landcover/land-use with remotely sensed data is proposed. In this study, a single image contains both spectral and temporal information is created from a multidate satellite imagery. The proposed change detection method can be divided into two main steps: training data selection and change detection. At the training step, the training set, basically consists of the classes of no-change and possible change data, is obtained from the composited image. Then the training data is used to input the neural network and obtain the network's weights. At the change detection step, the network's weights is employed to detect the change and no-change classes in the combined image. The proposed method is tested using a multidate SPOT imageries and a satisfied change pattern detection is obtained.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

C. F. Chen

Papers

Neural network for change detection of remotely sensed imagery