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CNN-Enhanced Multi-Indices Patch-Based Classification: A Case Study of Guwahati City

Arindom Ain1, Minakshi Gogoi2, Dibyajyoti Chutia3 1, 2Dept. of Computer Science & Engg., GIMT, Guwahati Guwahati-781017, Assam, India 3Scientist/Engineer SF North Eastern Space Applications Center, Department of Space Umium, Shillong, India

Abstract: Land use and land cover (LULC) provides a way to classify objects on the surface of Earth. This paper aims to identify the varying land cover classes by stacking of 6 spectral bands and 10 different generated indices from those bands together. We have considered the multispectral images of Landsat 7 for our research. It is seen that instead of using only basic spectral bands (blue, green, red, nir, swir1 and swir2) for classification, stacking relevant indices of multiple target classes like ndvi, evi, nbr, BU, etc. with basic bands generates more precise results. In this study, we have used automated clustering techniques fo r generating 5 diffe rent class labels fo r training the model. These labels are furthe r used to develop a predictive model to classify LULC classes. The proposed classifier is compared with the SVM and KNN classifiers. The results show that this proposed strategy gives preferable outcomes over other techniques. After training the model over 50 epochs, an accuracy of 93.29% is achieved. Keywords: Land use, land cover, CNN, ISODATA, indices

I. INTRODUCTION

Conserving Natural resources are an important element for environmental aspects. Different activities have been carried out by humans using various naturally existing resources in the environment. So monitoring and proper utilization of resources are necessary to maintain sustainable growth in the environment [1]. One of the effective methods suggested by the literature is by using areal images [2]. The changes that occur in the earth’s surface are caused due to evolution and are recognized as LULC changes. Areal images collected from the satellite could be used for LULC map generation [1], [2]. The LULC maps have a great impact in monitoring, management, and planning programmes at both local and national levels [3], [4], [5]. With the evolution of remote sensing technology, the generation of LULC maps over time became very efficient. LULC maps are efficient in terms of monitoring and planning activities, ecosystem management, climate change, and government policies [3]. In this paper, we intend to do a case study of the GM region, a prominent city of the North Eastern Region (NER) about the urban growth, agriculture, watershed, and bare land using multispectral satellite images. Many development activities are going on at Guwahati city [6]. An extension is planned under a Master Plan-2025 concerning smart city development. The city is listed in the "smart cities" category among t h e top 20 cities [7, 8]. With development, the prospect of urbanization and urban growth increases with time. So, we feel it is important to do proper assessment and planning for better development of the region. With this objective, it is proposed to develop an efficient deep learning model for the detection and classification of LULC classes and generate a LULC classification map for the GM region. LULC is primarily classified into four classes based on different signature viz, land, water, vegetation, and built-ups. North-East India is rapidly facing a transition from one class to another over time due to the influence of many factors on the base class. Factors can be classified into two major types viz. direct influencing factor (DIF) and indirect influencing factor (IIF). DIF is categorized as those factors which directly influence the transitions of classes and IIF are those which indirectly affect the transition. We have selected some DIF and IIF to understand and listed them in Table I. Due to these factors, NER is facing many challenges like improper field survey, smart and proper planning of roads, water conservation, dams, and other construction, access to unreachable areas, land cover changes monitoring, land use detection, flood problems, etc. These challenges result in some direct or indirect issues like increasing pollution, destruction of natural habitat, illegal settlements, eco-system disruption, and state economy slowdown by proper monitoring and analysis of the LULC map over time. Many works of literature [9, 10, 11, 12] have found remote sensing and GIS techniques as an adequate method of analyzing land use and land cover detection and classification. Guwahati Metropolitan (GM) is one of the renowned cities situated in the state of Assam of NER. It has undergone immense expansion due to rapid urbanization and other human needs [6]. For the last decades, the city has been known to have a relatively sparse population.